Efficient and Scalable Resource Management in Cloud-Based IoT Environments: A Systematic Literature Review

Nosheen Qamar; Faria Nazir; Nosheen Sabahat; Sundus Sagheer; Maham Noor

Authors

Nosheen Qamar University of Management and Technology (UMT)
Faria Nazir University of Management & Technology, Lahore
Nosheen Sabahat Forman Christian College University, Lahore
Sundus Sagheer University of Management & Technology, Lahore
Maham Noor University of Central Punjab, Lahore

Keywords:

Cloud-based IoT; Resource Management; Cost-Efficiency; Scalability; and Performance Optimization

Abstract

Introduction: The increasing rate of spread of the Internet of Things (IoT) has led to a massive amount of data and poses a pivotal challenge in the resource-sharing process of a cloud computing environment. It is needed to manage resources effectively, with scalability, to provide support for IoT operations at reasonable costs and reliability.

Novelty Statement: This Systematic Literature Review (SLR) examines literature on resource optimization techniques in IoT-enabled cloud environments with a focus on reducing resource consumption, yet system performance continues to be maintained.

Material and Method: This study examines the related research on prominent scholarly databases depending on the adaptation of dynamic resource provision, workload balancing, cost-based provisioning, and proactive scaling strategies. It also identified machine learning-based demand forecasting, adaptive resource scaling approaches, and containerization as efficient deployment methods. Another aspect that is mentioned in the review is the importance of the analysis of historical data and the intelligent distribution of workload for the improvement of scalability and responsiveness.

Result and Discussion: The results indicate an increasing interest in the flexible and elastic resource management dimensions that address the changes in workloads and varying application demands. This review provides discussion on sustainable and efficient cloud-based approaches to IoT since it summarizes the most current solutions and creates a research gap through the identifies research gaps and opportunities. Findings from reviewed studies indicate improvements, including 17.3% faster execution time, 22% higher throughput, 18% energy savings, 23% lower provisioning cost, and 87% resource efficiency in cloud-based IoT environments. The results indicate that security vulnerabilities have the highest impact severity score (10), while rule-based scaling and infrastructure control-related challenges show the lowest scores (6–7). Furthermore, auto-scaling demonstrates the highest effectiveness score (10) among dynamic scaling strategies, whereas rule-based and vertical scaling exhibit comparatively lower effectiveness scores (6–7) in cloud-based IoT environments.

Conclusion Remarks: It underlines that there is a demand of optimized, intelligent systems that correlate performances with energy consumption and power management in medium- to large-scale IoT environments.

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