Relevant bibliographies by topics / Cloud Workload prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Cloud Workload prediction'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "Cloud Workload prediction"

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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 a
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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 reliab
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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
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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
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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 approach
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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 an
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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, an
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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 impr
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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 resourc
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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
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Dissertations / Theses on the topic "Cloud Workload prediction"

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LaCurts, Katrina L. (Katrina Leigh). "Application workload prediction and placement in cloud computing systems." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91039.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>97<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 128-135).<br>Cloud computing has become popular in recent years. Companies such as Amazon and Microsoft host large datacenters of networked machines available for users to rent. These users are varied: from individual r
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Magalhães, Deborah Maria Vieira. "Workload modeling and prediction for resources provisioning in cloud." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/22987.

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MAGALHÃES, Deborah Maria Vieira. Workload modeling and prediction for resources provisioning in cloud. 2017. 100 f. Tese (Doutorado em Engenharia de Teleinformática)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2017.<br>Submitted by Hohana Sanders (hohanasanders@hotmail.com) on 2017-06-02T16:11:24Z No. of bitstreams: 1 2017_tese_dmvmagalhães.pdf: 5119492 bytes, checksum: 581c09b1ba042cf8c653ca69d0aa0d57 (MD5)<br>Approved for entry into archive by Marlene Sousa (mmarlene@ufc.br) on 2017-06-02T16:18:39Z (GMT) No. of bitstreams: 1 2017_tese_dmvmagalhães.pdf: 5119492 bytes, chec
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Hunt, Kristian. "Log Analysis for Failure Diagnosis and Workload Prediction in Cloud Computing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189186.

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The size and complexity of cloud computing systems makes runtime errors inevitable. These errors could be caused by the system having insufficient resources or an unexpected failure in the system. In order to be able to provide highly available cloud computing services it is necessary to auto- mate the resource provisioning and failure diagnosing processes as much as possible. Log files are often a good source of information about the current status of the system. In this thesis methods for diagnosing failures and predicting system workload using log file analysis are presented and the perform
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Chen, Yu-Fan, and 陳昱帆. "Knowledge-based Event Workload Prediction for Dynamic Resource Reallocation in Cloud Application Performance Management." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/70184922784538853020.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>101<br>In Application Performance Management (APM), the most common problem encountered by the administrators of many network service providers is how to sustain performance targets for the applications. Cloud computing offers the possibility to provide the resources on demand as Utility Computing. The internet services with highly changing demand can benefit from the implementation of cloud services rapid elasticity technology as live sport game broadcasting or online ticket booking system. We expect that specific events will cause significant increase the demand o
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Chuang, Feng-Wei, and 莊峰瑋. "A Predictive Method for Workload Forecasting in the Cloud Environment." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/47248265463757800553.

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碩士<br>國立東華大學<br>資訊工程學系<br>101<br>Cloud computing provides powerful computing capabilities, and also provides a flexible user pay mechanism, which makes the cloud more convenient. Due to a steady increase in the amount of data, people are getting more and more use out of the cloud. Therefore, in order to improve the performance and energy savings of the cloud, its resource allocation efficiency has become very important. In this thesis, we propose a neural network model to predict the workload of Cloud servers. Through this prediction mechanism, cloud service providers can know the following ti
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Book chapters on the topic "Cloud Workload prediction"

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Alzamil, Ibrahim, and Karim Djemame. "Energy Prediction for Cloud Workload Patterns." In Economics of Grids, Clouds, Systems, and Services. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61920-0_12.

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Suleiman, Basem, Muhammad Johan Alibasa, Ya-Yuan Chang, and Ali Anaissi. "Predictive Auto-scaling: LSTM-Based Multi-step Cloud Workload Prediction." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0989-2_1.

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Bhagavathiperumal, Sivasankari, and Madhu Goyal. "Dynamic Provisioning of Cloud Resources Based on Workload Prediction." In Lecture Notes in Networks and Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7150-9_5.

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Östberg, Per-Olov, Thang Le Duc, Paolo Casari, Rafael García Leiva, Antonio Fernández Anta, and Jörg Domaschka. "Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications." In Managing Distributed Cloud Applications and Infrastructure. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39863-7_3.

