TY - GEN
T1 - Rethinking and Optimizing Workload Redistribution in Large-scale Internet Data Centers
AU - Zhao, Yi
AU - Lv, Liang
AU - Li, Yusen
AU - Shen, Meng
AU - Zhu, Liehuang
AU - Xu, Ke
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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).
AB - 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).
KW - Communication Efficiency
KW - Matching Theory
KW - Resource Utilization
KW - Workload Redistribution
UR - http://www.scopus.com/inward/record.url?scp=85206377026&partnerID=8YFLogxK
U2 - 10.1109/IWQoS61813.2024.10682613
DO - 10.1109/IWQoS61813.2024.10682613
M3 - Conference contribution
AN - SCOPUS:85206377026
T3 - IEEE International Workshop on Quality of Service, IWQoS
BT - 2024 IEEE/ACM 32nd International Symposium on Quality of Service, IWQoS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE/ACM International Symposium on Quality of Service, IWQoS 2024
Y2 - 19 June 2024 through 21 June 2024
ER -