Risk-Averse Investment Strategy for MEC Service Provisioning: A Data-Driven Distributionally Robust Solution

The emerging Internet-of-Things (IoT) era has stimulated many new computation-intensive applications. To support them, mobile edge computing (MEC) is a promising solution that allows users to offload their heavy computing tasks to nearby edge servers. Taking such computation offloading as the service, application service providers (ASPs) can rent resources from mobile network operators for MEC service provisioning. However, it is challenging for ASPs to determine how many resources to rent at different regions and times due to the uncertain user demand. When making an investment strategy, it is crucial to maximize the profit with the consideration on the quality of service (QoS), where a joint scheduling on both communication and computing resource under the uncertain demand is needed. To deal with the uncertainty, the probability distribution information is usually employed, which, unfortunately, might be hardly obtainable in practice. Therefore, in this paper, we propose a data-driven risk-averse MEC resource investment (DRAI) strategy, where the demand uncertainty issue is particularly addressed. Specifically, we formulate the DRAI strategy into a stochastic optimization problem, which can achieve the expected optimal profit under QoS guarantee statistically from a risk-averse perspective. To solve it, instead of relying on specific distribution models, we construct an ambiguity set based on the statistical characteristics derived from the historical data that contains all possible distributions, and develop a data-driven distributionally robust solution, aiming at achieving the best strategy under the worst case to make it trustworthy. Simulation results illustrate the effectiveness of the proposed DRAI strategy.