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

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.

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

“…, M e h r o t r a et al [49], Sun et al [43], F a n g et al [39], Huu et al [34], K o e h l e r [14] and J i a n g et al [16] solved the problem of energy-efficient resource allocation in cloud computing, thereby minimizing the cost of operation. F o l i n o and P i s a n i [18] S i n g h, J u n e j a and M a l h o t r a [19] tackled the problem of load balancing in computing environments.…”

“… -A l k h a n a k et al [25]  -W a n g et al [38]  -B i t t e n c o u r t and M a d e i r a [31]  -W u et al [10]  -H a n et al [4]  -A. K o n a k, S. K o n a k and S n y d e r [5]  -B a r a t i and S h a r i f i a n [7] Cloud load prediction K a r a m a g i and Z a p a t a [9]  -B a m a k a n et al  -F a n g et al [39]  -Huu et al [34]  -K o e h l e r [14]  -J i a n g et al [16]  -F o l i n o and P i s a n i [18]  -S i n g h, J u n e j a and M a l h o t r a [19]  -M a m u n et al [23] Speech-signal processing A r a n g o a et al [24] Decision-making system…”

“…G e et al [35] proposed a sharing system known as Lineage-Signature to find a solution to the Multiple Query optimization problem in the cloud computing. F a n g et al [39] proposed a novel user-side optimization process-Off-the-Cloud Service Optimization method for efficient service provision. S h i and B o [42] focused on studying the physical layer security in downlink heterogeneous Cloud radio access network systems.…”

An approach to unified cloud service access, manipulation and dynamic orchestration via semantic cloud service operation specification framework

“…Meanwhile, a series third-party service APIs are also available as an alternative programmable service and resource entrance. In fact, these service API call/respond operations often enable more effective service access and enhanced service function manipulation, since they allow users to control services and relevant resources from a much lower level [25].…”

“…To enable flexible and generic service access and manipulation via a single point of interface, we adopt OCSO API [25]. It comprises OCSO API Adapter which can flexibly invoke cloud service API libraries as (user) requested.…”