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

Abstract

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.

Design of Big Data Task Scheduling Optimization Algorithm Based on Improved Deep Q-Network

“…Then, a phased approach was proposed to decouple the original scheduling problem. From the experimental results, the proposed model had good performance in solving scheduling problems [5].…”

Research on a multilevel plan management model based on the pyramid plan management structure

“…Li [6] combines work modes and task deadlines for comprehensive priority planning, enabling radar to allocate time reasonably during the tracking phase based on differences in target threat levels and threat areas. Freire [7] proposed a queue priority algorithm that uses heuristic methods to process large amounts of data in task scheduling of integrated processes. Gao [8] proposed a dynamic dual optimal first level scheduling algorithm to address the problem of stall in traditional algorithms and complex task sets, and proposed their respective dynamic priority calculation strategies for hard real-time and soft real-time tasks.…”

Iot Data Processing and Scheduling Based on Deep Reinforcement Learning

“…In the system architecture, the iot data access layer uses distributed iot access components to directly interface with the data stream reported by iot terminal devices, and supports forwarding with various distributed messaging middleware. in different iot scenarios [17,18]. The structure diagram of iot data access layer functions is shown in Figure 2.…”

“…The data forwarding cache layer mainly deploys distributed message-oriented middleware Kafka and ZooKeeper clusters to provide caching services for data forwarded by the data access layer of the Internet of Things, realizing the decoupling of the original data access of the Internet of Things and the big data computing engine, and improving the stability of the platform system. The edge stream computing data processing platform can provide a distributed real-time data access layer of the Internet of Things and provide load balancing access services for data processing applications in different iot scenarios [17][18]. The structure diagram of iot data access layer functions is shown in Figure 2.…”