Cross-layer Scheduling Optimization for Cloud Services Leveraging Predictive Application Traffic Models
DOI:
https://doi.org/10.66238/smsrj.v4i2.79Keywords:
Cloud Computing, Cross-layer Scheduling, Traffic Prediction, Resource Allocation, Machine LearningAbstract
The rapid proliferation of latency-sensitive and bandwidth-intensive applications has placed unprecedented demands on cloud computing infrastructures. Traditional resource allocation mechanisms, which typically operate within isolated layers of the network and computing stack, often fail to achieve optimal performance due to their reactive nature and lack of inter-layer visibility. This paper presents a comprehensive investigation into cross-layer scheduling for cloud services, augmented by predictive application traffic modeling. By integrating machine learning-based traffic forecasting with a joint optimization framework spanning the application, network, and computing layers, the proposed system transitions from reactive resource provisioning to proactive capacity planning. We detail the system architecture, mathematical formulation of the cross-layer optimization problem, and a heuristic algorithm designed to minimize end-to-end latency while maximizing resource utilization. Extensive simulation evaluations using real-world cloud cluster traces demonstrate that the predictive cross-layer approach significantly reduces Service Level Agreement violations and improves overall throughput compared to conventional single-layer and reactive scheduling paradigms. The findings underscore the necessity of holistic, predictive resource management in next-generation cloud environments, paving the way for more resilient and efficient infrastructure capable of handling highly volatile application workloads.
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