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Journal articles on the topic 'Cloud Workload prediction'
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1
Mao, Li, Deyu Qi, Weiwei Lin, and Chaoyue Zhu. "A Self-Adaptive Prediction Algorithm for Cloud Workloads." International Journal of Grid and High Performance Computing 7, no. 2 (2015): 65–76. http://dx.doi.org/10.4018/ijghpc.2015040105.
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It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.
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Simhadri Mallikarjuna Rao, Gangadhara Rao Kancherla, and Neelima Guntupalli. "A Hybrid Machine Learning Approach to Cloud Workload Prediction Using Decision Tree for Classification and Random Forest for Regression." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2240–52. https://doi.org/10.32628/cseit2410488.
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The dynamic nature of cloud workloads necessitates accurate predictions to optimize resource utilization, enhance performance, and ensure quality of service (QoS). Consequently, numerous researchers have developed workload prediction models to improve cloud design and deployment. These models enable timely and reliable workload forecasting, facilitating critical decisions such as resource allocation and network bandwidth management. This study proposes a hybrid learning model, termed DTCRFR, which integrates Decision Tree Classification and Random Forest Regression techniques to predict reliable workloads. The DTCRFR model operates by initially assigning a workload state to each input data point based on historical workload data and system metrics. Subsequently, the regression model refines this prediction, producing a highly accurate workload value for the classified state. The combined approach enhances prediction accuracy while reducing computational complexity, making it highly suitable for real-time applications. Empirical results validate the effectiveness of this hybrid model, demonstrating improved prediction accuracy and reduced mean-squared error (MSE) and mean absolute error (MAE). This highlights the benefit of combining classification and regression techniques to leverage their complementary strengths for more reliable and granular workload predictions. The proposed method significantly enhances resource management and system performance in diverse computational environments. By merging classification and regression, DTCRFR adds precision and subtlety to workload forecasting, providing a notable advancement in the field. This hybrid model improves both efficiency and reliability in workload prediction, marking a relevant contribution to cloud resource optimization.
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Krishnan, Smitha, and B. G. Prasanthi. "SGA Model for Prediction in Cloud Environment." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 370–80. http://dx.doi.org/10.17762/ijritcc.v11i5s.7046.
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With virtual information, cloud computing has made applications available to users everywhere. Efficient asset workload forecasting could help the cloud achieve maximum resource utilisation. The effective utilization of resources and the reduction of datacentres power both depend heavily on load forecasting. The allocation of resources and task scheduling issues in clouds and virtualized systems are significantly impacted by CPU utilisation forecast. A resource manager uses utilisation projection to distribute workload between physical nodes, improving resource consumption effectiveness. When performing a virtual machine distribution job, a good estimation of CPU utilization enables the migration of one or more virtual servers, preventing the overflow of the real machineries. In a cloud system, scalability and flexibility are crucial characteristics. Predicting workload and demands would aid in optimal resource utilisation in a cloud setting. To improve allocation of resources and the effectiveness of the cloud service, workload assessment and future workload forecasting could be performed. The creation of an appropriate statistical method has begun. In this study, a simulation approach and a genetic algorithm were used to forecast workloads. In comparison to the earlier techniques, it is anticipated to produce results that are superior by having a lower error rate and higher forecasting reliability. The suggested method is examined utilizing statistics from the Bit brains datacentres. The study then analyses, summarises, and suggests future study paths in cloud environments.
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Arbat, Shivani, Vinodh Kumaran Jayakumar, Jaewoo Lee, Wei Wang, and In Kee Kim. "Wasserstein Adversarial Transformer for Cloud Workload Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12433–39. http://dx.doi.org/10.1609/aaai.v36i11.21509.
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Predictive VM (Virtual Machine) auto-scaling is a promising technique to optimize cloud applications’ operating costs and performance. Understanding the job arrival rate is crucial for accurately predicting future changes in cloud workloads and proactively provisioning and de-provisioning VMs for hosting the applications. However, developing a model that accurately predicts cloud workload changes is extremely challenging due to the dynamic nature of cloud workloads. Long- Short-Term-Memory (LSTM) models have been developed for cloud workload prediction. Unfortunately, the state-of-the-art LSTM model leverages recurrences to predict, which naturally adds complexity and increases the inference overhead as input sequences grow longer. To develop a cloud workload prediction model with high accuracy and low inference overhead, this work presents a novel time-series forecasting model called WGAN-gp Transformer, inspired by the Transformer network and improved Wasserstein-GANs. The proposed method adopts a Transformer network as a generator and a multi-layer perceptron as a critic. The extensive evaluations with real-world workload traces show WGAN- gp Transformer achieves 5× faster inference time with up to 5.1% higher prediction accuracy against the state-of-the-art. We also apply WGAN-gp Transformer to auto-scaling mechanisms on Google cloud platforms, and the WGAN-gp Transformer-based auto-scaling mechanism outperforms the LSTM-based mechanism by significantly reducing VM over-provisioning and under-provisioning rates.
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T. Singh, Sanjay, and Mahendra Tiwari. "A STACKED GENERALIZATION BASED META-CLASSIFIER FOR PREDICTION OF CLOUD WORKLOAD." ICTACT Journal on Soft Computing 14, no. 4 (2024): 3340–46. http://dx.doi.org/10.21917/ijsc.2024.0469.
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Cloud computing has revolutionized the way software, platforms, and infrastructure can be acquired by making them available as on-demand services that can be accessed from anywhere via a web browser. Due to its ubiquitous nature Cloud data centers continuously experience fluctuating workloads which demands for dynamic resource provisioning. These workloads are either placed on Virtual Machines (VMs) or containers which abstract the underlying physical resources deployed at the data center. A proactive or reactive method can be used to allot required resources to the workload. Reactive approaches tend to be inefficient as it takes a significant amount of time to configure the resources to meet the change in demands. A proactive approach for resource management is better in meeting workload demands as it makes an appropriate number of resources available in advance to cater to the fluctuations in workload. The success of such an approach relies on the ability of the resource management module of a data center to accurately predict future workloads. Machine Learning (ML) has already proven itself to be very effective in performing prediction in various domains. In this work, we propose an ML meta-classifier based on stacked generalization for predicting future workloads utilising the past workload trends which are recorded as event logs at Cloud data centers. The proposed model showed a prediction accuracy of 98.5% indicating its applicability for the Cloud environment where SLA requirements must be closely adhered to.
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Kumar, K. Dinesh, and E. Umamaheswari. "HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network." Cybernetics and Information Technologies 20, no. 4 (2020): 55–73. http://dx.doi.org/10.2478/cait-2020-0047.
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AbstractFor cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.
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Sharma, Kirtikumar J. "Ensemble-Based Cloud Workload Prediction Using Recent AI and ML Methods for Optimized Resource Management & Scheduling." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 923–28. https://doi.org/10.22214/ijraset.2025.67534.
