MapReduce Scheduling for Deadline-Constrained Jobs in Heterogeneous Cloud Computing Systems - 2018


MapReduce is a software framework for processing knowledge-intensive applications with a parallel manner in cloud computing systems. Some MapReduce jobs have the deadline necessities for his or her job execution. The existing deadline-constrained MapReduce scheduling schemes don't think about the subsequent 2 problems: numerous node performance and dynamical task execution time. During this Project, we tend to utilize the Bipartite Graph modelling to propose a replacement MapReduce Scheduler called the BGMRS. The BGMRS can get the optimal solution of the deadline-constrained scheduling problem by reworking the matter into a well known graph problem: minimum weighted bipartite matching. The BGMRS has the following features. It considers the heterogeneous cloud computing surroundings, such that the computing resources of some nodes cannot meet the deadlines of some jobs. Additionally to meeting the deadline requirement, the BGMRS additionally takes the info locality into the computing resource allocation for shortening the info access time of a job. But, if the overall accessible computing resources of the system cannot satisfy the deadline requirements of all jobs, the BGMRS will minimize the number of jobs with the deadline violation. Finally, both simulation and testbed experiments are performed to demonstrate the effectiveness of the BGMRS in the deadline-constrained scheduling.

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