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Published January 2021 | public
Journal Article Open

Asymptotically optimal load balancing in large-scale heterogeneous systems with multiple dispatchers


We consider the load balancing problem in large-scale heterogeneous systems with multiple dispatchers. We introduce a general framework called Local-Estimation-Driven (LED). Under this framework, each dispatcher keeps local (possibly outdated) estimates of the queue lengths for all the servers, and the dispatching decision is made purely based on these local estimates. The local estimates are updated via infrequent communications between dispatchers and servers. We derive sufficient conditions for LED policies to achieve throughput optimality and delay optimality in heavy-traffic, respectively. These conditions directly imply delay optimality for many previous local-memory based policies in heavy traffic. Moreover, the results enable us to design new delay optimal policies for heterogeneous systems with multiple dispatchers. Finally, the heavy-traffic delay optimality of the LED framework also sheds light on a recent open question on how to design optimal load balancing schemes using delayed information.

Additional Information

© 2020 Elsevier B.V. Received 24 September 2020, Accepted 25 September 2020, Available online 8 October 2020. This project has been funded in part through NSF, USA grants: CNS-2007231, CNS-1719371, and CNS-1717060 and NSF, USA grants AitF-1637598 and CNS-1518941. Declaration of Competing Interest: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: S. Theja Maguluri, Georgia Institute of Technology, Atlanta, Georgia, United States C.H. Xia, OHIO STATE UNIVERSITY, Columbus, Ohio, United States.

Attached Files

Supplemental Material - 1-s2.0-S0166531620300663-mmc1.pdf

Submitted - 2002.08908.pdf


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August 22, 2023
August 22, 2023