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We consider the load balancing problem in large-scale heterogeneous systemswith multiple dispatchers. We introduce a general framework calledLocal-Estimation-Driven (LED). Under this framework, each dispatcher keepslocal (possibly outdated) estimates of queue lengths for all the servers, andthe dispatching decision is made purely based on these local estimates. Thelocal estimates are updated via infrequent communications between dispatchersand servers. We derive sufficient conditions for LED policies to achievethroughput optimality and delay optimality in heavy-traffic, respectively.These conditions directly imply delay optimality for many previous local-memorybased policies in heavy traffic. Moreover, the results enable us to design newdelay optimal policies for heterogeneous systems with multiple dispatchers.Finally, the heavy-traffic delay optimality of the LED framework directlyresolves a recent open problem on how to design optimal load balancing schemesusing delayed information.

Asymptotically Optimal Load Balancing in Large-scale Heterogeneous Systems with Multiple Dispatchers