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Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs - 2018

Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs

Research Area:  Cloud Computing


In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service providers perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.


Author(s) Name:  WalayatHussain,Farookh Khadeer Hussain,Morteza Saberi,Omar Khadeer Hussain and Elizabeth Chang

Journal name:  Future Generation Computer Systems

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.future.2018.06.041

Volume Information:  Volume 89, December 2018, Pages 464-477