Allocating the right amount of resources to each service in any of the data centers in a cloud environment is a very difficult task. This task becomes much harder due to the dynamic nature of the workload and the fact that while long term statistics about the demand may be known, it is impossible to predict the exact demand in each point in time. As a result, service providers either over allocate resources and hurt the service cost efficiency, or run into situation where the allocated local resources are insufficient to support the current demand. In these cases, the service providers deploy overflow mechanisms such as redirecting traffic to a remote data center or temporarily leasing additional resources (at a higher price) from the cloud infrastructure owner. The additional cost is in many cases proportional to the amount of overflow demand.
In this paper we propose a stochastic based placement algorithm to find a solution that minimizes the expected total cost of ownership in case of two data centers. Stochastic combinatorial optimization was studied in several different scenarios. In this paper we extend and generalize two seemingly different lines of work and arrive at a general approximation algorithm for stochastic service placement that works well for a very large family of overflow cost functions. In addition to the theoretical study and the rigorous correctness proof, we also show using simulation based on real data that the approximation algorithm performs very well on realistic service workloads.
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Titel
Risk Aware Stochastic Placement of Cloud Services: The Case of Two Data Centers
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