Cost Minimization of Scheduling Scientific Workflow Applications on Clouds
Doctoral Research Foundation of Liaoning. Grant Number: 20170520306, Hybrid Computation and IC Design Analysis Open Fund. Grant Number: HCIC201605, Supercomputing Center of Dalian University of Technology
Concurrency and Computation: Practice and Experience
Workflow scheduling with minimum cost is one of the most challenging problems for the users who need to execute a large‐scale scientific application on a cloud platform. However, traditional methods are hard to cover the highly complex applications and ignore the billing model of the public clouds. In this paper, we address the problem of scheduling a scientific application on cloud platform from the perspective of users. First, we propose a Satisfiability Modulo Theories (SMT) based algorithm to schedule a scientific application on cloud platform, the SMT algorithm constructs the scheduling problem to first‐order logic expressions and checks the expressions by solvers, which minimizes the number of Virtual Machine instances (VMs) allocated to the application. Furthermore, due to the hourly payment of cloud, we develop a heuristic algorithm called Multiple Strategies Algorithm (MSA) which determines the minimum instance hours of a scientific application deployed on VMs. At last, we combine the proposed SMT based algorithm and the MSA to a framework named SMT‐MSA, and compare it with other outstanding algorithms in experiments, the results show that, in most of cases, our algorithms reduce more cost than the other three methods which are HEFT, MSMD and IC‐PCPD2.
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Wu, H, Chen, X, Song, X, Guo, H. Cost minimization of scheduling scientific application on clouds. Concurrency Computat Pract Exper. 2019;e5503.