NACER: A Network-Aware Cost-Efficient Resource Allocation Method for Processing-Intensive Tasks in Distributed Clouds

Abstract

In the distributed cloud paradigm, data centers are geographically dispersed and interconnected over a wide-area network. Due to the geographical distribution of data centers, communication networks play an important role in distributed clouds in terms of communication cost and QoS. Large-scale, processing-intensive tasks require the cooperation of many VMs, which may be distributed in more than one data center and should communicate with each other. In this setting, the number of data enters serving the given task and the network distance among those data centers have critical impact on the communication cost, traffic and even completion time of the task. In this paper, we present the NACER algorithm, a Network-Aware Cost-Efficient Resource allocation method for optimizing the placement of largemulti-VM tasks in distributed clouds. NACER builds on ideas of the A* search algorithm from Artificial Intelligence research in order to obtain better results than typical greedy heuristics. We present extensive simulation results to compare the performance of NACER with competing heuristics and show its effectiveness.