Queueing model based analysis on flow scheduling in information-agnostic datacenter networks

Abstract

Minimizing the flow completion times (FCTs), especially for the short flows, is widely deemed an important optimization goal in designing data center networks, while flow scheduling strategies play great role in achieving this target. Existing optimal scheduling algorithms severely depend on the prior knowledge of flow size, which is hard to implement in practice. Therefore, it is necessary to estimate the effect of flow scheduling strategies in information-agnostic datacenter networks. In this paper, we simplify the problem of FCTs and propose priority queue based mathematical model to evaluate the performance of different strategies, and derive the expression of FCT. We also present several scheduling strategies for reducing the value of FCT via analysis on the expression. We evaluate and compare these scheduling strategies by numerical and simulation experiments. The results show that the performance can be significantly improved if flow scheduling combines the characteristics of flow size distribution and the principle of providing shortest flow the highest priority.