Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/4614
Title: Energy-aware task scheduling based on the DAG applications using Genetic Algorithm in Cloud computing
Authors: Khushleen
Supervisor: Kumar, Ravinder
Keywords: MCC;DAG;DVFS;EFE
Issue Date: 9-Aug-2017
Abstract: Cloud computing has offered services related to utility aligned IT services. Reducing the schedule length is considered as one of the significant QoS need of the cloud provider for the satisfaction of budget constraints of an application. Task scheduling in a parallel environment is one of the NP problems, which deals with the optimal assignment of a task. To deal with the favorable assignment of some task, task scheduling is considered as one of the NP problem. In this research, the enhancement of DAG (Directed Acyclic Graph) algorithm has been considered for less energy consumption, more efficiency and less makespan and cost. For the optimization of the traditional scheduling and balancing algorithm, an algorithm has been designed for reducing the delay. For this, DVFS (dynamic voltage and frequency scaling) mode is applied that permits the devices for performing the required tasks with the less amount of required power. It also scale upwards for increasing the performance. The job placement also has a great impact on the cost computation. Here, the placement is done by using Optimization algorithm that is genetic algorithm for generating high-quality solutions for optimizing and searching the problems by depending on bio-inspired operator, namely mutation, crossover and selection. Metrics namely, make span, CCR(Computation Cost Ratio) and Energy consumption are used for the evaluation of the proposed work.
URI: http://hdl.handle.net/10266/4614
Appears in Collections:Masters Theses@CSED

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