Energy-efficient offloading policy in D2D underlay communication integrated with MEC service

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

Mobile edge computing (MEC) is a promising paradigm to integrate computing and communication resources in mobile networks. MEC can improve mobile service quality and enhance Quality of Experience (QoE) by offloading computation tasks to MEC servers. However, a MEC server only can provide limited computational resources for users. In this paper, we consider a mobile edge computing system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in a local region, (iii)offloading tasks to servers in a nearby region. In the policy (iii), mobile user equipment can utilize computational resources of MEC servers in nearby regions to solve the problem of insufficient computational resources in local region servers. We formulate the computation offloading problem as a potential game and propose a Distributed Offloading strategy based on Jacobi algorithm (DOJ) for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system costs and guarantee the QoE of users.

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with mobile edge computing. In this integrated framework, the MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using graph coloring. The outcomes of the offloading decision and PRB allocation are then used to allocate the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

With the emergence of increasingly computing-intensive and delay-sensitive tasks, the processing of computing tasks on cloud servers cannot meet the current needs any longer. The emergence of mobile edge computing (MEC) technology and the popularity of 5G applications can solve these demands. Offloading tasks to the MEC server reduces the energy consumption of local devices, and also has a lower latency than offloading to the cloud server. In this paper, an MEC–edge cloud server collaborative system model with energy harvesting technology is designed to minimize the processing delay of computing tasks by allocating computing resources. We propose an optimal integer linear programming (OILP) algorithm with two steps. Firstly, we propose a Lyapunov stability optimization algorithm based on task priority. With the constraints of local mobile device power stability, the divide-and-conquer idea is used for solving the target values of the processing tasks locally, and the MEC and edge cloud servers separately. Therefore, the objective problem is transformed into an integer linear programming problem, and then an integer linear programming algorithm based on CPU utilization optimization is proposed to obtain a resource allocation scheme. Simulation results show that the proposed OILP algorithm can further reduce the delay, improve the CPU’s utilization of the MEC server, and reduce the number of the tasks that cannot be processed under the condition of the energy stability of the local device.

Current connected vehicle applications, such as platooning require heavy‐load computing capability. Although mobile edge computing (MEC) servers connected to the roadside intelligence facility can assist such separable applications from vehicles, it is a challenge to coordinate the allocation of subtasks among vehicles and MEC servers on the premise of ensuring communication quality. Therefore, an offloading algorithm is proposed based on a double deep Q‐network to solve the placement of subtasks for vehicle to infrastructure and vehicle to vehicle cases. This algorithm considers the randomness of task generation and is model‐free. The MEC server can assist the vehicle in training the neural network and storing relevant state transitions. To improve the performance of the algorithm, the decaying greedy policy is incorporated for faster convergence. The simulation results showed that this algorithm performed well in reducing the dropped subtask rate, average time delay, and total energy consumption.

With the increasing number of Internet of Things (IoT) devices, a huge amount of latency-sensitive and computation-intensive IoT applications have been injected into the network. Deploying mobile edge computing (MEC) servers in cloud radio access network (C-RAN) is a promising candidate, which brings a number of critical IoT applications to the edge network, to reduce the heavy traffic load and the end-to-end latency. The MEC server’s deployment mechanism is highly related to the user allocation. Therefore, in this paper, we study hierarchical deployment of MEC servers and user allocation problem. We first formulate the problem as a mixed integer nonlinear programming (MINLP) model to minimize the deployment cost and average latency. In terms of the MINLP model, we then propose an enumeration algorithm and approximate algorithm based on the improved entropy weight and TOPSIS methods. Numerical results show that the proposed algorithms can reduce the total cost, and the approximate algorithm has lower total cost comparing the heaviest-location first and the latency-based algorithms.

For mobile devices (MDs) and Internet of Things (IoT) devices with limited computing capacity and battery, offloading part of tasks to the mobile edge computing (MEC) server is attractive. In this paper, we propose a joint partial computation offloading and pricing scheme in a multi-user MEC system. Firstly, we establish MD’s cost model and MEC server’s revenue model in terms of money. Secondly, we investigate MD’s cost minimization partial offloading strategy to jointly control MD’s task allocation, local CPU frequency and the amount of computational resource blocks (CRBs) requested. Finally, we formulate the revenue maximization problem for MEC server with limited computing capacity, a heuristic algorithm is proposed for MEC server to find the optimal service price. Numerical results verify the effectiveness of our proposed scheme in cost saving and pricing.

Aiming at the problem that fixed mobile edge computing (MEC) server is difficult to meet the needs of mobile users and temporary computing services, this study proposes a network resource allocation strategy based on unmanned aerial vehicle (UAV) cooperative edge computing. First, a UAV-aided MEC scene is designed, and a single UAV with an MEC server is used to provide auxiliary computing services for ground multiusers. Then, an optimization model aiming at total system delay is constructed by considering the system communication model and calculation model. Finally, Deep Q-Network is used to solve the optimization problem to obtain the best resource allocation scheme. Based on the experimental platform, the proposed strategy is demonstrated and analyzed. The results show that when the number of user equipment is 40, the total delay is about 33s, which is 35.29%, 31.25%, and 15.38% lower than other comparison strategies and effectively reduces the computing delay of users.

