International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
A Review of Cost-Effective Resource Management in Cloud Computing using AIBased Forecasting Mohammad Shahbaz1, Deepshikha2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - For modern computing, the cloud computing is
unpredictable and there is a tradeoff between cost and performance.
imperative, it provides scalable and on demand availability of the resources. One such challenge is resource management for efficient workloads that fluctuate, demand that is unpredictable and costs bounded. The problem with traditional allocation methods is that they can over provision or under provision and that comes at a higher cost or poor performance. However, with the rise in the race for faster deployment, AI based forecasting has significantly proven itself as a viable option in solving the issue of optimizing resource utilization by precisely forecasting future workload demands. This review studies the role of AI driven forecasting in cost efficient cloud resource management and discusses AI methods such as Machine Learning (ML), Deep Learning (DL) as well as Reinforcement Learning (RL). The strategies that this explores to save costs are: predictive scaling, intelligent load balancing, and optimal pricing models. Challenges such as model accuracy, data privacy, and integration with increasingly popular tech such as edge computing and IoT are also reviewed in the review. Through a discussion of recent progress and case studies, it provides an example of how AI forecasting could help improve cloud efficiency, sustainability and scalability.
1.2.IMPORTANCE OF COST-EFFECTIVE RESOURCE MANAGEMENT Cost Structure: Cloud services offers its own pay as you go pricing model and User has to pay on resource consumption basis. Such inefficient resource management can lead to over provisioning, where extra resources than what is required are allocated and this leads to additional operational cost, over provisioning can also lead to a poor user experience, because there are not enough resources to suffice the requirements and this results in poor service performance, and finally, idle resource, where resources are not being used or being used inefficient. Cost effective resource management aims to allocate the computational resources to the actual demand at least cost. This approach incorporates dynamic scaling, workload prediction, and cost awareness scheduling to use the resources properly.
1.3.ROLE OF OPTIMIZATION
1. INTRODUCTION 1.1. Background on Cloud Computing The access to computing resources has been revolutionized by cloud computing, which gives scalable, on demand, services over the cloud, which is the internet, without the requirement to own any additional costly hardware. Deploying applications, storages and computing power is given flexibility. Infrastructure as a Service (IaaS), such as Amazon EC2, Microsoft Azure VMs; Platform as a Service (PaaS), like development environment, Google App Engine, AWS Elastic Beanstalk; software as a Service (SaaS) are the main models of this category. Nevertheless, managing the resources still remains a challenge, as workloads fluctuate, demand is
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Impact Factor value: 8.315
IN
FORECASTING
AND
Techniques such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL) among others improve Artificial Intelligence (AI) to boost cloud resource management. Preproactive scaling powered by AI driven models and reduce wastage of power. Serves auto scaling, dynamic resource adjustment, as well as load balancing to gain improved performance. Also, AI enables cost optimization by advice on best to go pricing strategy, comparable to spot instances and cast plans. Through integration of AI, organizations overcome costs, better resource allocation, and performance of cloud.
Key Words: Cloud Computing, Resource Management, AIBased Forecasting, Cost Optimization, Machine Learning, Predictive Scaling, Load Balancing.
© 2025, IRJET
AI
1.4.OBJECTIVE OF THE REVIEW PAPER This review paper aims to give a comprehensive analysis on AI based cost effective forecasting techniques in cloud computing. The paper aims to:
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Examine the problems that arise in the context of traditional cloud resource management.
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