Page-Sharing-Based Virtual Machine Packing with Multi-Resource Constraints to Reduce Network Traffic in Migration for Clouds

Virtual machine (VM) packing plays an important role in improving resource utilization in cloud data centers. Recently, memory content similarity among VM instances has been used to speed up multiple VM migration in large clouds. Based on this, many VM packing algorithms have been proposed, which only considered the memory capacity of physical machines (PMs) as the resource constraint. However, in practice the results of such algorithms are not feasible, because thy may not satisfy the constraints of multiple resources (e.g., CPU of the PMs). Besides, the granularities of memory sharing in existing studies are very coarse, and they cannot fully leverage the benefits of memory content similarity which mainly appears at memory page level. In this paper, we study the page-sharing-based VM packing that considers constraints in multiple resources. Given a set of VM instances that share a large number of common memory pages, we pack them into the minimum number of PMs, subject to the constraints in the multiple resources on the PMs. This problem is solved in two steps. First, we pack the maximum number of VMs into a given PM, and then propose an approximation algorithm. The approximation ratio is better than that of the existing algorithm. Then, based on this approximation algorithm, we propose a heuristic algorithm to solve the general problem. Experimental results show that our heuristic algorithm outperforms existing approaches with at most 25% less required PMs and at most 40% less memory page transferring