Locality-aware process placement for parallel and distributed simulation in cloud data centers

Cloud is a multi-tenant paradigm providing resources as a service. With its easily available computing infrastructure, researchers are adopting cloud for experimental purposes. However, using the platform efficiently for parallel and distributed simulations comes with new challenges. One such challenge is that the simulations comprise logical processes executing on distributed nodes, traditionally, organized in a sequential pattern. This placement strategy leads to delays as frequently communicating processes might get placed farther from one another. In this paper, we proposed a framework to facilitate implementation and evaluation of process placement algorithms inside a three-tier cloud data center. Furthermore, we used the framework to test different process placement strategies based on classical clustering techniques, as well as, our proposed efficient locality-aware placement algorithm. Our evaluation results show a performance gain of 14.5% for the algorithm in comparison with sequential process placement used in practice.