Communication-aware and energy-efficient scheduling for parallel applications in virtualized data centers

In this paper, we propose Peer VMs Aggregation (PVA) to enable dynamic discovery of communication patterns and reschedule VMs based on the determined communication patterns using VM migration. In the implementation, we consider that communication delays occur at the server (i.e., memory-bus) and at the data center network. To evaluate our approach, we modeled a network and a memory subsystem on CloudSim simulator. We then used NAS Parallel Benchmarks, which consists of six different applications as parallel applications. We thoroughly evaluated our proposed approach measuring several assessment metrics including VMs placement, performance degradation, and network utilization of each link. The results of the simulation show that our proposed approach significantly reduces the total amount of traffic in the network where it reduces the average of the network's utilization by 25%.