Evaluating map reduce tasks scheduling algorithms over cloud computing infrastructure

摘要

Efficiently scheduling MapReduce tasks is considered as one of the major challenges that face MapReducernframeworks. Many algorithms were introduced to tackle this issue. Most of these algorithms are focusing onrnthe data locality property for tasks scheduling. The data locality may cause less physical resources utilizationrnin non-virtualized clusters and more power consumption. Virtualized clusters provide a viable solutionrnto support both data locality and better cluster resources utilization. In this paper, we evaluate the majorrnMapReduce scheduling algorithms such as FIFO, Matchmaking, Delay, and multithreading locality (MTL)rnon virtualized infrastructure. Two major factors are used to test the evaluated algorithms: the simulation timernand the energy consumption. The evaluated schedulers are compared, and the results show the superiorityrnand the preference of the MTL scheduler over the other existing schedulers. Also, we present a comparisonrnstudy between virtualized and non-virtualized clusters for MapReduce tasks scheduling.