Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

作     者：Freire, Daniela L. Frantz, Rafael Z. Roos-Frantz, Fabricia Basto-Fernandes, Vitor 

作者机构：Inst Univ Lisboa ISCTE IUL ISTAR IUL Lisbon Portugal Unijui Univ Dept Exact Sci & Engn Ijui Brazil 

出 版 物：《JOURNAL OF SUPERCOMPUTING》 (超高速计算杂志)

年 卷 期：2022年第78卷第1期

页      面：1501-1531页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Research Support Foundation of the State of Rio Grande do Sul (FAPERGS) [17/2551-0001206-2] National Council for Scientific and Technological Development (CNPq) [309315/2020-4] 

主　　题：Task scheduling algorithm System integration Application integration Optimization Heuristic 

摘      要：The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.