Abstract
AbstractLarge-scale shared service hosting environments, such as content delivery networks and cloud computing, have gained much popularity in recent years. A key challenge faced by service owners in these environments is to determine the locations where service instances (e.g. virtual machine instances) should be placed such that the hosting cost is minimized while key performance requirements (e.g. response time) are assured. Furthermore, the dynamic nature of service hosting environments favors a distributed and adaptive solution to this problem. In this paper, we present an efficient algorithm for this problem. Our algorithm not only provides a worst-case approximation guarantee, but can also adapt to changes in service demand and infrastructure evolution. The effectiveness of our algorithm is evaluated though realistic simulation studies.KeywordsGreedy AlgorithmLocal Search AlgorithmIEEE INFOCOMService InstanceContent Delivery NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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