Provided with mobile edge computing (MEC) services, wireless devices (WDs) no longer have to experience long latency in running their desired programs locally, but can pay to offload computation tasks to the edge server. Given its limited storage space, it is important for the edge server at the base station (BS) to determine which service programs to cache by meeting and guiding WDs’ offloading decisions. In this article, we propose an MEC service pricing scheme to coordinate with the service caching decisions and control WDs’ task offloading behavior in a cellular network. We propose a two-stage dynamic game of incomplete information to model and analyze the two-stage interaction between the BS and multiple associated WDs. Specifically, in Stage I, the BS determines the MEC service caching and announces the service program prices to the WDs, with the objective to maximize its expected profit under both storage and computation resource constraints. In Stage II, given the prices of different service programs, each WD selfishly decides its offloading decision to minimize individual service delay and cost, without knowing the other WDs’ desired program types or local execution delays. Despite the lack of WD’s information and the coupling of all the WDs’ offloading decisions, we derive the optimal threshold-based offloading policy that can be easily adopted by the WDs in Stage II at the Bayesian equilibrium. In particular, a WD is more likely to offload when there are fewer WDs competing for the edge server’s computation resource, or when it perceives a good channel condition or low MEC service price. Then, by predicting the WDs’ offloading equilibrium, we jointly optimize the BS’ pricing and service caching in Stage I via a low-complexity algorithm. In particular, we first study the differentiated pricing scheme and prove that the same price should be charged to the cached programs of the same workload. Motivated by this analysis, we further propose a low-complexity uniform pricing heuristics.

Pricing-Driven Service Caching and Task Offloading in Mobile Edge Computing

Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions

Mobile network operators (MNOs) allocate computing and caching resources for mobile users by deploying a central control system. Existing studies mainly use programming and heuristic methods to solve the resource allocation problem, which ignores the energy cost problem that is really significant to the MNO. To solve this problem, in this article, we design a joint computing and caching framework by integrating deep deterministic policy gradient (DDPG) algorithm. Especially, we focus on the Internet of Vehicles scenario, which needs the support of mobile network provided by MNO. We first formulate an optimization problem to minimize MNO’s energy cost by considering the computation and caching energy costs jointly. Then, we turn the formulated problem into a reinforcement learning problem and utilize DDPG methods to solve this problem. The final simulation result shows that our solution can reduce energy costs by more than 15%, while ensuring the tasks can be completed on time.

Deep Reinforcement Learning-Based Energy-Efficient Edge Computing for Internet of Vehicles

Profit maximization in cache-aided intelligent computing networks

Privacy-preserving incentive mechanism for multi-leader multi-follower IoT-edge computing market: A reinforcement learning approach

Mobile edge computing (MEC) has become a promising technique to accommodate demands of resource-constrained mobile devices by offloading the task onto edge clouds nearby. However, most existing works only focus on whether to offload or where to offload the task but ignore the motivations of edge clouds to offer service. In this paper, we first propose an incentive mechanism in a non-competitive environment. We utilize market-based profit maximization pricing model to establish the relationship between the resources provided by edge clouds and the price charged to mobile devices. By solving the optimization problem, we provide a reasonable pricing strategy to not only ensure the profit of resource providers but guarantee the quality of experience (QoE) of mobile devices. Furthermore, we design an online profit maximization multi-round auction (PMMRA) mechanism for the resource trading between edge clouds as sellers and mobile devices as buyers in a competitive environment. The mechanism can effectively determine the price paid by buyers to use the resources provided by sellers and make the corresponding match between edge clouds and mobile devices. Finally, numerical results show that proposed mechanism outperforms other existing algorithms in maximizing the profit of edge clouds.

Profit Maximization Incentive Mechanism for Resource Providers in Mobile Edge Computing

Mobile edge computing (MEC) can use wireless access network (RAN) to provide the services required by user's information technology (IT) and cloud computing functions nearby, which can create a high-performance and low latency service environment. Performing task offloading and data caching at access points (APs) in a cooperative manner can reduce the heavy backhaul load and the retransmission of content downloading. However, in edge networks (ENs), how to maximize storage utilization while reducing service latency and energy consumption is still a key issue, because the heterogeneity of ENs and the uneven distribution of users make it difficult to determine which MEC server and what data should be cached. In this paper, we study a two-tier MEC system, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers. The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. In order to solve the problem, we transform it into an equivalent task offloading convex optimization problem by fixing an optimization variable. Furthermore, we solve a cache placement problem by dynamic programming (DP) algorithm. Then we propose a distributed collaborative data caching and computing offloading (CDCCO) iterative algorithm. Simulation results demonstrate that our proposed CDCCO algorithm can significantly reduce the network cost and achieve better performance than other existing schemes.

Li Yang

Papers

Profit Maximization Incentive Mechanism for Resource Providers in Mobile Edge Computing

Collaborative Data Caching and Computation Offloading for Multi-Service Mobile Edge Computing

Fast congestion-free consistent flow forwarding rules update in software defined networking