Distributed Denial-of-Service (DDoS) attack is critical to latency-critical systems such as Multi-Access Edge Computing (MEC) as it significantly increases the response delay of the victim service. An intrusion prevention system (IPS) is a promising solution to defend against such attacks. Still, there will be a trade-off between IPS deployment and application resource reservation as IPS deployment will reduce the computational resources for MEC applications. In this work, we propose a game-theoretic framework to study the joint computational resource allocation and IPS deployment in the MEC architecture. Given the expected attack strength and end-user demands, we study the pricing strategy of the MEC platform operator (MPO) and purchase strategy of the application service providers (ASPs). The best responses of both MPO and ASPs are derived theoretically. Based on the best responses, we propose an efficient algorithm to derive the Stackelberg equilibrium. The properties and optimality in the efficiency of the equilibrium are analyzed through simulations. The results confirm that the proposed solutions significantly increase the social welfare of the system.

The network attack such as Distributed Denial-of-Service (DDoS) attack could be critical to latency-critical systems such as Mobile Edge Computing (MEC) as such attacks significantly increase the response delay of the victim service. Intrusion prevention system (IPS) is a promising solution to defend against such attacks, but there will be a trade-off between IPS deployment and application resource reservation as the deployment of IPS will reduce the number of computation resources for MEC applications. In this paper, we proposed a game-theoretic framework to study the joint computation resource allocation and IPS deployment in the MEC architecture. We study the pricing strategy of the MEC platform operator and purchase strategy of the application service provider, given the expected attack strength and end user demands. The best responses of both MPO and ASPs are derived theoretically to identify the Stackelberg equilibrium. The simulation results confirm that the proposed solutions significantly increase the social welfare of the system.

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of Internet-of-Things (IoT) devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from IoT devices, the queueing delay at the edge nodes not only poses a colossal impediment to achieving satisfactory quality of experience (QoE) for the IoT devices but also to the benefits of the edge nodes owing to escalating energy expenditure. Moreover, since the service providers may differ, computationally competent entities' computing services should entail economic compensation for the incurred energy expenditure and the capital investment. Therefore, the workload allocation mechanism, where we consider flat-rate and dynamic pricing schemes in the multi-layer edge computing structure, is much-needed. We use Stackelberg game to capture the inherent hierarchy and interdependence between the second-layer edge node (SLEN) and first-layer edge nodes (FLENs). A truthful admission control mechanism grounded on the optimal workload allocation is designed for FLENs without violating end-to-end (E2E) latency requirements. We prove that a Stackelberg equilibrium with the E2E latency guarantee and truthfulness exists and can be reached through proposed algorithm. Simulation results confirm the effectiveness of our scheme and illustrate several insights.

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of mobile devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from the comparatively limited edge servers, an admission control charging a flat-rate price and resistant from self-interested manipulations is proposed to both utilize the scarce resources to the fullest and guarantee end- to-end latency for served mobile devices. Besides, to achieve energy sustainability and maximize profit, edge servers can lessen the energy consumption, and thus the energy expenditure by offloading certain requests to the cloud with a payment. Nonetheless, the cloud can reduce its energy consumption and enlarge the revenue through raising the service price to maximize its profit as well. The inherent hierarchy and interdependence between edge servers and the cloud are captured by Stackelberg game where the unique Stackelberg equilibrium is reached. The utility maximization problems for determining the optimal offloading ratio of edge servers and setting the optimal pricing of the cloud are investigated by exploiting the strict convexity and KKT conditions. Simulation results confirm the effectiveness of our scheme and several insights are illustrated.