title,doi,url,abstract,journal,publication_year,pmid,arxiv
Hybrid Job-Driven Scheduling for Heterogeneous MapReduce Clusters, https://doi.org/10.32628/IJSRCSEIT, https://ijsrcseit.com/CSEIT172566, It is cost-efficient for a tenant with a limited budget to establish a heterogeneous virtual MapReduce clusters by renting various virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment and MapReduce still performs poorly on heterogeneous clusters we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant perspective. JoSS provide not only job level scheduling but also Map-task level scheduling and Reduce-task level scheduling; The deployment of MapReduce in data canters and clouds present several challenges improve data locality for both map-level task and reduce-level task avoid job starvation and improve job execution performance. Two variations of JoSS-Task and JoSS-Job are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations (JoSS-T and JoSS-J) with current scheduling algorithms supported by Hadoop. The result shows that the two variations crush the opposite tested algorithms in terms of map and reduce data locality  and network overhead while not acquisition significant overhead. Additionally the two variations area unit severally appropriate for various MapReduce-workload eventualities and supply the most effective job performance among all tested algorithms., International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2017, CSEIT172566