Modern data centers face new scheduling challenges in optimizing job-level
performance objectives, where a significant challenge is the scheduling of
highly parallel data flows with a common performance goal (e.g., the shuffle
operations in MapReduce applications). Chowdhury and Stoica introduced the
coflow abstraction to capture these parallel communication patterns, and
Chowdhury et al. proposed effective heuristics to schedule coflows efficiently.
In our previous paper, we considered the strongly NP-hard problem of minimizing
the total weighted completion time of coflows with release dates, and developed
the first polynomial-time scheduling algorithms with O(1)-approximation ratios.
In this paper, we carry out a comprehensive experimental analysis on a
Facebook trace and extensive simulated instances to evaluate the practical
performance of several algorithms for coflow scheduling, including the
approximation algorithms developed in our previous paper. Our experiments
suggest that simple algorithms provide effective approximations of the optimal,
and that the performance of our approximation algorithms is relatively robust,
near optimal, and always among the best compared with the other algorithms, in
both the offline and online settings.Comment: 29 pages, 8 figures, 11 table