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with the rising demand for efficient cloud computing and resource management, precise workload prediction has become vital. This paper explores altered methods used for workload predicting, from traditional methods to recent machine learning methods. We train models such as XGBoost, LightGBM, CatBoost, LSTM, and GRU, along with an ensemble method, to know their efficiency in practical cloud environments. The study uses the Alibaba Cluster 2017 dataset, focusing on batch (offline) workloads for well prediction precision. Numerous pre-processing methods, with outlier detection, normalization, and sequence creation, are applied to increase model performance. We associate the results of distinct models and ensemble methods using performance parameters like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results show that whereas deep learning models seizure sequential patterns, ensemble techniques deliver improved complete stability and correctness. This research shows the importance of merging multiple models to improve workload predicting and increase cloud resource consumption.
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Bharote, Dinesh Tulasidas, and Prof Pallavi Bagde. "A Review and Taxonomy on Data Driven Regression Models for Estimating Future Cloud Workloads." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51229.
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Cloud Computing has long become asought after fields in computer science. Several applications which need high computational complexity but cannot be performed on conventional hardware prefer to leverage cloud based platforms. Hence with increasing traffic and load on cloud servers or cloud based platforms, there seems to be a natural need for cloud workload prediction so as to estimate and manage cloud based resources. Since cloud data is large and complex at the same time, hence it is necessary to use artificial intelligence based techniques for the estimation of cloud workload so as to improve upon the accuracy of conventional techniques. This paper presents a review on the contemporary techniques for cloud workload prediction. The performance evaluation parameters have also been discussed.. It is expected that the paper would present with a headway for further research in cloud workload prediction. Keywords—Cloud Workload Prediction, Aftificial Intelligence, Machine Learning, Artificial Neural Network (ANN), Mean Absolute Percentage error, Mean Square Error.
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Lu, Yao, John Panneerselvam, Lu Liu, and Yan Wu. "RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/5635673.
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Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.
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Liu, Yanbing, Bo Gong, Congcong Xing, and Yi Jian. "A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/973069.
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Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM) migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM), the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.
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Jia, Kai, Jun Xiang, and Baoxia Li. "DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure." Electronics 13, no. 18 (2024): 3588. http://dx.doi.org/10.3390/electronics13183588.
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Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and job scheduling due to its ability to estimate upcoming workloads. However, due to users’ diverse usage demands, the changing characteristics of workloads have become more and more complex, including not only short-term irregular fluctuation characteristics but also long-term dynamic variations. This prevents existing workload-prediction methods from fully capturing the above characteristics, leading to degradation of prediction accuracy. To deal with the above problems, this paper proposes a framework based on a dual-channel temporal convolutional network and transformer (referred to as DuCFF) to perform workload prediction. Firstly, DuCFF introduces data preprocessing technology to decouple different components implied by workload data and combine the original workload to form new model inputs. Then, in a parallel manner, DuCFF adopts the temporal convolution network (TCN) channel to capture local irregular fluctuations in workload time series and the transformer channel to capture long-term dynamic variations. Finally, the features extracted from the above two channels are further fused, and workload prediction is achieved. The performance of the proposed DuCFF’s was verified on various workload benchmark datasets (i.e., ClarkNet and Google) and compared to its nine competitors. Experimental results show that the proposed DuCFF can achieve average performance improvements of 65.2%, 70%, 64.37%, and 15%, respectively, in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared (R2) compared to the baseline model CNN-LSTM.
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Miguel, Carlos, Víctor Rampérez, Javier Soriano, and Shadi Aljawarneh. "Towards SLA-Driven Autoscaling of Cloud Distributed Services for Mobile Communications." Mobile Information Systems 2022 (October 3, 2022): 1–13. http://dx.doi.org/10.1155/2022/3725657.
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In recent years cloud computing has established itself as the computing paradigm that supports most distributed systems, which are essential in mobile communications, such as publish-subscribe (pub/sub) systems or complex event processing (CEP). The cornerstone of cloud computing is elasticity, and today’s autoscaling systems leverage that property by making scaling decisions based on estimates of future workload to satisfy service level agreements (SLAs). However, these autoscaling systems are not generic enough, as the workload definition is application-based. On the other hand, the workload prediction needs to be mapped in terms of SLA parameters, which introduces a double prediction problem. This work presents an empirical study on the relationship between different types of workloads in the literature and their relationship in terms of SLA parameters in the context of mobile communications. In addition, more than 30 prediction models have been trained using different techniques (time series analysis, regression, random forests) to test which ones offer better prediction results of the SLA parameters based on the type of workload and the prediction horizon. Finally, a series of conclusions on the predictive models to be used as a first step towards an autonomous decision system are presented.
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Borna, Keivan, and Reza Ghanbari. "A self-adaptive deep learning-based model to predict cloud workload." Neural Network World 33, no. 3 (2023): 161–69. http://dx.doi.org/10.14311/nnw.2023.33.010.
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Predicting cloud workload is a problematic issue for cloud providers. Recent research has led us to a significant improvement in workload prediction. Although self-adaptive systems have an imperative impact on lowering the number of cloud resources, those still have to be more accurate, detailed and accelerated. A new self-adaptive technique based on a deep learning model to optimize and decrease the use of cloud resources is proposed. It is also demonstrated how to prognosticate incoming workload and how to manage available resources. The PlanetLab dataset in this research is used. The obtained results have been compared to other relevant designs. According to these comparisons with the state-of-theart deep learning methods, our proposed model encompasses a better prediction efficiency and enhances productivity by 5%.
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Liu, Chunhong, Jie Jiao, Weili Li, Jingxiong Wang, and Junna Zhang. "Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction." Entropy 24, no. 12 (2022): 1770. http://dx.doi.org/10.3390/e24121770.
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Accurate workload prediction plays a key role in intelligent scheduling decisions on cloud platforms. There are massive amounts of short-workload sequences in the cloud platform, and the small amount of data and the presence of outliers make accurate workload sequence prediction a challenge. For the above issues, this paper proposes an ensemble learning method based on sample weight transfer and long short-term memory (LSTM), termed as Tr-Predictor. Specifically, a selection method of similar sequences combining time warp edit distance (TWED) and transfer entropy (TE) is proposed to select a source domain dataset with higher similarity for the target workload sequence. Then, we upgrade the basic learner of the ensemble model two-stage TrAdaBoost.R2 to LSTM in the deep model and enhance the ability of the ensemble model to extract sequence features. To optimize the weight adjustment strategy, we adopt a two-stage weight adjustment strategy and select the best weight for the learner according to the sample error and model error. Finally, the above process determines the parameters of the target model and uses the target model to predict the short-task sequences. In the experimental validation, we arbitrarily select nine sets of short-workload data from the Google dataset and three sets of short-workload data from the Alibaba cluster to verify the prediction effectiveness of the proposed algorithm. The experimental results show that compared with the commonly used cloud workload prediction methods Tr-Predictor has higher prediction accuracy on the small-sample workload. The prediction indicators of the ablation experiments show the performance gain of each part in the proposed method.
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Zou, Ding, Wei Lu, Zhibo Zhu, et al. "OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4090–103. http://dx.doi.org/10.14778/3685800.3685829.
