Performance Analysis of Randomized Data Fetching in Cluster Computing

The shuffle transfer pattern is widely adopted in today’s cluster computing applications and the completion time of each group of transmissions directly affects application performance. Because of the restriction on the number of concurrent threads and the TCP Incast problem, the randomized data fetching strategy is widely employed in this kind of communication in practice. In this paper, to assess the performance of randomized data fetching, we build a general analytical model and define two metrics – link overload probability and K-deviation load balancing probability – to evaluate the degree of link overload and load balancing respectively, since they are closely related to the transfer completion time. Leveraging our model, we theoretically analyze the transfer performance in three typical scenarios and provide recommendations for setting the number of concurrent connections per receiver. Finally, we validate the theoretical analysis as well as the recommendations through extensive simulations.