Relevant bibliographies by topics / Google Cluster trace

Contents

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

Academic literature on the topic 'Google Cluster trace'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Google Cluster trace"

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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 Neur
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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.

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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 comp
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Gao, Xia, and Fangqin Xu. "Research on task offloading based on deep reinforcement learning in mobile edge environment." MATEC Web of Conferences 309 (2020): 03026. http://dx.doi.org/10.1051/matecconf/202030903026.

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With the rapid development of Internet technology and mobile terminals, users’ demand for high-speed networks is increasing. Mobile edge computing proposes a distributed caching approach to deal with the impact of massive data traffic on communication networks, in order to reduce network latency and improve user service quality. In this paper, a deep reinforcement learning algorithm is proposed to solve the task unloading problem of multi-service nodes. The simulation platform iFogSim and data set Google Cluster Trace are used to carry out experiments. The final results show that the task offl
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Xu, Xin, and Huiqun Yu. "A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/915878.

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On-demand resource management is a key characteristic of cloud computing. Cloud providers should support the computational resource sharing in a fair way to ensure that no user gets much better resources than others. Another goal is to improve the resource utilization by minimizing the resource fragmentation when mapping virtual machines to physical servers. The focus of this paper is the proposal of a game theoretic resources allocation algorithm that considers the fairness among users and the resources utilization for both. The experiments with an FUGA implementation on an 8-node server clus
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Hu, Rongdong, Guangming Liu, Jingfei Jiang, and Lixin Wang. "A New Resources Provisioning Method Based on QoS Differentiation and VM Resizing in IaaS." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/215147.

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In order to improve the host energy efficiency in IaaS, we proposed an adaptive host resource provisioning method, CoST, which is based on QoS differentiation and VM resizing. The control model can adaptively adjust control parameters according to real time application performance, in order to cope with changes in load. CoST takes advantage of the fact that different types of applications have different sensitivity degrees to performance and cost. It places two different types of VMs on the same host and dynamically adjusts their sizes based on the load forecasting and QoS feedback. It not onl
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van Renen, Alexander, and Viktor Leis. "Cloud Analytics Benchmark." Proceedings of the VLDB Endowment 16, no. 6 (2023): 1413–25. http://dx.doi.org/10.14778/3583140.3583156.

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The cloud facilitates the transition to a service-oriented perspective. This affects cloud-native data management in general, and data analytics in particular. Instead of managing a multi-node database cluster on-premise, end users simply send queries to a managed cloud data warehouse and receive results. While this is obviously very attractive for end users, database system architects still have to engineer systems for this new service model. There are currently many competing architectures ranging from self-hosted (Presto, PostgreSQL), over managed (Snowflake, Amazon Redshift) to query-as-a-
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Faisal Haroon. "Intelligent Resource Allocation in Cloud Computing Environments: Leveraging Machine Learning for Dynamic Workload Balancing, Cost Efficiency, and Performance Optimization." Annual Methodological Archive Research Review 3, no. 4 (2025): 526–49. https://doi.org/10.63075/gdxsyd86.

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Cost optimization is widely considered a critical aspect of resource management in current cloud computing scenarios to consider service quality, resource utilization and SLAs. The traditional static or the rule based algorithms are not well suited to handle the dynamics, heterogeneity and variability of workloads usually found in cloud data centers of vast sizes. To address these challenges, the current research introduces an intelligent resource allocation framework that uses machine learning. In particular, the workload prediction is achieved with a Random Forest model, while the resource m
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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 Resourc
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Hussain, Altaf, and Muhammad Aleem. "GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures." Data 3, no. 4 (2018): 38. http://dx.doi.org/10.3390/data3040038.

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Developers of resource-allocation and scheduling algorithms share test datasets (i.e., benchmarks) to enable others to compare the performance of newly developed algorithms. However, mostly it is hard to acquire real cloud datasets due to the users’ data confidentiality issues and policies maintained by Cloud Service Providers (CSP). Accessibility of large-scale test datasets, depicting the realistic high-performance computing requirements of cloud users, is very limited. Therefore, the publicly available real cloud dataset will significantly encourage other researchers to compare and benchmar
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Dzhyhora, O. M. "Bibliometric analysis of scientific research on the financial mechanism of NATO activities in the enlargement process." Economic Bulletin of Dnipro University of Technology, no. 90 (June 30, 2025): 48–59. https://doi.org/10.33271/ebdut/90.048.

