Agerl Based Enhanced Map Reduce Technique in Cloud Scheduling

Abstract

Today's real time big data applications mostly rely on map-reduce (M-R) framework of Hadoop File System (HDFS). Hadoop makes the complexity of such applications in a simpler manner. This paper works on two goals: maximizing resource utilization and reducing the overall job completion time. Based on the goals proposed, we have developed Agent Centric Enhanced Reinforcement Learning Algorithm (AGERL) .The algorithm concentrates in four dimensions: variable partitioning of tasks, calculation of progress ratio of processing tasks including delays, XMPP based multi attribute query posting and Hopkins statistics assessment based dynamic cluster restructuring. An Enhanced Reinforcement Learning Process with the above features is employed to achieve the proposed goal. Finally performance gain is theoretically proved.

A Research on an Efficient Cloud Scheduling with a Geo Microarray Data Set

“…From the log, the holes should be mapped with the sub ensembles, at the same time deadline constraints are calculated, if it is satisfied send to queue for further processing and updating should be performed continuously else the ensembles need to be rejected. After estimating the budget and rejection [8] of the ensembles, the distance between the workflow present in the queue has been calculated for any two nodes with the help of the Manhattan distance(U m ) for a node which is computed as follows [14], = Manhautten Distance Manhautten Distance…”

“…After that the appropriated resources are provided to the genomic data, by forming the clusters. During the cluster estimation process [14], neighboring genomic information is gathered for reducing the dissimilarity information processing that indirectly enhances the processing time. The similar gene information is clustered which is then allocated to the virtual machine by implementing HFEL algorithm [15].…”