Carving Software-Defined Networks for Scientific Applications with SpateN

Abstract

Scientific applications (SciApps) are broadly used in all science domains. For more accurate results, they have been increasingly demanding computational power and extremely agile networks. These applications are usually implemented using numerical methods presenting well-behaved patterns to exchange data across its computing nodes. This paper presents SpateN, a tool that exploits the spatial communication patterns of SciApps as the fundamental logic to drive the network programming. SpateN classifies the SciApps nodes communications and balances the elephant flows across the available network paths. As a proof of concept, we carried out a set of experiments in real testbeds, demonstrating that network programming may affect the performance of SciApps significantly. Also, a balanced flow allocation can speed up SciApps to near-optimal execution times.