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Can I read Reinforcement learning based energy efficient resource allocation strategy of MapReduce jobs with deadline constraint on EtoBox?
Reinforcement learning based energy efficient resource allocation strategy of MapReduce jobs with deadline constraint by Greeshma Lingam is a Computer Science article available to read on EtoBox.
What is Reinforcement learning based energy efficient resource allocation strategy of MapReduce jobs with deadline constraint about?
Big Data applications require more energy consumption to process a massive volume of data in a heterogeneous environment. Moreover, reducing energy consumption in Big Data applications is an important research topic. It is one of the challenging issues to conserve energy with a deadline constraint in a heterogeneous environment. In this paper, we formulate scheduling the MapReduce jobs as a minimization problem by considering the decision variables with a userspecified deadline constraint. Further, a Learning Automata-based MapReduce Scheduling (LA-MRS) algorithm has been proposed to identify the resource allocation and save energy consumption of MapReduce tasks in a heterogeneous environment. We perform experimentation on the proposed LA-MRS algorithm using Hibench benchmark workloads such as Enhanced DFSIO, Nutch Indexing, k-mean Clustering and Hive Join. The experimentation illustrates that the proposed LA-MRS algorithm schedules the MapReduce task by saving around 25% of less energy consumed when compared to the existing algorithms.
Who reads Reinforcement learning based energy efficient resource allocation strategy of MapReduce jobs with deadline constraint?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Greeshma Lingam
- Publisher
- Springer Science and Business Media LLC
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)