A cost-aware auto-scaling approach using the workload prediction in service clouds
Information Systems Frontiers
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Web applications play a major role in various enterprise and cloud services. With the popularity of social networks and with the speed at which information can be disseminate around the globe, online systems need to face ever growing, unpredictable peak load events. Auto-scaling technique provides on-demand resources according to workload in cloud computing system. However, most of the existing solutions are subject to some of the following constraints: (1) replying on user-provided scaling metrics and threshold values, (2) employing the simple Majority Vote scaling algorithm, which is ineffective for scaling Web applications, and (3) lack of capability for predicting workload changes. In this work, we develop an auto-scaling system, WebScale, which is not subject to the aforementioned constraints, for managing resources for Web applications in data centers. We also compare the efficiency of different scaling algorithms for Web applications, and devise a new method for analyzing the trend of workload changes. The experiment results demonstrate that WebScale can keep the response time of Web applications low even when facing sudden load changing.