Development of heuristic task scheduling algorithm in cloud computing - Diallo, Laouratou; Hassan Abdalla Hashim, Aisha; Olanrewaju, Rashidah Funke (Palestine Ahliya University, 2016)
As the immense growth of data have affected many organizations, there have been a need to adopt the cloud resources for processing big data applications which could cost highly by using traditional storage. In order to optimize these resources used, high level of scheduling algorithms are required. As a result, many scheduling algorithms are proposed by researchers for scheduling big data analytics from static to heuristics algorithms. Therefore, heuristics algorithms are widely proposed recently for their capabilities of resources optimization in a range finite of time. It can be said that heuristics algorithms become the undeniable candidate for task scheduling big data analytics. To this direction, in this paper we make a summary of some scheduling algorithms and propose an Enhanced Greedy Heuristic Scheduling Algorithm (EGHSA) for task scheduling adapted for big data applications. The proposed algorithm is followed by a discussion in order to make smoother the implementation. Index Terms— Big Data Analytics, Cloud Computing, Heuristic, Scheduling, Static.