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Methods. In the process of scientific research, a set of general scientific and specific research methods was applied. In particular, structural and functional analysis were used to identify internal relationships between key components of the financial mechanism of NATO activities in the process of enlargement. The method of analytical synthesis ensured the formation of a holistic theoretical and methodological model of the study. The bibliometric approach allowed for a quantitative and qualitative assessment of scientific productivity in the relevant field of knowledge, to identify leading r
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Dissertations / Theses on the topic "Google Cluster trace"

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Olivetti, Dennis. "Simulazione di datacenter e sua validazione utilizzando tracce di Google." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6650/.

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Sono state analizzate tracce rilasciate da Google riguardanti il funzionamento di uno dei suoi cluster allo scopo di capirne il funzionamento. In Omnet++ è stato implementato un modello di datacenter e lo si è validato confrontandone i risultati con quelli ottenibile dalle tracce di Google.
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Book chapters on the topic "Google Cluster trace"

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Zhang, Shuo, and Yaping Liu. "Analysis and Modeling of Heterogeneity from Google Cluster Traces." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34528-9_16.

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Conference papers on the topic "Google Cluster trace"

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Jomah, Samah, and S. Aji. "Analysis of Workload in Google Cluster Trace based on Job Termination Events." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10626633.

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Rashid, Norfazlin, and Umi Kalsom Yusof. "Literature Survey: Statistical Characteristics of Google Cluster Trace." In 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA). IEEE, 2018. http://dx.doi.org/10.1109/icaccaf.2018.8776820.

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Rasheduzzaman, Md, Md Amirul Islam, Tasvirul Islam, Tahmid Hossain, and Rashedur M. Rahman. "Study of different forecasting models on Google cluster trace." In 2013 16th International Conference on Computer and Information Technology (ICCIT). IEEE, 2014. http://dx.doi.org/10.1109/iccitechn.2014.6997346.

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Rasheduzzaman, Md, Md Amirul Islam, Tasvirul Islam, Tahmid Hossain, and Rashedur M. Rahman. "Task shape classification and workload characterization of google cluster trace." In 2014 IEEE International Advance Computing Conference (IACC). IEEE, 2014. http://dx.doi.org/10.1109/iadcc.2014.6779441.

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Jassas, Mohammad, and Qusay H. Mahmoud. "Failure Analysis and Characterization of Scheduling Jobs in Google Cluster Trace." In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2018. http://dx.doi.org/10.1109/iecon.2018.8592822.

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Alam, Mansaf, Kashish Ara Shakil, and Shuchi Sethi. "Analysis and Clustering of Workload in Google Cluster Trace Based on Resource Usage." In 2016 19th IEEE Intl Conference on Computational Science and Engineering (CSE), IEEE 14th Intl Conference on Embedded and Ubiquitous Computing (EUC), and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES). IEEE, 2016. http://dx.doi.org/10.1109/cse-euc-dcabes.2016.271.

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Liu, Bingwei, Yinan Lin, and Yu Chen. "Quantitative workload analysis and prediction using Google cluster traces." In IEEE INFOCOM 2016 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2016. http://dx.doi.org/10.1109/infcomw.2016.7562213.

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Jassas, Mohammad S., and Qusay H. Mahmoud. "Failure Characterization and Prediction of Scheduling Jobs in Google Cluster Traces." In 2019 IEEE 10th GCC Conference & Exhibition (GCC). IEEE, 2019. http://dx.doi.org/10.1109/gcc45510.2019.1570516010.

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Alnooh, Ali Hussein Ali, and Dhuha Basheer Abdullah. "Investigation and Analysis of Google Cluster Usage Traces: Facts and Real-Time Issues." In 2018 International Conference on Engineering Technology and their Applications (IICETA). IEEE, 2018. http://dx.doi.org/10.1109/iiceta.2018.8458082.

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Bappy, Faisal Haque, Tariqul Islam, Tarannum Shaila Zaman, Raiful Hasan, and Carlos Caicedo. "A Deep Dive into the Google Cluster Workload Traces: Analyzing the Application Failure Characteristics and User Behaviors." In 2023 10th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2023. http://dx.doi.org/10.1109/ficloud58648.2023.00023.

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