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Autoscaling is a critical mechanism in cloud computing, enabling the autonomous adjustment of computing resources in response to dynamic workloads. This is particularly valuable for co-located, long-running applications with diverse workload patterns. The primary objective of autoscaling is to regulate resource utilization at a desired level, effectively balancing the need for resource optimization with the fulfillment of Service Level Objectives (SLOs). Many existing proactive autoscaling frameworks may encounter prediction deviations arising from the frequent fluctuations of cloud workloads. Reactive frameworks, on the other hand, rely on realtime system feedback, but their hysteretic nature could lead to violations of stringent SLOs. Hybrid frameworks, while prevalent, often feature independently functioning proactive and reactive modules, potentially leading to incompatibility and undermining the overall decision-making efficacy. In addressing these challenges, we propose OptScaler, a collaborative autoscaling framework that integrates proactive and reactive modules through an optimization module. The proactive module delivers reliable future workload predictions to the optimization module, while the reactive module offers a self-tuning estimator for real-time updates. By embedding a Model Predictive Control (MPC) mechanism and chance constraints into the optimization module, we further enhance its robustness. Numerical results have demonstrated the superiority of our workload prediction model and the collaborative framework, leading to over a 36% reduction in SLO violations compared to prevalent reactive, proactive, or hybrid autoscalers. Notably, OptScaler has been successfully deployed at Alipay, providing autoscaling support for the world-leading payment platform.
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Li, Lei, Yilin Wang, Lianwen Jin, Xin Zhang, and Huiping Qin. "Two-Stage Adaptive Classification Cloud Workload Prediction Based on Neural Networks." International Journal of Grid and High Performance Computing 11, no. 2 (2019): 1–23. http://dx.doi.org/10.4018/ijghpc.2019040101.
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Workload prediction is important for automatic scaling of resource management, and a high accuracy of workload prediction can reduce the cost and improve the resource utilization in the cloud. But, the task request is usually random mutation, so it is difficult to achieve more accurate prediction result for single models. Thus, to improve the prediction result, the authors proposed a novel two-stage workload prediction model based on artificial neural networks (ANNs), which is composed of one classification model and two prediction models. On the basis of the first-order gradient feature, the model can categorize the workload into two classes adaptively. Then, it can predict the workload by using the corresponding prediction neural network models according to the classification results. The experiment results demonstrate that the suggested model can achieve more accurate workload prediction compared with other models.
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Dang-Quang, Nhat-Minh, and Myungsik Yoo. "An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing." Applied Sciences 12, no. 7 (2022): 3523. http://dx.doi.org/10.3390/app12073523.
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With the rapid development of 5G technology, the need for a flexible and scalable real-time system for data processing has become increasingly important. By predicting future resource workloads, cloud service providers can automatically provision and deprovision user resources for the system beforehand, to meet service level agreements. However, workload demands fluctuate continuously over time, which makes their prediction difficult. Hence, several studies have proposed a technique called time series forecasting to accurately predict the resource workload. However, most of these studies focused solely on univariate time series forecasting; in other words, they only analyzed the measurement of a single feature. This study proposes an efficient multivariate autoscaling framework using bidirectional long short-term memory (Bi-LSTM) for cloud computing. The system framework was designed based on the monitor–analyze–plan–execute loop. The results obtained from our experiments on different actual workload datasets indicated that the proposed multivariate Bi-LSTM exhibited a root-mean-squared error (RMSE) prediction error 1.84-times smaller than that of the univariate one. Furthermore, it reduced the RMSE prediction error by 6.7% and 5.4% when compared with the multivariate LSTM and convolutional neural network-long short-term memory (CNN-LSTM) models, respectively. Finally, in terms of resource provisioning, the multivariate Bi-LSTM autoscaler was 47.2% and 14.7% more efficient than the multivariate LSTM and CNN-LSTM autoscalers, respectively.
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Abdullayeva, Fargana J. "Cloud Computing Virtual Machine Workload Prediction Method Based on Variational Autoencoder." International Journal of Systems and Software Security and Protection 12, no. 2 (2021): 33–45. http://dx.doi.org/10.4018/ijsssp.2021070103.
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The paper proposes a method for predicting the workload of virtual machines in the cloud infrastructure. Reconstruction probabilities of variational autoencoders were used to provide the prediction. Reconstruction probability is a probability criterion that considers the variability in the distribution of variables. In the proposed approach, the values of the reconstruction probabilities of the variational autoencoder show the workload level of the virtual machines. The results of the experiments showed that variational autoencoders gave better results in predicting the workload of virtual machines compared to simple deep neural networks. The generative characteristics of the variational autoencoders determine the workload level by the data reconstruction.
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Hanif, Muhammad, Choonhwa Lee, and Sumi Helal. "Predictive topology refinements in distributed stream processing system." PLOS ONE 15, no. 11 (2020): e0240424. http://dx.doi.org/10.1371/journal.pone.0240424.
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Cloud computing has evolved the big data technologies to a consolidated paradigm with SPaaS (Streaming processing-as-a-service). With a number of enterprises offering cloud-based solutions to end-users and other small enterprises, there has been a boom in the volume of data, creating interest of both industry and academia in big data analytics, streaming applications, and social networking applications. With the companies shifting to cloud-based solutions as a service paradigm, the competition grows in the market. Good quality of service (QoS) is a must for the enterprises, as they strive to survive in a competitive environment. However, achieving reasonable QoS goals to meet SLA agreement cost-effectively is challenging due to variation in workload over time. This problem can be solved if the system has the ability to predict the workload for the near future. In this paper, we present a novel topology-refining scheme based on a workload prediction mechanism. Predictions are made through a model based on a combination of SVR, autoregressive, and moving average model with a feedback mechanism. Our streaming system is designed to increase the overall performance by making the topology refining robust to the incoming workload on the fly, while still being able to achieve QoS goals of SLA constraints. Apache Flink distributed processing engine is used as a testbed in the paper. The result shows that the prediction scheme works well for both workloads, i.e., synthetic as well as real traces of data.
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Narek, Narek, and Sandy Montajab Hazzouri. "An Effective Workload Prediction with Rnn-Lstm For Efficient Resource Autoscaling In Private Cloud Environments." International Journal of Advances in Applied Computational Intelligence 7, no. 1 (2025): 63–77. https://doi.org/10.54216/ijaaci.070105.
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The research focuses on an accurate workload prediction approach for auto-scaling resources in the Private Cloud using improved Time-Series models. Although many factors still result in dynamic workloads of cloud systems, an accurate forecast becomes vital for service quality and cost. The chapter discusses a Proactive Prediction Engine (PPE) framework using Auto Regressive Integrated Moving Average (ARIMA) and Recurrent Neural Network Long Short-Term, to forecast CPU utilization. Real-time datasets of OpenStack private cloud and Amazon AWS were used for experimental evaluation. The analyses show that the RNN_LSTM model performs far better than ARIMA by reducing the MAE and RMSE values by roughly 40 percent in each set. This has further reinforced that RNN_LSTM can model non-linearity and handle correlation issues in the workload data. Automated scaling of the instances with the Open Stack based on the predicted CPU load is made possible by the integration of RNN_LSTM prediction with OpenStack, supported by Terraform. This strategy reduces times of service outages and enables the efficient use of resources in the network. Regarding accuracy and automation, the proposed method can be a relevant solution for workload management for private cloud infrastructure. In this respect, the results support the implementation of deep learning-based predictive models to optimize the performance of autoscaling.
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Li, Lei, and Xue Gao. "Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction." Applied Sciences 15, no. 5 (2025): 2347. https://doi.org/10.3390/app15052347.
