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  1. Proceedings of the 11th Central & Eastern European Software Engineering Conference in Russia (CEE-SECR '15)
  2. CPU utilization prediction methods overview
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Securing outsourced database: architecture for protected web resource
Implementation of request switch in PleskFS file system
Automatic request analyzer for QoS enabled storage system
Aspect markup of a source code for quick navigating a project
On development of static analysis tools for string-embedded languages
Some aspects of a modern common-purpose programming language design
CPU utilization prediction methods overview
Discovering clones in software: from complex algorithms to everyday desktop tool
Justification of the choice of neural networks learning algorithms for indoor mobile positioning
BDD by example: Russian bylina written in Gherkin language
Usage of design patterns as a kind of components of software architecture

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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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