Mobile edge computing (MEC) can significantly reduce latency by pushing resources away from remote clouds to distributed base stations (BSs) equipped with MEC servers, which are closer to users and deployed by service providers (SPs) at the edge of cellular networks. To improve user experience and increase their own revenue, SPs tend to use resources in their deployed BSs to provide services instead of using resources in BSs deployed by other SPs. We envision a densely-deployed multi-SP MEC network where a user equipment (UE) is covered by multiple BSs from different SPs. As the resource in BSs and MEC servers is limited, it is a challenging problem for SPs to reasonably allocate resources in the edge computing (EC) layer to improve the quality of service. In this article, we propose a novel resource allocation scheme, Decentralized Multi-SP Resource Allocation (DMRA), which aims to maximize the total profit of all SPs at the EC layer and provide high-quality services. Then, we extend our design to the online scheme. The algorithm Decentralized Multi-SP Online Resource Allocation (DMORA) is proposed to fit the dynamic network environment. Simulation results indicate that our proposed schemes can effectively maximize the total profit of all SPs at the EC layer while improving user experience.

Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.

Mobile edge computing (MEC) can extend the computing capability of mobile users (MUs), by offloading computational tasks from MUs to proximal MEC servers. Specifically, offloaded tasks are firstly transmitted to access points (APs) via wireless channels, and then relayed to MEC servers through backhauls. Due to differentiated wireless channels and input data sizes, offloaded tasks from different MUs arrive at the AP and MEC server in an asynchronous manner. Considering such asynchrony, we design sequential execution model for backhaul transmission and task processing in the MEC server and present its merits in terms of maximum task completion time for different backhaul configurations: 1) With ideal backhaul, the optimal task scheduling order in the MEC server follows the first coming and first scheduling rule; 2) With limited backhaul, an effective two-stage task scheduling order on the backhaul and MEC server is determined by the Johnson's rule. Further, we make suboptimal decisions on wireless resource provisioning and task offloading, with respect to the attained task scheduling order and derived necessary and sufficient conditions under which full local processing and full offloading are optimal. Finally, simulation results demonstrate the superiority of our devised sequential execution model and computation offloading mechanisms.

In this paper, we consider distributed heterogeneous multi-layer mobile edge computing (HetMEC) networks, where resource-poor edge devices (EDs) upload computing tasks for processing to the mobile edge computing (MEC) servers and a cloud center (CC). To reduce total energy consumption, computation offloading and resource allocation are independently performed by each device and each server. However, due to the partial information available at each device and server, the offloading strategies may overwhelm the layers above. This may lead to network congestion, i.e., so many tasks are offloaded to the same node that this node is overloaded. To address this problem, we develop a smart pricing mechanism to coordinate the computation offloading of multi-layer devices, where the CC charges the MEC servers and EDs for computing services and network congestion. In particular, to satisfy the latency constraints of each task, we construct a Lagrangian framework where multi-agent reinforcement learning is utilized by each MEC server to determine its offloading strategies and resource allocation, so that the total energy consumption is reduced. Simulation results show that our algorithm achieves an energy consumption reduction of 28% and a decrease in congestion probability of between 28% and 100% compared to the state of the art.

In this paper, the problem of joint task, power, and spectrum allocation is studied for a mobile edge computing (MEC) based small cell network (SCN). In the considered network, each small base station (SBS) that is connected with an MEC server provides commutation service to users while each MEC server provides computation service to users, who will request various types of computational tasks including: collaboration computational task performed at both the user and the MEC sever, edge computational task processed at the MEC server, and local computational task implemented by user itself. As the data size of each computational task requested by each user varies, one must reconsider resource (transmit power and subcarrier) allocation and task partition to effectively service the users. We formulate this problem as an optimization problem aiming to minimize the transmission and computational delay. To solve this problem, a multiple stack Q-learning method is proposed. The proposed method can use multiple stacks to record the wireless environment and users' information so as to avoid repeatedly learning the same information and, thus improving the learning efficiency and speeding up the convergence. Simulation results show that the proposed resource allocation scheme provides 79\% and 63\% gains in terms of sum delay compared to cases that fully computed at user and fully computed at the MEC server, respectively.

For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

The unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pre-trained deep neural network (DNN) is deployed on the UAV to identify a target from the captured video frames and enable UAV to keep tracking. However, tracking in real time by the DNN requires a lot of computational resources. This motivates us to consider offloading this type of machine learning (ML) tasks to a mobile edge computing (MEC) server. Specifically, we propose a novel hierarchical ML tasks distribution framework for the UAV tracking system, where the UAV is embedded with lower layers of the pre-trained convolutional neural network (CNN) model due to its limited computing capability, while the MEC server with rich computing resources will handle the higher layers of the CNN model. An optimization problem is formulated to minimize the CNN inference delay while taking into account the communications delay, computing time, and ML error. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions. Numerical results demonstrate the effectiveness of the proposed ML tasks distribution framework with the optimized offloading strategy.