Understanding Spark Performance in Hybrid and Multi-Site Clouds

International audienceRecently, hybrid multi-site big data analytics (that combines on-premise with off-premise resources) has gained increasing popularity as a tool to process large amounts of data on-demand, without additional capital investment to increase the size of a single datacenter. However, making the most out of hybrid setups for big data analytics is challenging because on-premise resources can communicate with off-premise resources at significantly lower throughput and higher latency. Understanding the impact of this aspect is not trivial, especially in the context of modern big data an-alytics frameworks that introduce complex communication patterns and are optimized to overlap communication with computation in order to hide data transfer latencies. This paper contributes with a work-in-progress study that aims to identify and explain this impact in relationship to the known behavior on a single cloud. To this end, it analyses a representative big data workload on a hybrid Spark setup. Unlike previous experience that emphasized low end-impact of network communications in Spark, we found significant overhead in the shuffle phase when the bandwidth between the on-premise and off-premise resources is sufficiently small