NDLI: CPU utilization prediction methods overview

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CPU utilization prediction methods overview

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CPU utilization prediction methods overview

Content Provider	ACM Digital Library
Author	Kudinova, Marina Melekhova, Anna Verinov, Alexander
Abstract	CPU utilization prediction is an important component of the resource allocation and job scheduling problems in virtualized servers and clouds. Utilization prediction is used by a resource manager to balance workload among physical nodes thus increasing the efficiency of resource usage. In the case of virtual machine distribution task the accurate prediction of CPU utilization allows to migrate one or more virtual machines, avoiding the physical machines' overload. For example, DRS - dynamic resource scheduler in the cloud, uses CPU load prediction for balancing workload and forecasting depletion of resources. However, due to diversity of forecasting methods it is challenging to choose a suitable one for each individual situation. This overview discusses the most popular approaches to CPU utilization prediction, the advantages and disadvantages of the methods being applied and the appliance limitations. We focus on regression models (ARFIMA and its special cases), Bayesian models, the Kalman filter and clustering. The mathematical basis and implementation algorithms of the corresponding methods are provided, the methods' assumptions are also mentioned. In reviewed papers authors stated that there is a correlation between the load level of different components of the same application, and between statistics of operating system activity and CPU utilization. This correlation could be used to improve the prediction accuracy. It was concluded that the choice of prediction method should be based on the following load characteristics: the ratio of the duration of the job and the prediction horizon, the characteristic length of load stationarity, the importance of random fluctuations. As a summary of the overview, a comparison of different approaches to the utilization prediction is provided.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450341301
DOI	10.1145/2855667.2855675
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cloud systems Processor utilization Prediction methods Virtual machines Cpu load Resource management Virtualization Cpu usage
Content Type	Text
Resource Type	Article

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