Joint Optimization of Memory Sharing and Communication Distance for Virtual Machine Instantiation in Cloudlet Networks

Abstract

Cloudlet networks are an emerging distributed data processing paradigm, which contain multiple cloudlets deployed beside base stations to serve local user devices (UDs). Each cloudlet is a small data center with limited memory, in which multiple virtual machines (VMs) can be instantiated. Each VM runs a UD’s application components and provides dedicated services for that UD. The number of VMs that serve UDs with low latency is limited by a lack of sufficient memory of cloudlets. Memory deduplication technology is expected to solve this problem by sharing memory pages between VMs. However, maximizing page sharing means that more VMs that can share the same memory pages should be instantiated on the same cloudlet, which prevents the communication distance between UDs and their VMs from minimizing, as each VM cannot be instantiated in the cloudlet with the shortest communication distance from its UD. In this paper, we study the problem of VM instantiation with the joint optimization of memory sharing and communication distance in cloudlet networks. First, we formulate this problem as a bi-objective optimization model. Then, we propose an iterative heuristic algorithm based on the ε-constraint method, which decomposes original problems into several single-objective optimization subproblems and iteratively obtains the subproblems’ optimal solutions. Finally, the proposed algorithm is evaluated through a large number of experiments on the Google cluster workload tracking dataset and the Shanghai Telecom base station dataset. Experimental results show that the proposed algorithm outperforms other benchmark algorithms. Overall, the memory sharing between VMs increased by 3.6%, the average communication distance between VMs and UDs was reduced by 22.7%, and the running time decreased by approximately 29.7% compared to the weighted sum method.