  MapReduce framework is the de facto standard in Hadoop. Considering the data
locality in data centers, the load balancing problem of map tasks is a special
case of affinity scheduling problem. There is a huge body of work on affinity
scheduling, proposing heuristic algorithms which try to increase data locality
in data centers like Delay Scheduling and Quincy. However, not enough attention
has been put on theoretical guarantees on throughput and delay optimality of
such algorithms. In this work, we present and compare different algorithms and
discuss their shortcoming and strengths. To the best of our knowledge, most
data centers are using static load balancing algorithms which are not efficient
in any ways and results in wasting the resources and causing unnecessary delays
for users.
