Workload Alleviation Scheduling Framework to Alleviate Negative Performance Impact of Intermediate Data Skew in Small-Scale MapReduce Cloud

A MapReduce cloud becomes the essential platform in the cloud computing infrastructure today. Because applications may process input data with different algorithms and logics to produce intermediate data, a MapReduce cloud may suffer intermediate data skew by unevenly distributing intermediate data among nodes at run time. When intermediate data skew happens, a MapReduce cloud not only idles nodes to waste computation resources but also prolongs the application execution progress to hurt user experiences in cloud computing. Instead of the existing solutions that assume many available idle nodes and use computation resources in a loose way, a MapReduce cloud can use the Workload Alleviation Scheduling Framework (W ASF) proposed in this paper to alleviate the negative performance impact of intermediate data skew in a small-scale MapReduce cloud by smartly utilizing computation resources. Besides, a MapReduce cloud is verified with popular applications in experiments to have the outstanding performance improvement with W ASF when intermediate data skew happens.