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Internet services are increasingly being deployed using cloud computing. However, the workload of an Internet service is not constant; therefore, the required cloud computing resources need to be allocated elastically to minimize the associated costs. Thus, this study proposes a proactive cloud resource scheduling framework. First, we propose a new workload prediction method—named the adaptive two-stage multi-neural network based on long short-term memory (LSTM)—which can adaptively route prediction tasks to the corresponding LSTM sub-model according to the workload change trend (i.e., uphill and downhill categories), in order to improve the predictive accuracy. To avoid the cost associated with manual labeling of the training data, the first-order gradient feature is used with the k-means algorithm to cluster and label the original training data set automatically into uphill and downhill training data sets. Then, based on stochastic queueing theory and the proposed prediction method, a maximum cloud service profit resource search algorithm based on the network workload prediction algorithm is proposed to identify a suitable number of virtual machines (VMs) in order to avoid delays in resource adjustment and increase the service profit. The experimental results demonstrate that the proposed proactive adaptive elastic resource scheduling framework can improve the workload prediction accuracy (MAPE: 0.0276, RMSE: 3.7085, R2: 0.9522) and effectively allocate cloud resources.
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Karpagam, Thulasi, and Jayashree Kanniappan. "Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems." Symmetry 17, no. 3 (2025): 383. https://doi.org/10.3390/sym17030383.
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Cloud computing offers scalable and adaptable resources on demand, and has emerged as an essential technology for contemporary enterprises. Nevertheless, it is still challenging work to efficiently handle cloud resources because of dynamic changes in load requirement. Existing forecasting approaches are unable to handle the intricate temporal symmetries and nonlinear patterns in cloud workload data, leading to degradation of prediction accuracy. In this manuscript, a Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems (MASNN-WL-RTSP-CS) is proposed. Here, the input data from the Google cluster trace dataset were preprocessed using Multi Window Savitzky–Golay Filter (MWSGF) to remove noise while preserving important data patterns and maintaining structural symmetry in time series trends. Then, the Multi-Dimensional Attention Spiking Neural Network (MASNN) effectively models symmetric patterns in workload fluctuations to predict workload and resource time series. To enhance accuracy, the Secretary Bird Optimization Algorithm (SBOA) was utilized to optimize the MASNN parameters, ensuring accurate workload and resource time series predictions. Experimental results show that the MASNN-WL-RTSP-CS method achieves 35.66%, 32.73%, and 31.43% lower Root Mean Squared Logarithmic Error (RMSLE), 25.49%, 32.77%, and 28.93% lower Mean Square Error (MSE), and 24.54%, 23.65%, and 23.62% lower Mean Absolute Error (MAE) compared with other approaches, like ICNN-WL-RP-CS, PA-ENN-WLP-CS, and DCRNN-RUP-RP-CCE, respectively. These advances emphasize the utility of MASNN-WL-RTSP-CS in achieving more accurate workload and resource forecasts, thereby facilitating effective cloud resource management.
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Sahi, Supreet Kaur, and V. S. Dhaka. "Performance Analysis of Web Applications Working on Cloud Environment Using Workload Prediction Model Based on ANN." International Journal of Emerging Research in Management and Technology 6, no. 6 (2018): 55. http://dx.doi.org/10.23956/ijermt.v6i6.245.
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Cloud computing is good fit for deployment of different applications but workload and instances requirement will vary depending upon type of applications. Workload estimation of cloud computing is tedious task. In cloud computing number of instances of cloud need to be reserved based on certain parameters. If these instance are under estimated then performance of system will reduce and if over estimated then cost will increase. In order to optimize this cost there must be some algorithm working that can help in reserving number of instances based on certain parameters. Web applications have unpredictable workload. Certain steps of capacity planning need to follow for predicting workload of web applications. This paper analysis performance of ANN based workload estimation model for web applications on cloud environment. A brief survey of literature is also presented to find out different parameters necessary for capacity planning of website.
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Torana Kamble, Et al. "Predictive Resource Allocation Strategies for Cloud Computing Environments Using Machine Learning." Journal of Electrical Systems 19, no. 2 (2024): 68–77. http://dx.doi.org/10.52783/jes.692.
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Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing. Businesses can quickly adapt to changing market conditions and operational needs with cloud-based solutions' adaptability, scalability, and cost-efficiency. IT operations and service delivery have changed due to widespread computational resource access. Cloud computing efficiently allocates resources in cloud environments, making it crucial to this transformation. Resource allocation impacts efficiency, cost, performance, and SLAs. Users and providers can allocate cloud resources based on workloads using elasticity, scalability, and on-demand provisioning. IT economics and operational effectiveness have changed due to rapid and flexible resource allocation. Proactive versus reactive resource allocation is key to understanding cloud resource management challenges and opportunities. Reactive strategies allocate resources only when shortages or surpluses occur at demand. This responsive strategy often leads to inefficiencies like over- or under-allocation, which raises costs and lowers performance. Predictive analysis and workload forecasting predict resource needs in proactive resource allocation. Optimize resource use to avoid shortages and over-provisioning. Attention has been drawn to proactive predictive resource allocation. These methods predict resource needs using historical data, machine learning, and predictive analytics. Predictive strategies optimize resource allocation by considering future decisions. Reduced bottlenecks boost user satisfaction and lower operational costs. Matching resource distribution to workloads optimizes cloud resource management. Resource allocation prediction improves with deep learning. CNN, LSTM, and Transformer cloud resource forecasting algorithms are promising. New tools for accurate and flexible workload predictions have come from their ability to spot intricate patterns in historical data. This paper compares CNN, LSTM, and Transformer deep learning algorithms for cloud computing resource allocation forecasting. This study determines the best predictive accuracy and workload ada[1]ptability algorithm using Google Cluster Data (GCD). The study evaluates upgrading cloud computing resource allocation with the Transformer model. This study advances predictive resource allocation strategies, which can help cloud service providers and organizations improve resource utilization, cost-effectiveness, and performance in the face of rapid technological change.
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Myung, Rohyoung, and Sukyong Choi. "Machine-Learning Based Memory Prediction Model for Data Parallel Workloads in Apache Spark." Symmetry 13, no. 4 (2021): 697. http://dx.doi.org/10.3390/sym13040697.
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A lack of memory can lead to job failures or increase processing times for garbage collection. However, if too much memory is provided, the processing time is only marginally reduced, and most of the memory is wasted. Many big data processing tasks are executed in cloud environments. When renting virtual resources in a cloud environment, it is necessary to pay the cost according to the specifications of resources (i.e., the number of virtual cores and the size of memory), as well as rental time. In this paper, given the type of workload and volume of the input data, we analyze the memory usage pattern and derive the efficient memory size of data-parallel workloads in Apache Spark. Then, we propose a machine-learning-based prediction model that determines the efficient memory for a given workload and data. To determine the validity of the proposed model, we applied it to data-parallel workloads which include a deep learning model. The predicted memory values were in close agreement with the actual amount of required memory. Additionally, the whole building time for the proposed model requires a maximum of 44% of the total execution time of a data-parallel workload. The proposed model can improve memory efficiency up to 1.89 times compared with the vanilla Spark setting.
