International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
A REVIEW OF ENERGY-EFFICIENT TASK SCHEDULING TECHNIQUES IN CLOUD COMPUTING ENVIRONMENTS Raj Luxmi Yadav1, Mr. Rajesh Sharma2 1Master of Technology, Computer Science and Engineering, Sagar Institute of Technology and Management,
Barabanki, India
2Assistant Professor, Department of Computer Science and Engineering, Sagar Institute of Technology and
Management, Barabanki, India ---------------------------------------------------------------------***--------------------------------------------------------------------1. INTRODUCTION Abstract - Cloud computing has become a novel technology that can be characterized as a significant technology within the contemporary computing experience that allows flexible, scalable, and on-demand access to computing resources. Nevertheless, the rising use of cloud information centers has increased energy expenses considerably, contributing to the growing business operations and environmental pollution. Task scheduling, as a fundamental part of cloud resource management, can crucially affect the cost based on performance metrics, which include make span, resource utilization, and quality of service (QoS). At the same time, it is energy-conscious. This can be described as a systematic review paper investigating the terrain of energy-efficient task scheduling techniques in cloud computing. The paper entails a detailed examination of task scheduling and strategies that comprise both the traditional heuristic algorithms, such as Min-Min, Max-Min, and met heuristics such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the bio-inspired models. At that, it also analyzes the recent trend towards using artificial intelligence (AI)-based scheduling algorithms, especially reinforcement learning (RL) or deep learning strategies, as the latter shows distinctive flexibility regarding dynamic and heterogeneous cloud environments.
1.1 Background of Cloud Computing Cloud computing has become a paradigm hegemony in distributed computing, changing how organizations and individuals orient to computing resources. Cloud computing promises on-demand access to a shared pool of configurable computer resources via one network of remote servers installed on the internet. The resources can quickly be deployed and released, and with minimal management, users can scale their operations effectively. The various cloud service models, namely Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), have transformed industries by minimizing the necessity to invest vast sums of capital into Information Technology infrastructure. Cloud computing handles practically any application with scalability, flexibility, low cost, business data processing, scientific research, and web hosting applications. Nonetheless, due to the increasing popularity of cloud services, data centers have expanded, which has subsequently caused the emergence of two issues: energy cost and environmental sustainability.
1.2 Significance of Task Scheduling in Cloud Environments
The central issues noted in the review are scalability, workload dynamics, real-time scheduling constraints, and the energyperformance trade-off issue. Furthermore, it brings out the new quest to combine AI-based scheduling with the edge computing paradigms. Although the research on the subject has gone a long way, open research gaps still exist in developing universally scalable, adaptable, and energyefficient scheduling algorithms that can be deployed in largescale cloud settings. This paper, through integrating the results of modern literature, provides the reader with new information about the contemporary approaches to the problem and major obstacles in research, as well as the future areas of investigation that must be developed to improve the sustainable approach to cloud computing.
Task scheduling has become an essential part of cloud computing since it directly impacts resource utilization, system performance, and user satisfaction. It means assigning computational work to available resources so that the goals of the operation, minimization of the response time, maximization of throughput, load balance, and energy optimization are met. Task scheduling is even more problematic in the constantly changing and heterogeneous cloud settings since workloads are unpredictable and resources vary in their capabilities. Effective scheduling ensures that the tasks are completed on time without compromising the much-needed system efficiency. In addition, scheduling tasks is a key characteristic when dealing with the cost-performance trade-off in pay-per-use cloud service frameworks. Lack of proper scheduling plans will make cloud providers underutilize their resources, overload their servers, and raise operational expenditure, undermining the overall service quality.
Key Words: Cloud Computing, Task Scheduling, Energy Efficiency, Heuristic Algorithms, Met heuristic Techniques, Artificial Intelligence, Reinforcement Learning, Green Cloud Computing.
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