Intelligent Task Scheduling and Load Balancing: A Comprehensive Review of Techniques, Challenges, and Emerging Trends

Abstract

Authors: Aarav Kulkarni, Meenakshi Sehrawat, Prashant N. Pandey

Abstract: The rapid growth of distributed computing environments such as cloud computing, edge computing, and large-scale data centers has significantly increased the complexity of managing computational resources efficiently. Intelligent task scheduling and load balancing play a vital role in improving system performance, reducing execution time, minimizing energy consumption, and ensuring quality of service. Traditional scheduling and load balancing techniques, though effective in static environments, fail to adapt efficiently to dynamic and heterogeneous systems. As a result, intelligent approaches incorporating machine learning, heuristic optimization, and adaptive algorithms have gained increasing attention in recent years. This paper presents a comprehensive review of intelligent task scheduling and load balancing techniques, covering classical methods, heuristic and metaheuristic algorithms, and learning-based strategies. The paper also discusses key performance metrics, practical challenges, and emerging research trends in modern computing environments. Finally, open research issues and future directions are highlighted to guide further advancements in this field.

Keywords: Task Scheduling, Load Balancing, Intelligent Algorithms, Cloud Computing, Distributed Systems, Machine Learning