Task Scheduling Optimization on Enterprise Application Integration Platforms Based on the Meta-heuristic Particle Swarm Optimization

Abstract

Companies seek technological alternatives that provide competitiveness for their business processes. Among these alternatives, there are integration platforms that allow you to connect applications to your software ecosystems. These ecosystems are often composed of local applications and cloud computing services, such as SaaS and PaaS, and still, interact with social media. Integration platforms are specialized software that allows you to design, execute and monitor integration solutions, which connect functionality and data from different applications. Integration platforms typically provide a specific domain language, development toolkit, runtime engine, and monitoring tool. The efficiency of the engine in scheduling and performing integration tasks has a direct impact on the performance of a solution and this is one of the challenges faced by integration platforms. Our literature review has identified that integration engines adopt task scheduling algorithms based on the textit First-In-First-Out discipline, which may be inefficient. Therefore, it is appropriate to seek a task scheduling algorithm that optimizes engine performance, providing a positive impact on the performance of the integration solution in different scenarios. This article proposes an algorithm for task scheduling based on the meta-heuristic optimization technique, which assigns the tasks to the computational resources, considering the waiting time in the queue of ready tasks and the computational complexity of Each task in order to optimize the performance of the integration solution.