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We consider robust resource allocation of services in Clouds. Morespecifically, we consider the case of a large public or private Cloud platformthat runs a relatively small set of large and independent services. Theseservices are characterized by their demand along several dimensions (CPU,memory,...) and by their quality of service requirements, that have beendefined through an SLA in the case of a public Cloud or fixed by theadministrator in the case of a private Cloud. This quality of service definesthe required robustness of the service, by setting an upper limit on theprobability that the provider fails to allocate the required quantity ofresources. This maximum probability of failure can be transparently turned intoa pair (price,penalty). Failures can indeed hit the platform, and resilience isprovided through service replication. Our contribution is two-fold. First, wepropose a resource allocation strategy whose complexity is logarithmic in thenumber of resources, what makes it very efficient for large platforms. Second,we propose an efficient algorithm based on rare events detection techniques inorder to estimate the robustness of an allocation, a problem that has beenproven to be P-complete. Finally, we provide an analysis of the proposedstrategy through an extensive set of simulations, both in terms of the overallnumber of allocated resources and in terms of time necessary to compute theallocation.

Efficient and Robust Allocation Algorithms in Clouds under Memory Constraints