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KaurSahi, Supreet, and V. S. Dhaka V.S.Dhaka. "A Review on Workload Prediction of Cloud Services." International Journal of Computer Applications 109, no. 9 (2015): 1–4. http://dx.doi.org/10.5120/19213-0911.
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Rasheduzzaman, Md, Md Amirul Islam, and Rashedur M. Rahman. "Workload Prediction on Google Cluster Trace." International Journal of Grid and High Performance Computing 6, no. 3 (2014): 34–52. http://dx.doi.org/10.4018/ijghpc.2014070103.
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Workload prediction in cloud systems is an important task to ensure maximum resource utilization. So, a cloud system requires efficient resource allocation to minimize the resource cost while maximizing the profit. One optimal strategy for efficient resource utilization is to timely allocate resources according to the need of applications. The important precondition of this strategy is obtaining future workload information in advance. The main focus of this analysis is to design and compare different forecasting models to predict future workload. This paper develops model through Adaptive Neuro Fuzzy Inference System (ANFIS), Non-linear Autoregressive Network with Exogenous inputs (NARX), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). Public trace data (workload trace version II) which is made available by Google were used to verify the accuracy, stability and adaptability of different models. Finally, this paper compares these prediction models to find out the model which ensures better prediction. Performance of forecasting techniques is measured by some popular statistical metric, i.e., Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Sum of Squared Error (SSE), Normalized Mean Squared Error (NMSE). The experimental result indicates that NARX model outperforms other models, e.g., ANFIS, ARIMA, and SVR.
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Ahamed, Zaakki, Maher Khemakhem, Fathy Eassa, Fawaz Alsolami, and Abdullah S. Al-Malaise Al-Ghamdi. "Technical Study of Deep Learning in Cloud Computing for Accurate Workload Prediction." Electronics 12, no. 3 (2023): 650. http://dx.doi.org/10.3390/electronics12030650.
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Proactive resource management in Cloud Services not only maximizes cost effectiveness but also enables issues such as Service Level Agreement (SLA) violations and the provisioning of resources to be overcome. Workload prediction using Deep Learning (DL) is a popular method of inferring complicated multidimensional data of cloud environments to meet this requirement. The overall quality of the model depends on the quality of the data as much as the architecture. Therefore, the data sourced to train the model must be of good quality. However, existing works in this domain have either used a singular data source or have not taken into account the importance of uniformity for unbiased and accurate analysis. This results in the efficacy of DL models suffering. In this paper, we provide a technical analysis of using DL models such as Recurrent Neural Networks (RNN), Multilayer Perception (MLP), Long Short-Term Memory (LSTM), and, Convolutional Neural Networks (CNN) to exploit the time series characteristics of real-world workloads from the Parallel Workloads Archive of the Standard Workload Format (SWF) with the aim of conducting an unbiased analysis. The robustness of these models is evaluated using the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error metrics. The findings of these highlight that the LSTM model exhibits the best performance compared to the other models. Additionally, to the best of our knowledge, insights of DL in workload prediction of cloud computing environments is insufficient in the literature. To address these challenges, we provide a comprehensive background on resource management and load prediction using DL. Then, we break down the models, error metrics, and data sources across different bodies of work.
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Sinduja, V. Infine, and P. Joesph Charles. "A hybrid approach using attention bidirectional gated recurrent unit and weight-adaptive sparrow search optimization for cloud load balancing." Scientific Temper 16, no. 05 (2025): 4270–83. https://doi.org/10.58414/scientifictemper.2025.16.5.12.
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With the evolution of cloud computing (CC) technologies, there is a growing insistence for the maximum utilization of cloud resources, therefore increasing the computing power consumption of cloud’s systems. Cloud’s Virtual Machines (VMs) consolidation imparts a practical mechanism to minimize energy consumption of cloud Data Centers (DC). Efficient consolidation and migration of VM in the absence of infringing Service Level Agreement (SLA) can be arrived at by making decisions proactively based on cloud’s future workload prediction. Efficient load balancing, another major issue of CC also depends on accurate forecasting of resource usage. Cloud workload traces reveal both periodic and non-periodic patterns with the unexpected peak of load. As a result, it is very demanding for the prediction models to accurately anticipate future workload. This prompted us to propose a method called, Attention Bidirectional Gated and Weight-adaptive Sparrow Search Optimization (ABiG-WSSO) to accurately forecast future workload with minimal makespan and overhead. The proposed ABiG-WSSO method includes Attention Bidirectional Gated Recurrent Unit (ABiGRU) and Weight-adaptive Sparrow Search Optimization (WSSO). Attention Bidirectional Gated Recurrent Unit (ABiGRU) is initially designed that along with the use of Bidirectional Gated Recurrent Unit (BiGRU) and adaptation of attention mechanism aids in predicting future cloud load requirements accurately. Next, Weight-adaptive Sparrow Search Optimization (WSSO) algorithm is employed in fine-tuning the parameters of the ABiGRU model for accurate and optimal load balancing performance. The WSSO algorithm is applied to optimize ABiGRU model hyperparameters (i.e. learning rate), to enhance its prediction accuracy. Comprehensive simulations are carried out using the gwa-bitbrains dataset to verify the efficiency of the proposed ABiG-WSSO method in boosting the distribution of resources and cloud load balancing. The proposed method achieves comparatively better results in terms of better makespan time, energy consumption, associated overhead and throughput.
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S, Suriya, and Surya Arvindh M. "Prediction of Workloads in Cloud using ARIMA-ANN." Journal of ISMAC 6, no. 4 (2025): 327–42. https://doi.org/10.36548/jismac.2024.4.003.
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This study introduces an innovative hybrid ARIMA-ANN model personalized for cloud workload prediction. Unlike existing models that focus solely on linear or nonlinear patterns, the approach combines the strengths of ARIMA for time-series linear trends and ANN for nonlinear data complexities. This integration ensures higher accuracy, as validated using the MIT Supercloud dataset. The methodology leverages data pre-processing, sensitivity analysis, and advanced validation techniques, demonstrating improved accuracy in scenarios of high workload variability. This model supports cloud providers in resource optimization and dynamic load management.
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Liu, Peng, Weisen Zhao, Baoliang Zhang, and Jing Wang. "Hybrid Elastic Scaling Strategy for Container Cloud based on Load Prediction and Reinforcement Learning." Journal of Physics: Conference Series 2732, no. 1 (2024): 012014. http://dx.doi.org/10.1088/1742-6596/2732/1/012014.
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Abstract To harness the advantages of both proactive and responsive scaling, adapting to various workload scenarios, this paper introduces a container hybrid scaling strategy called HyPredRL, rooted in load prediction and reinforcement learning. Within the proactive scaling module RL-PM, a load prediction model, MSC-LSTM, predict workloads and, in conjunction with current workload states, leverages reinforcement learning agents for intelligent scaling decisions. The responsive scaling strategy, SLA-HPA, enhances Kubernetes’ native scaling strategy, which primarily considers resource utilization, by incorporating response time metrics. Ultimately, a hybrid scaling controller is designed, applying the principles of “rapid scaling out” and “balanced conflicts” to coordinate proactive and responsive scaling. Experimental results demonstrate that HyPredRL outperforms existing methods in SLA violation rate, resource utilization, and request response time, effectively improving application performance and scalability.
