Rethinking and Optimizing Workload Redistribution in Large-scale Internet Data Centers

Heuristic-based workload redistribution is the most commonly adopted solution to provide enhanced service performance in large-scale Internet Data Centers (IDCs). However, statistics show that they cannot perform as well as expected in real-world IDCs. In this paper, we rethink existing solutions based on real-world trace data and pinpoint two major pitfalls: (i) Sensitive to hand-tuning parameters; (ii) Reassigning only a few workloads locally at a time. The two of them jointly limit the universal applicability of existing solutions in optimizing multiple objectives fairly. To address such issues, we propose the matching-theory-based solution for workload redistribution, namely Themis. It is an efficient and universal solution for large-scale IDCs, which can avoid empirical parameters in optimization and reassign several workloads globally each time. Moreover, the newly proposed Themis can optimize multiple objectives (e.g., resource utilization balancing and communication efficiency improving) simultaneously and fairly. In addition to its own performance advantages, our proposed Themis is also compatible with existing methods, thus adapting to a wider range of deployment scenarios. Extensive evaluations based on the trace data from two real-world IDCs demonstrate that our proposed Themis outperforms multiple comparison solutions, as well as the compatibility of parameter changes (i.e., stability properties in terms of parameter configuration).