A Time-Efficient Solution to the General Resource Placement Problem in Cloud

Abstract

Cloud based large-scale online services are faced with regionally distributed stochastic demands for various resources. With multiple regional cloud data centers, a crucial problem that needs to be settled is how to properly place resources to satisfy massive stochastic demands from many different regions. For the general stochastic demands oriented cross region resource placement problem, the time complexity of existing optimal algorithm is linear to total amount of resources and thus may be inefficient when dealing with a large number of resources. To end this, we propose an efficient algorithm, named discrete function based unbound resource placement (D-URP). Experiments show that in scenarios with general settings, D-URP can averagely achieve at least 97% revenue of optimal solution, with reducing time by three orders of magnitude. Moreover, due to the generality of problem setting, it can be extended to get efficient solution for a broad range of similar problems under various scenarios with different constraints. Therefore, D-URP can be used as an effective supplement to existing algorithm under time-tense scheduling scenarios with large number of resources.