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Gao, Mohan, Kexin Xu, Xiaofeng Gao, Tengwei Cai, and Haoyuan Ge. "Spatial-Temporal Heterogenous Graph Contrastive Learning for Microservice Workload Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11681–89. https://doi.org/10.1609/aaai.v39i11.33271.
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With the widely adoption of microservice architecture in the cloud computing industry, accurate prediction of workloads, especially CPU cores, can support reasonable resource allocation, thereby optimizing the resource utilization of the system. However, workload prediction is challenging in two dimensions. In the temporal dimension, workload series 1) has non-stationary characteristics, leading to poor predictability; 2) has a multi-periodic nature with entangled temporal patterns; 3) may be influenced by dynamic system states like response time and number of requests. In the spatial dimension, when regarding microservices as nodes in a distributed system, there is no topology caused by physical connections, but exists complex similarity dependencies. Extracting robust spatial features from these dependencies presents difficulties. To address these, we propose STEAM, a Spatio Temporal Heterogenous Graph Contrastive Learning for Microservice Workload Prediction. STEAM leverages non-stationary decomposition self-attention to extract temporal features from non-stationary and multi-periodic workload series, while the decoupled embedding is used to capture system state information of microservices. By treating microservices as nodes and constructing a similarity graph, STEAM effectively models the similarity relationships between microservices. To reduce the prior interference caused by the similarity threshold and improve the robustness, STEAM constructs two heterogeneous augmentation views and uses contrastive learning to extract the shared consistent spatial features. The multi-scale learning is adopted to model the long- and short-term temporal features, forming a spatio-temporal stacking structure. Experiments on two datasets, including MS dataset obtained from Ant Group, which is one of the world’s largest cloud service providers, demonstrate the superiority of STEAM.
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Li, Lei, Min Feng, Lianwen Jin, Shenjin Chen, Lihong Ma, and Jiakai Gao. "Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing." Journal of Information Technology Research 11, no. 4 (2018): 137–54. http://dx.doi.org/10.4018/jitr.2018100109.
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Online services are now commonly deployed via cloud computing based on Infrastructure as a Service (IaaS) to Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). However, workload is not constant over time, so guaranteeing the quality of service (QoS) and resource cost-effectiveness, which is determined by on-demand workload resource requirements, is a challenging issue. In this article, the authors propose a neural network-based-method termed domain knowledge embedding regularization neural networks (DKRNN) for large-scale workload prediction. Based on analyzing the statistical properties of a real large-scale workload, domain knowledge, which provides extended information about workload changes, is embedded into artificial neural networks (ANN) for linear regression to improve prediction accuracy. Furthermore, the regularization with noisy is combined to improve the generalization ability of artificial neural networks. The experiments demonstrate that the model can achieve more accuracy of workload prediction, provide more adaptive resource for higher resource cost effectiveness and have less impact on the QoS.
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Kecskemeti, Gabor, Zsolt Nemeth, Attila Kertesz, and Rajiv Ranjan. "Cloud workload prediction based on workflow execution time discrepancies." Cluster Computing 22, no. 3 (2018): 737–55. http://dx.doi.org/10.1007/s10586-018-2849-9.
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Selvan Chenni Chetty, Thirumalai, Vadim Bolshev, Siva Shankar Subramanian, et al. "Optimized Hierarchical Tree Deep Convolutional Neural Network of a Tree-Based Workload Prediction Scheme for Enhancing Power Efficiency in Cloud Computing." Energies 16, no. 6 (2023): 2900. http://dx.doi.org/10.3390/en16062900.
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Workload prediction is essential in cloud data centers (CDCs) for establishing scalability and resource elasticity. However, the workload prediction accuracy in the cloud data center could be better due to noise, redundancy, and low performance for workload prediction. This paper designs a hierarchical tree-based deep convolutional neural network (T-CNN) model with sheep flock optimization (SFO) to enhance CDCs’ power efficiency and workload prediction. The kernel method is used to preprocess historical information from the CDCs. Additionally, T-CNN model weight parameters are optimized using SFO. The suggested TCNN-SFO technology has successfully reduced excessive power consumption while correctly forecasting the incoming demand. Further, the proposed model is assessed using two benchmark datasets: Saskatchewan HTTP traces and NASA. The developed model is executed in a Java tool. Therefore, associated with existing methods, the developed technique has achieved higher accuracy of 20.75%, 19.06%, 29.09%, 23.8%, and 20.5%, as well as lower energy consumption of 20.84%, 18.03%, 28.64%, 30.72%, and 33.74% when validating the Saskatchewan HTTP traces dataset. It has also achieved higher accuracy of 32.95%, 12.05%, 32.65%, and 26.54%.
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Ahamed, Zaakki, Maher Khemakhem, Fathy Eassa, Fawaz Alsolami, Abdullah Basuhail, and Kamal Jambi. "Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments." Sensors 23, no. 15 (2023): 6911. http://dx.doi.org/10.3390/s23156911.
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The Federated Cloud Computing (FCC) paradigm provides scalability advantages to Cloud Service Providers (CSP) in preserving their Service Level Agreement (SLA) as opposed to single Data Centers (DC). However, existing research has primarily focused on Virtual Machine (VM) placement, with less emphasis on energy efficiency and SLA adherence. In this paper, we propose a novel solution, Federated Cloud Workload Prediction with Deep Q-Learning (FEDQWP). Our solution addresses the complex VM placement problem, energy efficiency, and SLA preservation, making it comprehensive and beneficial for CSPs. By leveraging the capabilities of deep learning, our FEDQWP model extracts underlying patterns and optimizes resource allocation. Real-world workloads are extensively evaluated to demonstrate the efficacy of our approach compared to existing solutions. The results show that our DQL model outperforms other algorithms in terms of CPU utilization, migration time, finished tasks, energy consumption, and SLA violations. Specifically, our QLearning model achieves efficient CPU utilization with a median value of 29.02, completes migrations in an average of 0.31 units, finishes an average of 699 tasks, consumes the least energy with an average of 1.85 kWh, and exhibits the lowest number of SLA violations with an average of 0.03 violations proportionally. These quantitative results highlight the superiority of our proposed method in optimizing performance in FCC environments.
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Gadhavi, Lata J., and Madhuri D. Bhavsar. "Prediction Based Efficient Resource Provisioning and Its Impact on QoS Parameters in the Cloud Environment." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 5359. http://dx.doi.org/10.11591/ijece.v8i6.pp5359-5370.
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<p>The purpose of this paper is to provision the on demand resources to the end users as per their need using prediction method in cloud computing environment. The provisioning of virtualized resources to cloud consumers according to their need is a crucial step in the deployment of applications on the cloud. However, the dynamical management of resources for variable workloads remains a challenging problem for cloud providers. This problem can be solved by using a prediction based adaptive resource provisioning mechanism, which can estimate the upcoming resource demands of applications. The present research introduces a prediction based resource provisioning model for the allocation of resources in advance. The proposed approach facilitates the release of unused resources in the pool with quality of service (QoS), which is defined based on prediction model to perform the allocation of resources in advance. In this work, the model is used to determine the future workload prediction for user requests on web servers, and its impact toward achieving efficient resource provisioning in terms of resource exploitation and QoS. The main contribution of this paper is to develop the prediction model for efficient and dynamic resource provisioning to meet the requirements of end users.</p>
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Daradkeh, Tariq, and Anjali Agarwal. "Cloud Workload and Data Center Analytical Modeling and Optimization Using Deep Machine Learning." Network 2, no. 4 (2022): 643–69. http://dx.doi.org/10.3390/network2040037.
