Dynamic a nity scheduling has been an open problem for nearly three decades. The problem is to dynamically schedule multi-type tasks to multi-skilled servers such that the resulting queueing system is both stable in the capacity region (throughput optimality) and the mean delay of tasks is minimized at high loads near the boundary of the capacity region (heavy-tra c optimality). As for applications, dataintensive analytics like MapReduce, Hadoop, and Dryad fit into this setting, where the set of servers is heterogeneous for di↵erent task types, so the pair of task type and server determines the processing rate of the task. The load balancing algorithm used in such frameworks is an example of a nity scheduling which is desired to be both robust and delay optimal at high loads when hot-spots occur. Fluid model planning, the MaxWeight algorithm, and the generalized cµ-rule are among the first algorithms proposed for a nity scheduling that have theoretical guarantees on being optimal in di↵erent senses, which will be discussed in the related work section. All these algorithms are not practical for use in data center applications because of their non-realistic assumptions. The join-the-shortest-queue-MaxWeight (JSQ-MaxWeight), JSQ-Priority, and weighted-workload algorithms are examples of load balancing policies for systems with two and three levels of data locality with a rack structure. In this work, we propose the Generalized-Balanced-Pandas algorithm (GB-PANDAS) for a system with multiple levels of data locality and prove its throughput optimality. We prove this result under an arbitrary distribution for service times, whereas most previous theoretical work assumes geometric distribution for service times. The extensive simulation results show that the GB-PANDAS algorithm alleviates the mean delay and has a better performance than the JSQ-MaxWeight algorithm by up to twofold at high loads. We believe that the GB-PANDAS algorithm is heavy-tra c optimal in a larger region than JSQ-MaxWeight, which is an interesting problem for future work.
This model is generalized in REF to more levels of data locality.
GB-PANDAS:: Throughput and heavy-traffic optimality analysis for affinity scheduling
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Computer Science