OCSO: Off-the-cloud service optimization for green efficient service resource utilization

Fang, Daren; Liu, Xiaodong; Liu, Lin; Yang, Hongji

Abstract

Many efforts have been made in optimizing cloud service resource management for efficient service provision and delivery, yet little research addresses how to consume the provisioned service resources efficiently. Meanwhile, typical existing resource scaling management approaches often rest on single monitor category statistics and are driven by certain threshold algorithms, they usually fail to function effectively in case of dealing with complicated and unpredictable workload patterns. Fundamentally, this is due to the inflexibility of using static monitor, threshold and scaling parameters. This paper presents Off-the-Cloud Service Optimization (OCSO), a novel user-side optimization solution which specifically deals with service resource consumption efficiency from the service consumer perspective. OCSO rests on an intelligent resource scaling algorithm which relies on multiple service monitor metrics plus dynamic threshold and scaling parameters. It can achieve proactive and continuous service optimizations for both real-world IaaS and PaaS services, through OCSO cloud service API. From the two series of experiments conducted over Amazon EC2 and ElasticBeanstalk using OCSO prototype, it is demonstrated that the proposed approach can make significant improvement over Amazon native automated service provision and scaling options, regardless of scaling up/down or in/out.

• Journal Article Type: Article
• Acceptance Date: May 22, 2014
• Online Publication Date: Jun 14, 2014
• Publication Date: 2014-12
• Deposit Date: Jul 29, 2014
• Publicly Available Date: May 16, 2017
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 1
• DOI: https://doi.org/10.1186/s13677-014-0009-1
• Keywords: Cloud computing; Auto scaling; Service API; Service optimization; Service resource consumption management
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/6918
• Publisher URL: http://dx.doi.org/10.1186/s13677-014-0009-1
• Contract Date: May 16, 2017