关键词： game theory   power aware computing   Queueing theory   Probability theory, stochastic processes, and statistics   queueing theory   Optimisation techniques   Nash equilibrium   Game theory   mobile computing   MATLAB platform   Performance evaluation and testing   optimisation   performance optimization   Ubiquitous and pervasive computing   Operating systems   offloading weight   base station   resource allocation   Electrical/electronic equipment (energy utilisation)   Internet of Things   energy consumption constraint   mobile edge computing   Lyapunov drift penalty technology   energy consumption deficit queue   task offloading  

摘要： This paper proposes a task offloading and resource allocation algorithm based on mobile edge computing. Firstly, for the long-term performance optimization of small base stations, the system model is established according to task arrival characteristics, credit relationship between small base stations, time delay and energy consumption of computing tasks and cable channel congestion. Secondly, the energy consumption deficit queue based on Lyapunov drift penalty technology used for the energy consumption constraint of small base stations in long-term optimization process. The energy consumption deficit queue is established for each small base station to couple the energy consumption and time of small base stations, so that small base stations can meet the energy consumption constraints in long-term optimization process. Finally, game theory is introduced to calculate offloading weight by the offloading weight model based on Shapley value. Besides, the offloading weight is calculated equitably according to the return of different tasks. Simulation results on MATLAB platform show that the proposed algorithm can achieve Nash equilibrium after finite iterations. Moreover, its performance on energy consumption, time delay and number of tasks successfully offloaded is better than other comparison strategies.

关键词： mobile edge computing   mobile edge server placement   multiagent RL   edge security  

摘要： Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.

关键词： Task analysis   Batteries   Servers   Uplink   Delays   Neural networks   Energy harvesting   Computation offloading   deep deterministic policy gradient   deep reinforcement learning   mobile edge computing   resource allocation  

摘要： We study a joint partial offloading and resource allocations (JPORA) problem for the mobile edge computing (MEC) with energy harvesting (EH), where the number of MDs, computation task, and harvested energy of mobile device (MD) are highly dynamic. It is critical to acquire an algorithm that optimally adapts JPORA decisions. Recent studies employ deep deterministic policy gradient (DDPG) agent to tackle the JPORA problem. However, traditional DDPG cannot generalize well to different MEC network scale, due to the deep neural networks in DDPG can only extract latent representation from Euclidean data, with the characteristics of MEC network structural information ignored. To this end, by taking advantage of the graph-based relationship deduction ability form graph convolutional networks (GCN) and the self-evolution ability from the experience training of DDPG, we propose a centralized GCN-DDPG agent to learn making decisions for MDs, including offloading ratio, local computation capacity, and uplink transmission power. Experimental results show that the proposed GCN-DDPG provides significant performance improvement over a number of state-of-the-art DRL agents.

关键词： cost-performance tradeoff   edge server   mobile edge computing   queueing model   server configuration  

摘要： Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest-quality services with a given cost constraint;(2) minimize the investment cost with a service-quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality.

关键词： Task analysis   Resource management   Microcell networks   Computational modeling   Servers   Interference   Reinforcement learning   Mobile edge computing   small cell networks   multi-agent deep reinforcement learning   computation offloading   interference coordination  

摘要： Integrating mobile edge computing (MEC) with small cell networks has been conceived as a promising solution to provide pervasive computing services. However, the interactions among small cells due to inter-cell interference, the diverse application-specific requirements, as well as the highly dynamic wireless environment make it challenging to design an optimal computation offloading scheme. In this paper, we focus on the joint design of computation offloading and interference coordination for edge intelligence empowered small cell networks. To this end, we propose a distributed multi-agent deep reinforcement learning (DRL) scheme with the objective of minimizing the overall energy consumption while ensuring the latency requirements. Specifically, we exploit the collaboration among small cell base station (SBS) agents to adaptively adjust their strategies, considering computation offloading, channel allocation, power control, and computation resource allocation. Further, to decrease the computation complexity and signaling overhead of the training process, we design a federated DRL scheme which only requires SBS agents to share their model parameters instead of local training data. Numerical results demonstrate that our proposed schemes can significantly reduce the energy consumption and effectively guarantee the latency requirements compared with the benchmark schemes.

关键词： Satellites   Task analysis   Resource management   Satellite broadcasting   Edge computing   Processor scheduling   Voltage control   Deep reinforcement learning (DRL)   mobile edge computing   resource allocation   satellite communication   vehicle-to-vehicle (V2V)  

摘要： Satellite assisted vehicle-to-vehicle (V2V) communication can provide services for vehicles in depopulated areas, and it can be employed as an effective complementary component for terrestrial vehicular networks. Since the available communication and computing resource for satellites are scarce, task offloading, computing and communication resource allocation, which are coupled with each other, are critical issues for satellite assisted V2V communication. To tackle these problems, we formulate the joint offloading decision, computing and communication resource allocation problem for satellite assisted V2V communication as a mixed-integer nonlinear programming problem with minimum weighted-sum end-to-end latency, and we decouple it into two subproblems. First, the Lagrange multiplier method is adopted to obtain the optimal computing and communication resource allocation with fixed offloading decision. Then, the results of the resource allocation subproblem are fed into the offloading decision problem, which is formulated as a Markov decision process. To maximize the long-term reward of offloading decision, a deep reinforcement learning based method is adopted to learn the optimal offloading decision. Finally, the simulation results show that the proposed joint task offloading and resource allocation approach has superior performance compared with other schemes.

关键词： hierarchical strategy   mobile edge computing   task scheduling  

摘要： Mobile Edge Computing (MEC) has attracted much attention from the global researchers because it supports the task migration from the cloud server to the mobile edge devices, which greatly improves the computation performance. Under MEC, this letter proposes a hierarchical task scheduling strategy. To be specific, the system framework includes the edge computing layer and the frog computing layer, in which the former is responsible for processing the simple tasks (outputting the sketchy result) and the latter is used to addressing the complex tasks (outputting the precise result). In particular, the tasks after going through the edge computing layer, they are allocated with the priorities;on this basis, the frog computing layer embeds a priority-based task scheduling strategy. The experimental results demonstrate that the proposed hierarchical scheduling strategy not only is feasible but also has better delay performance and load balance performance than the benchmarks.

关键词： Internet of Vehicles   mobile edge computing   task offloading   Stackelberg game   reinforcement learning  

摘要： Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.

关键词： Task analysis   Servers   Internet of Things   Solid modeling   Computational modeling   Energy consumption   Reinforcement learning   Mobile edge computing   deep reinforcement learning   computation offloading   latency   energy efficiency  

摘要： Internet of Things (IoT) is considered as the enabling platform for a variety of promising applications, such as smart transportation and smart city, where massive devices are interconnected for data collection and processing. These IoT applications pose a high demand on storage and computing capacity, while the IoT devices are usually resource constrained. As a potential solution, mobile edge computing (MEC) deploys cloud resources in the proximity of IoT devices so that their requests can be better served locally. In this work, we investigate computation offloading in a dynamic MEC system with multiple edge servers, where computational tasks with various requirements are dynamically generated by IoT devices and offloaded to MEC servers in a time-varying operating environment (e.g., channel condition changes over time). The objective of this work is to maximize the completed tasks before their respective deadlines and minimize energy consumption. To this end, we propose an end-to-end Deep Reinforcement Learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. The simulation results are provided to demonstrate that the proposed approach outperforms the existing methods.

关键词： Task analysis   Servers   Delays   Cloud computing   Heuristic algorithms   Edge computing   Collaboration   Collaborative Data Caching   Multi-user Computation Offloading   Convex Optimization   Mobile Edge Computing  

摘要： 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.