Robust Dynamic Edge Service Placement Under Spatio-Temporal Correlated Demand Uncertainty

TL;DR

This paper presents a robust, multi-period model for edge service placement that leverages spatio-temporal demand correlations to reduce costs and improve service quality under uncertainty.

Contribution

It introduces a novel two-stage robust optimization framework with an iterative solution method that effectively handles demand uncertainty and integer variables.

Findings

Model reduces operational costs compared to traditional methods.

Leverages demand spatio-temporal correlation for better resource allocation.

Demonstrates convergence and efficiency of the proposed solution approach.

Abstract

Edge computing allows Service Providers (SPs) to enhance user experience by placing their services closer to the network edge. Determining the optimal provisioning of edge resources to meet the varying and uncertain demand cost-effectively is a critical task for SPs. This paper introduces a novel two-stage multi-period robust model for edge service placement and workload allocation, aiming to minimize the SP's operating costs while ensuring service quality. The salient feature of this model lies in its ability to enable SPs to utilize dynamic service placement and leverage spatio-temporal correlation in demand uncertainties to mitigate the inherent conservatism of robust solutions. In our model, resource reservation is optimized in the initial stage, preemptively, before the actual demand is disclosed, whereas dynamic service placement and workload allocation are determined in the subsequent stage, following the revelation of uncertainties. To address the challenges posed by integer recourse variables in the second stage of the resulting tri-level adjustable robust optimization problem, we propose a novel iterative, decomposition-based approach, ensuring finite convergence to an exact optimal solution. Extensive numerical results are provided to demonstrate the efficacy of the proposed model and approach.