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Gadhavi, Lata J., and Madhuri D. Bhavsar. "Efficient Resource Provisioning Through Workload Prediction in the Cloud System." In Smart Innovation, Systems and Technologies. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0077-0_33.

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Patel, Yashwant Singh, and Rajiv Misra. "Performance Comparison of Deep VM Workload Prediction Approaches for Cloud." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7871-2_15.

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Ismaeel, Salam, and Ali Miri. "Multivariate Time Series ELM for Cloud Data Centre Workload Prediction." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39510-4_52.

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Lalitha Devi, K., K. Deepa Thilak, K. Kalaiselvi, and K. Arthi. "Workload Prediction for Resource Scaling and Migration in the Cloud." In Information and Communication Technology for Competitive Strategies (ICTCS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9304-6_18.

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Gao, Ming, Yuchan Li, and Jixiang Yu. "Workload Prediction of Cloud Workflow Based on Graph Neural Network." In Web Information Systems and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87571-8_15.

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Yadav, Mahendra Pratap, and Dharmendra Kumar Yadav. "Workload Prediction for Cloud Resource Provisioning using Time Series Data." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2712-5_37.

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Conference papers on the topic "Cloud Workload prediction"

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Lu, Hui, Yi Zhang, Zebin Wu, and Zhihui Wei. "Cloudformer: Contrastive Learning Based Cloud Workload Prediction." In 2024 7th International Conference on Electronics Technology (ICET). IEEE, 2024. http://dx.doi.org/10.1109/icet61945.2024.10672804.

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Jiang, Jinhui, Xiushuang Yi, Yuting Zhao, and Tengsheng Tu. "Pattern Mining-Based Integrated Prediction Method for Cloud Workload." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN). IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744135.

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Zhang, Biying, Zhimin Qiu, Yanping Chen, and Yuling Huang. "Cloud workload prediction by the DE-based nonstationary transformer model." In International Conference on Network Communication and Information Security (ICNCIS 2024), edited by Pascal Lorenz and Ljiljana Trajkovic. SPIE, 2025. https://doi.org/10.1117/12.3052128.

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Vajpayee, Abhay, Pawan Kumar Tiwari, Shiv Prakash, Tiansheng Yang, and Raikumar Singh Rathore. "A Data-Driven Workload Prediction Model for Cloud Computing Using Machine Learning." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836480.

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Deshmukh, Hrushikesh. "Coordinate Attention Mechanism based Gated Recurrent Unit for Workload Prediction in Cloud Computing." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934669.

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Smorodnikov, Grigory, Ruslan Zolotarev, Aleksandra Rykova, Artem Sabutkevich, and Aleksandr Samochadin. "Elastic cloud resource allocation using short-term long short-term memory-based workload prediction." In Fourth International Conference on Optics, Computer Applications, and Materials Science (CMSD-IV 2024), edited by Arthur Gibadullin and Ramazon Abdullozoda. SPIE, 2025. https://doi.org/10.1117/12.3060861.

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Wen, Linfeng, Minxian Xu, Adel N. Toosi, and Kejiang Ye. "TempoScale: A Cloud Workloads Prediction Approach Integrating Short-Term and Long-Term Information." In 2024 IEEE 17th International Conference on Cloud Computing (CLOUD). IEEE, 2024. http://dx.doi.org/10.1109/cloud62652.2024.00030.

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Mahajan, Isham, and Deepak Nadig. "Enhancing Workload Predictions Using Service Interactions in Cloud-Native Microservices." In 2024 IEEE 13th International Conference on Cloud Networking (CloudNet). IEEE, 2024. https://doi.org/10.1109/cloudnet62863.2024.10815917.

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Lackinger, Anna, Andrea Morichetta, and Schahram Dustdar. "Time Series Predictions for Cloud Workloads: A Comprehensive Evaluation." In 2024 IEEE International Conference on Service-Oriented System Engineering (SOSE). IEEE, 2024. http://dx.doi.org/10.1109/sose62363.2024.00011.

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S, Senthil Pandi, Kumar P, and R. M. Suchindhar. "Predicting Cloud Workloads: Using Machine Learning with Amazon Infrastructure." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690810.

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