Hadoop Job Scheduling Using Improvised Ant Colony Optimization
Main Article Content
Abstract
Hadoop Distributed File System is used for storage along with a programming framework MapReduce for processing large datasets allowing parallel processing. The process of handling such complex and vast data and maintaining the performance parameters up to certain level is a difficult task. Hence, an improvised mechanism is proposed here that will enhance the job scheduling capabilities of Hadoop and optimize allocation and utilization of resources. Significantly, an aggregator node is added to the default HDFS framework architecture to improve the performance of Hadoop Name node. In this paper, four entities viz., the name node, secondary name node, aggregator nodes, and data nodes have been modified. Here, the aggregator node assigns jobs to data node, while Name node tracks aggregator nodes. Also, based on the job size and expected execution time, an improvised ant colony optimization method is developed for scheduling jobs.In the end, the results demonstrate notable improvisation over native Hadoop and other approaches.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.