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Predicting workload demands can help to achieve elastic scaling by optimizing data center configuration, such that increasing/decreasing data center resources provides an accurate and efficient configuration. Predicting workload and optimizing data center resource configuration are two challenging tasks. In this work, we investigate workload and data center modeling to help in predicting workload and data center operation that is used as an experimental environment to evaluate optimized elastic scaling for real data center traces. Three methods of machine learning are used and compared with an analytical approach to model the workload and data center actions. Our approach is to use an analytical model as a predictor to evaluate and test the optimization solution set and find the best configuration and scaling actions before applying it to the real data center. The results show that machine learning with an analytical approach can help to find the best prediction values of workload demands and evaluate the scaling and resource capacity required to be provisioned. Machine learning is used to find the optimal configuration and to solve the elasticity scaling boundary values. Machine learning helps in optimization by reducing elastic scaling violation and configuration time and by categorizing resource configuration with respect to scaling capacity values. The results show that the configuration cost and time are minimized by the best provisioning actions.
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Pradeep, Kumar. "Adaptive Workload Modeling using AI for Performance Testing of Cloud-Based Multitenant Enterprise Applications." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 1 (2024): 1–17. https://doi.org/10.5281/zenodo.15087595.
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Cloud-based multitenant enterprise applications face growing challenges in optimizing performance, managing resources efficiently, and ensuring scalability due to unpredictable workload fluctuations. Traditional workload management approaches, such as rule-based and threshold-based autoscaling, struggle to accurately forecast and respond to dynamic workload variations, leading to higher latency, inefficient resource utilization, and increased operational costs. To address these challenges, this paper introduces an AI-driven adaptive workload modeling framework that leverages machine learning (ML) for workload forecasting and reinforcement learning (RL) for real-time resource adaptation.The proposed framework utilizes ML models such as Long Short-Term Memory (LSTM) and XGBoost to analyze historical workload patterns and predict future demand. In parallel, RL-based techniques, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), dynamically adjust resource allocation based on system performance in real time. Experimental evaluations conducted in a cloud-based test environment demonstrate that the AI-driven system outperforms traditional autoscaling methods, reducing resource adjustment time by 50%, improving workload prediction accuracy by 30-40%, and lowering cloud computing costs by 35-50%.Beyond performance gains, the AI-driven approach enhances service reliability, system responsiveness, and workload balancing by proactively preventing resource bottlenecks and overload conditions. However, challenges remain in handling unexpected workload spikes, minimizing computational overhead for AI inference, and adapting models to diverse application environments. Future research should explore collaborative AI-driven workload models for multi-cloud environments, interpretable AI techniques for transparent decision-making, and advanced computing methods for optimizing real-time AI-based workload adjustments.The findings of this study highlight the potential of AI-powered workload management in transforming cloud performance optimization. By enabling self-adjusting, intelligent cloud systems with minimal human intervention, this approach offers significant advantages for cloud service providers, SaaS companies, and enterprises aiming to enhance operational efficiency and cost-effectiveness.
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Karimunnisa, Syed, and Yellamma Pachipala. "Deep Learning-Driven Workload Prediction and Optimization for Load Balancing in Cloud Computing Environment." Cybernetics and Information Technologies 24, no. 3 (2024): 21–38. http://dx.doi.org/10.2478/cait-2024-0023.
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Abstract Cloud computing revolutionizes as a technology that succeeds in serving large-scale user demands. Workload prediction and scheduling tend to be factors dictating cloud performance. Forecasting the future workload in due to avoid unfair resource allocation, emerges to be a crucial inspecting feature for enhanced performance. The aforementioned issues of interest are addressed in our work by soliciting a Deep Learning driven Max-out prediction model, which efficiently forecasts the future workload by providing a balanced approach for enhanced scheduling with the Tasmanian Devil-Bald Eagle Search (TDBES) optimization algorithm. The results obtained proved that the TDBES scored efficacy in makespan with 16.75%, migration cost with 14.78%, and a migration efficiency rate of 9.36% over other existing techniques like DBOA, WACO, and MPSO, with additional error analysis of prediction performance using RMSE, MAP, and MAE, among which our contributed approach overrides traditional methods with least error.
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Marinho, Carlos S. S., Leonardo O. Moreira, Emanuel F. Coutinho, José S. Costa Filho, Flávio R. C. Sousa, and Javam C. Machado. "LABAREDA: A Predictive and Elastic Load Balancing Service for Cloud-Replicated Databases." Journal of Information and Data Management 9, no. 1 (2018): 94. http://dx.doi.org/10.5753/jidm.2018.1639.
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Cloud computing emerges as an alternative to promote quality of service for data-driven applications. Database management systems must be available to support the deployment of cloud applications resorting to databases.Many solutions use database replication as a strategy to increase availability and decentralize the workload of database transactions among replicas. Due to the distribution of database transactions among replicas, load balancing techniques improve the computational resources utilization. However, several solutions use the current state of the database service to make decisions for the distribution of transactions. This article proposes a predictive and elastic load balancing service for replicated cloud databases. Experiments carried out showed that the use of prediction models can help to predict possible SLA violations in time series that represent workloads of cloud-replicated databases.
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Hanif, Naufal, Dadang Priyanto, and Neny Sulistianingsih. "Prediksi Beban Kerja Server Secara Real-Time pada Pusat Data Cloud dengan Pendekatan Gabungan Long Short-Term Memory (LSTM) dan Fuzzy Logic." JTIM : Jurnal Teknologi Informasi dan Multimedia 7, no. 3 (2025): 420–32. https://doi.org/10.35746/jtim.v7i3.731.
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Efficient resource management in Cloud Data Centers is essential to reduce energy waste and maintain optimal system performance. This study aims to predict server workload in real time using a hybrid approach that combines Long Short-Term Memory (LSTM) and Fuzzy Logic. CPU and RAM usage data were collected every second from a Proxmox Cluster using its API, then normalized and processed using an LSTM model to forecast future workloads. The predicted results were then classified using Fuzzy Logic into three workload categories: light, medium, and heavy. The model was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), where the results showed an MAE of 2.48 on the training data and 3.09 on the testing data, as well as RMSE values of 5.15 and 5.57, respectively. Based on these evaluation results, the prediction system achieved an accuracy of 97.52% on the training data and 96.91% on the testing data, indicating that the model can generate accurate and stable predictions. This method enables automated decision-making such as workload-based power management, thereby improving energy efficiency and overall system performance.
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Jeyarani, R., N. Nagaveni, Satish Kumar Sadasivam, and Vasanth Ram Rajarathinam. "Power Aware Meta Scheduler for Adaptive VM Provisioning in IaaS Cloud." International Journal of Cloud Applications and Computing 1, no. 3 (2011): 36–51. http://dx.doi.org/10.4018/ijcac.2011070104.
