Efficient Resource Scheduling in Cloud Computing Based on QoS Parameters

Abstract

Cloud computing has emerged as a dominant paradigm for delivering scalable and on-demand computing resources. Efficient resource scheduling plays a critical role in ensuring optimal utilization of cloud infrastructure while meeting Quality of Service (QoS) requirements such as latency, throughput, availability, and reliability. This paper proposes a QoS-aware scheduling approach that dynamically allocates resources based on user-defined service parameters. The proposed system improves performance, reduces execution time, and enhances user satisfaction compared to existing scheduling techniques. Cloud computing has become the backbone of modern digital services, offering scalable and on-demand access to computing resources. However, efficient resource scheduling remains a critical challenge due to heterogeneous workloads and diverse Quality of Service (QoS) requirements. Traditional scheduling approaches often fail to balance performance, reliability, and user satisfaction, leading to resource underutilization and frequent SLA violations.This paper proposes a QoS-aware resource scheduling framework that dynamically allocates cloud resources based on multiple service parameters, including latency, throughput, availability, and cost. The architecture integrates a monitoring feedback loop to adapt scheduling decisions in real time, ensuring responsiveness to changing workloads. Experimental evaluation demonstrates that the proposed system reduces latency by 40%, improves resource utilization by 20%, and lowers SLA violation rates by more than half compared to existing methods.

The results validate the effectiveness of incorporating QoS metrics into scheduling algorithms and highlight the framework’s potential to enhance efficiency, reliability, and user satisfaction in large-scale cloud environments. This work provides a foundation for future research in intelligent, sustainable, and adaptive resource scheduling strategies.