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.
Highlights
The efforts made in optimizing ICT (Information Communication Technology) energy consumption have been largely focusing on efficient utilization of physical computational resources e.g., green networking, storage and computation in large scale data centers [1]
Conclusions and future work Differently from the majority of efforts which are made in achieving energy-efficient service provision from the service provider perspective, in this paper, we present a novel user-side off-the-cloud service optimization solution that facilitates efficient service utilizations, knowingly Off-the-Cloud Service Optimization (OCSO)
Typical formal methods or heuristics-based resource management optimizations as well as the official service provider resource scaling options expose various limitations in satisfying native green efficiency requirements when dealing with different workload patterns/ types and multiple metric monitor data
Summary
The efforts made in optimizing ICT (Information Communication Technology) energy consumption have been largely focusing on efficient utilization of physical computational resources e.g., green networking, storage and computation in large scale data centers [1]. For PaaS optimization, when the current application environment monitor data violates its current green up/down limit for the respected threshold, which indicates the environment is under/over provisioned, OCSO scales it in/out with the exact green optimal numbers of VMs (necessity for the real-time workload). This is due to the fact that threshold mechanisms would act only after green limit violations, by which time the VMs have already been running inefficiently for a while This work eliminates such limitation by advocating OCSO IaaS optimization, which rest on optimized threshold algorithms and rule evolution equations as following: For resource utilization regulation: RV t. The optimization rule parameters are updated for the new environment resource provision, which ends the optimization cycle
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