Improving map reduce task scheduling and micro-partitioning mechanism for mobile cloud multimedia services. (2016)

• Record Type: Journal Article
• Authors: Saravanan, S.; Venkatachalam, V.
• Is Part Of: International journal of advanced intelligence paradigms. Volume 8:Number 2(2016)
• Page Start: 156
• Page End: 167
• Publication Date: 2016
• Subjects: MTSD -- MapReduce -- workload distribution -- task scheduling -- micro-partitioning -- mobile services -- m-services -- cloud computing -- multimedia services -- cloud services -- mobile cloud -- load balancing -- memory consumption -- deadline constraints; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

Abstract

Over the several decades, there is a massive improvement in the computer technology which leads to infinite number of resources in all over the world. Many computing devices have to generate data that comes from various domains. In order to reduce the time complexity and the storage space of data, a novel technique namely map reduce programming model has been proposed to divide the workload among computers in a network to enhance the performance. To rectify the challenging issue of uneven data distribution, and also to enhance the process of load balancing along with memory consumption of computer, data sampling is highly preferred. To enhance the accuracy in scheduling, an innovative method called map reduce task scheduling algorithm is proposed for job deadline constraints. This algorithm classifies the nodes into several levels in heterogeneous clusters. Under this algorithm, a novel data distribution model has been elucidated in which it distributes data according to the node's capacity level respectively.