Predictive ML Model for Workload-Aware Resource Allocation in Cloud
Neelesh Shrivastava
, Pramod Singh , Akhilesh A. Waoo
Auto-Scaling, Cloud Computing, Energy-Efficient Cloud, Machine Learning, Resource Allocation, Service Level Agreement (SLA), Workload Prediction.
The rapid growth of cloud computing has led to highly dynamic and heterogeneous workloads, making efficient resource allocation a critical challenge for cloud service providers. Traditional reactive and static resource management strategies often fail to cope with workload variability, resulting in resource underutilization, performance degradation, Service Level Agreement (SLA) violations, and increased energy consumption. This review paper presents a comprehensive analysis of predictive machine learning models for workload-aware resource allocation in cloud platforms. It systematically examines existing workload prediction techniques, including statistical models, classical machine learning approaches, and deep learning architectures, highlighting their strengths and limitations in forecasting resource demand across multiple dimensions such as CPU, memory, storage, and network bandwidth. Furthermore, the paper reviews workload-aware resource allocation strategies that integrate predictive insights into provisioning, scheduling, virtual machine placement, and auto-scaling mechanisms. Key challenges such as data heterogeneity, model adaptability, scalability, prediction accuracy, and real-time deployment are discussed in detail. Finally, the paper identifies open research issues and future directions, emphasizing the need for adaptive, energy-efficient, and intelligent resource management frameworks that can support next-generation cloud and smart infrastructure applications
"Predictive ML Model for Workload-Aware Resource Allocation in Cloud", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a836-a846, January-2026, Available :https://ijsdr.org/papers/IJSDR2601090.pdf
Volume 11
Issue 1,
January-2026
Pages : a836-a846
Paper Reg. ID: IJSDR_306935
Published Paper Id: IJSDR2601090
Downloads: 000239
Research Area: Science and Technology
Country: SATNA, Madhya Praadesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
