Many efforts have been made in optimizing cloud service resource management for efficient service provision and\ndelivery, yet little research addresses how to consume the provisioned service resources efficiently. Meanwhile,\ntypical existing resource scaling management approaches often rest on single monitor category statistics and are\ndriven by certain threshold algorithms, they usually fail to function effectively in case of dealing with complicated\nand unpredictable workload patterns. Fundamentally, this is due to the inflexibility of using static monitor, threshold\nand scaling parameters. This paper presents Off-the-Cloud Service Optimization (OCSO), a novel user-side optimization\nsolution which specifically deals with service resource consumption efficiency from the service consumer perspective.\nOCSO rests on an intelligent resource scaling algorithm which relies on multiple service monitor metrics plus dynamic\nthreshold and scaling parameters. It can achieve proactive and continuous service optimizations for both real-world\nIaaS and PaaS services, through OCSO cloud service API. From the two series of experiments conducted over Amazon\nEC2 and Elastic Beanstalk using OCSO prototype, it is demonstrated that the proposed approach can make significant\nimprovement over Amazon native automated service provision and scaling options, regardless of scaling up/down or\nin/out.
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