Risk-Aware Resource Allocation in Edge Computing Using Stochastic Forecasting

Edge computing brings processing closer to data sources, reducing latency and bandwidth usage for modern applications. However, the limited capacity of edge resources and volatile nature of workload demands create significant challenges for efficient resource management, often leading to resource underutilization. In this work, we propose a speculative resource allocation framework supported by stochastic workload forecasting, inspired by the Black-Scholes financial model. This framework dynamically assesses the risk associated with fluctuating demands over different time windows and proactively aligns resource allocation based on the performed risk assessments. The outcomes of this model drive a multi-objective heuristic mechanism that dynamically manages resources, speculatively aligning differing workload demands when placing them within a node, optimizing key performance metrics such as latency, infrastructure utilization, costeffectiveness, and potential application disruptions during execution. Our approach does not require training, making it more adaptable to fluctuating demands compared to machine learning-based methods. Through simulations we demonstrate that our framework improves performance and resource utilization, providing a scalable, responsive, and cost-effective solution that benefits both end-users and operators.