CPU Load Prediction Model for Distributed Computing

Bey, Kadda Beghdad; Benhammadi, Farid; Mokhtari, A.; Guessoum, Zahia

doi:10.1109/ispdc.2009.8

Cited by 24 publications

(15 citation statements)

References 14 publications

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“…Although ANN represents a universal approximation, but still have the drawbacks of in choosing a suitable algorithm, network structure, and initial condition. For butter performance, ANN may be combined with the typical prediction methods such as Sliding Window Method (SWM) [85], Auto-regression model [39], and Fuzzy System (FS) [23,39,144].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

2018

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Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

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Section: Artificial Neural Networkmentioning

confidence: 99%

“…Dynamic behavior forecasting problem can be resolved with ANN [38,140,150], Adaptive Neuro-Fuzzy Inference System (ANFIS) [23,39,144], Support Vector Machine (SVM) [7], and latent feature learning based models [35,36,39].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

2018

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“…Bey et al used several different models for time series prediction. They use an adaptive network to estimate the future value of CPU load for distributed computing . However, their hybrid predictors were designed to perform for one‐step‐ahead prediction and the work presented in this paper builds on this work by predicting both one step and multisteps ahead.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…CPU is one of the most important metrics for measuring and testing the performance of a host machine. Recent studies by other works investigated one‐step ahead CPU utilization forecasting methods such as local regression and feed‐forward neural networks. However, these one step ahead prediction models (usually forecast on a time scale no longer than 5 minutes ahead) give insufficient time for cloud resources to be adjusted.…”

Section: Introductionmentioning

confidence: 99%

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

et al. 2018

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One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.

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“…Bey et al [37], presented an approach near optimal for solving the independent task scheduling problems in heterogeneous distributed computing. The propose scheduler attempts to present a solution in two phase.…”

Section: Independent Task Scheduling Via Makespan Refinery Approachmentioning

confidence: 99%

Evaluation Performance of Task Scheduling Algorithms in Heterogeneous Environments

Yazdanpanah¹,

Shouraki²,

Jamali³

2016

IJCA

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A heterogeneous computing environment is a large-scale distributed data processing environment, it is depends to some extent parameters on the application and that classified in three main categories such as the hardware, the communication layer, and the software. A computer system is consists of hardware and software from two or more different manufacturers. Scheduling is one of the important factors in the heterogeneous environment and the aim of task scheduling in the processing environment is to move computation towards data. In order to achieve improve performance, increase the throughput and minimizing the makespan; scheduler must avoid unnecessary data transmission. Hence, different scheduling algorithms for heterogeneous computing environment are necessary to provide good performance. How to speedup scheduling the service resources to achieve the lowest cost becomes more and more important. This paper tries to illustrate and analyze the overview of eighteen different scheduling algorithms for heterogeneous computing environment and their scheduling issues and problems.

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CPU Load Prediction Model for Distributed Computing

Cited by 24 publications

References 14 publications

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

Evaluation Performance of Task Scheduling Algorithms in Heterogeneous Environments

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