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Cloud Computing provides on-demand access to a shared pool of configurable computing resources. The major issue lies in managing extremely large agile data centers which are generally over provisioned to handle unexpected workload surges. This paper focuses on green computing by introducing Power-Aware Meta Scheduler, which provides right fit infrastructure for launching virtual machines onto host. The major challenge of the scheduler is to make a wise decision in transitioning state of the processor cores by exploiting various power saving states inherent in the recent microprocessor technology. This is done by dynamically predicting the utilization of the cloud data center. The authors have extended existing cloudsim toolkit to model power aware resource provisioning, which includes generation of dynamic workload patterns, workload prediction and adaptive provisioning, dynamic lifecycle management of random workload, and implementation of power aware allocation policies and chip aware VM scheduler. The experimental results show that the appropriate usage of different power saving states guarantees significant energy conservation in handling stochastic nature of workload without compromising the performance, both when the data center is in low as well as moderate utilization.
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Kapil Pothakanoori. "Energy-efficient Cloud Infrastructure for IoT Device Management: A Comprehensive Analysis of Edge-Cloud Workload Distribution Strategies." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1439–49. https://doi.org/10.32628/cseit241061190.
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The rapid expansion of the Internet of Things (IoT) has led to significant increases in data traffic and computational demands on cloud infrastructures, raising concerns about energy consumption in data centers. This article explores innovative approaches to creating energy-efficient cloud infrastructures for managing IoT device fleets through optimized workload distribution between edge devices and cloud resources. It proposes implementing energy-aware algorithms that dynamically determine the optimal location for data processing based on energy consumption, latency requirements, and workload characteristics. The system employs advanced machine learning techniques for workload prediction and resource allocation, demonstrating substantial improvements in energy efficiency while maintaining high-performance standards. Case studies in smart cities, agricultural monitoring, and transportation networks validate the effectiveness of this approach in real-world scenarios. The results indicate that intelligent workload distribution across edge and cloud platforms can significantly reduce energy consumption while enhancing system performance and operational efficiency, providing a sustainable pathway for large-scale IoT deployments.
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Tri Fidrian Arya, Reza Fuad Rachmad, and Achmad Affandi. "Cloud Node Auto-Scaling System Automation Based on Computing Workload Prediction." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 5 (2024): 597–606. https://doi.org/10.29207/resti.v8i5.5928.
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Auto-scaling systems in cloud computing are important for handling application workload fluctuations. This research uses machine learning to predict resource requirements based on workload work patterns and design an automatic scaling system. The dataset used includes features of node name, time, CPU usage percentage, and RAM usage. The ML model is applied for prediction regression of CPU usage percentage, CPU load, and RAM usage, and then server workload is classified into four categories: Very High, High, Low, and Very Low. The autoscaling system used is horizontal scaling. From the results of this research, it was found that the stacking algorithm with the base learner Random Forest and XGBoost had better performance in producing predictive regression. Then, after performing stability testing using K-Fold cross-validation by classifying based on workload status, it was found that the Gradient Boosting algorithm had better results compared to other algorithms, namely for the percentage of CPU usage with an accuracy of 0.998, precision 0.9, recall 0.878, f1score 0.888; CPU load average 15 minutes with accuracy 0.997, precision 0.854, recall 0.863, f1score 0.863; Meanwhile, the percentage of RAM usage is accuracy 0.992, precision 0.986, recall 0.986, and f1score 0.986. However, the XGBoost algorithm also has test results that are almost the same as Gradient Boosting.
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Kenga, Derdus, Vincent Omwenga, and Patrick Ogao. "Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds." Journal of Information Technology and Computer Science 6, no. 2 (2021): 170–82. http://dx.doi.org/10.25126/jitecs.202162196.
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The main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing approaches focus on VM allocation and migration, which only leads to physical machine (PM) level optimization. Other approaches use horizontal auto-scaling, which is not a visible solution in the case of IaaS public cloud. In this paper, we propose an approach of customizing user VM’s size to match the resources requirements of their application workloads based on an analysis of real backend traces collected from a VM in a production data centre. In this approach, a VM is given fixed size resources that match applications workload demands and any demand that exceeds the fixed resource allocation is predicted and handled through vertical VM auto-scaling. In this approach, energy consumption by PMs is reduced through efficient resource utilization. Experimental results obtained from a simulation on CloudSim Plus using GWA-T-13 Materna real backend traces shows that data center energy consumption can be reduced via efficient resource utilization
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Feng, Binbin, and Zhijun Ding. "Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives." Tsinghua Science and Technology 30, no. 1 (2025): 34–54. http://dx.doi.org/10.26599/tst.2024.9010024.
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Li, Zhulin, Cuirong Wang, Haiyan Lv, and Tongyu Xu. "Research on CPU Workload Prediction and Balancing in Cloud Environment." International Journal of Hybrid Information Technology 8, no. 2 (2015): 159–72. http://dx.doi.org/10.14257/ijhit.2015.8.2.14.
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Jingwen Liu and Yifan Zhou. "Predictive CPU Utilization Modeling in Cloud Operating Systems Using Machine Learning." Frontiers in Robotics and Automation 2, no. 1 (2025): 25–32. https://doi.org/10.71465/fra280.
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Efficient CPU utilization is vital for maintaining performance and reducing operational costs in cloud computing environments. As workloads grow increasingly dynamic and complex, traditional resource allocation methods struggle to adapt in real time. This paper explores the use of machine learning techniques to model and predict CPU utilization within cloud operating systems. By analyzing historical usage data, workload characteristics, and system metrics, we construct predictive models that provide real-time insights for proactive resource management. Our proposed framework leverages supervised learning algorithms, including random forests and neural networks, to capture non-linear trends and temporal dependencies. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods, highlighting the feasibility of machine learning-based forecasting for optimizing cloud CPU resource allocation.
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Velayutham, Vijayasherly, and Srimathi Chandrasekaran. "A Prediction based Cloud Resource Provisioning using SVM." Recent Advances in Computer Science and Communications 13, no. 3 (2020): 531–35. http://dx.doi.org/10.2174/2666255813666200206124025.
Full textAbstract:
Aim: To develop a prediction model grounded on Machine Learning using Support Vector Machine (SVM). Background: Prediction of workload in a Cloud Environment is one of the primary task in provisioning resources. Forecasting the requirements of future workload lies in the competency of predicting technique which could maximize the usage of resources in a cloud computing environment. Objective: To reduce the training time of SVM model. Methods: K-Means clustering is applied on the training dataset to form ‘n’ clusters firstly. Then, for every tuple in the cluster, the tuple’s class label is compared with the tuple’s cluster label. If the two labels are identical then the tuple is rightly classified and such a tuple would not contribute much during the SVM training process that formulates the separating hyperplane with lowest generalization error. Otherwise the tuple is added to the reduced training dataset. This selective addition of tuples to train SVM is carried for all clusters. The support vectors are a few among the samples in reduced training dataset that determines the optimal separating hyperplane. Results: On Google Cluster Trace dataset, the proposed model incurred a reduction in the training time, Root Mean Square Error and a marginal increase in the R2 Score than the traditional SVM. The model has also been tested on Los Alamos National Laboratory’s Mustang and Trinity cluster traces. Conclusion: The Cloudsim’s CPU utilization (VM and Cloudlet utilization) was measured and it was found to increase upon running the same set of tasks through our proposed model.