Research Area:  Cloud Computing

Abstract:

Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields. However, there are very few cloud resource provisioning and scheduling algorithms tailored for BoT workflows. Furthermore, existing algorithms fail to consider the stochastic task execution times of BoT workflows which leads to deadline violations and increased resource renting costs. In this paper, we propose a dynamic cloud resource provisioning and scheduling algorithm which aims to fulfill the workflow deadline by using the sum of task execution time expectation and standard deviation to estimate real task execution times. A bag-based delay scheduling strategy and a single-type based virtual machine interval renting method are presented to decrease the resource renting cost. The proposed algorithm is evaluated using a cloud simulator ElasticSim which is extended from CloudSim. The results show that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to the existing algorithms.

Keywords:  

Author(s) Name:  Zhicheng Cai,Xiaoping Li,Rubén Ruiz,Qianmu Li

Publisher name:  Elsevier

DOI:  10.1016/j.future.2017.01.020

Volume Information:  Volume 71, June 2017, Pages 57-72

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0167739X17300870