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MapReduce framework is the de facto in big data and its applications where abig data-set is split into small data chunks that are replicated on differentservers among thousands of servers. The heterogeneous server structure of thesystem makes the scheduling much harder than scheduling for systems withhomogeneous servers. Throughput optimality of the system on one hand and delayoptimality on the other hand creates a dilemma for assigning tasks to servers.The JSQ-MaxWeight and Balanced-Pandas algorithms are the states of the artsalgorithms with theoretical guarantees on throughput and delay optimality forsystems with two and three levels of data locality. However, the schedulingcomplexity of these two algorithms are way too much. Hence, we use the power of$d$ choices algorithm combined with the Balanced-Pandas algorithm and theJSQ-MaxWeight algorithm, and compare the complexity of the simple algorithmsand the power of $d$ choices versions of them. We will further show that theBalanced-Pandas algorithm combined with the power of the $d$ choices,Balanced-Pandas-Pod, not only performs better than simple Balanced-Pandas, butalso is less sensitive to the parameter $d$ than the combination of theJSQ-MaxWeight algorithm and the power of the $d$ choices, JSQ-MaxWeight-Pod. Infact in our extensive simulation results, the Balanced-Pandas-Pod algorithm isperforming better than the simple Balanced-Pandas algorithm in low and mediumloads, where data centers are usually performing at, and performs almost thesame as the Balanced-Pandas algorithm at high loads. Note that the loadbalancing complexity of Balanced-Pandas and JSQ-MaxWeight algorithms are$O(M)$, where $M$ is the number of servers in the system which is in the orderof thousands servers, whereas the complexity of Balanced-Pandas-Pod andJSQ-MaxWeight-Pod are $O(1)$, that makes the central scheduler faster and savesenergy.

The Power of d Choices in Scheduling for Data Centers with Heterogeneous Servers