Improving scheduling performance using a q-learning-based leasing policy for clouds
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
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Application server clusters are often used to service high-throughput web applications. In order to host more than a single application, an organisation will usually procure a separate cluster for each application. Over time the utilisation of the clusters will vary, leading to variation in the response times experienced by users of the applications. Techniques that statically assign servers to each application prevent the system from adapting to changes in the workload, and are thus susceptible to providing unacceptable levels of service. This paper investigates a system for allocating server resources to applications dynamically, thus allowing applications to automatically adapt to variable workloads. Such a scheme requires meticulous system monitoring, a method for switching application servers between \text it {server pools} and a means of calculating when a server switch should be made (balancing switching cost against perceived benefits). Experimentation is performed using such a switching system on a Web application testbed hosting two applications across eight application servers. The testbed is used to compare several theoretically derived switching policies under a variety of workloads. Recommendations are made as to the suitability of different policies under different workload conditions.