Computation Resource Allocation in Mobile Blockchain-enabled Edge Computing Networks

This paper considers the computation offloading and resource allocation for system sum cost minimization in a mobile edge computing (MEC)network. The computation offloading decision of the user equipments (UEs)can be made through wireless link to access the MEC server. Our intent is to minimize the weighted sum cost of the system, which takes into account the energy consumption and time delay, for both task processing and data transmissions, subject to the force delay requirement of the devices. We first formulate the problem as a mixed-integer and non-convex optimization scheme. Enlightened by the superior performance of reinforcement learning (RL)on solving resource control problems, we put forward a novel RL-based scheme for computation offloading and resource allocation in MEC network. Specifically, the MEC node is considered as an “agent”, which first makes decision on whether to offloading the computing task to the MEC server, and then utilizes convex optimization algorithms to settle the radio spectrum and computation resource scheduling problem in each decision phase. The simulation results present that the RL-based method can achieve better computation offloading and resource allocation performance compared to those of local computing and remote computing modes. It also achieves very similar or slightly better performance compared to the genetic algorithm based method.

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.

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.

By leveraging the 5G enabled V2X networks, the vehicles connected by cellular base-stations can support a wide variety of computation-intensive services. In order to solve the arisen challenges in end-to-end low-latency transmission and backhaul resources, mobile edge computing (MEC) is now regarded as a promising paradigm for 5G-V2X communications. Considering the importance of both reliability and delay in vehicle communication, this article innovatively envisions a joint computation and URLLC resource allocation strategy for collaborative MEC assisted cellular-V2X networks and formulate a jointly power consumption optimization problem while guaranteeing the network stability. To solve this NP hard problem, we decouple it into two sub-problems: URLLC resource allocation for multi-cells to multi-vehicles and computation resource decisions among local vehicle, serving MEC server and collaborative MEC server. Secondly, non-cooperative game and bipartite graph are introduced to reduce the inter-cell interference and decide the channel allocation, which aims to maximize the throughput with a guarantee of reliability in URLLC V2X communication. Then, an online Lyapunov optimization method is proposed to solve computation resource allocation to get a trade-off between the average weighted power consumption and delay where CPU frequency are calculated using Gauss-Seidel method. Finally, the simulation results demonstrate that our proposed strategy can get better trade-off performance among power consumption, overflow probability and execution delay than the one based on centralized MEC assisted V2X.

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.

To increase the processing capabilities of mobile devices, mobile edge computing (MEC), has garnered a lot of attention. Computation offloading is a significant challenge in the MEC paradigm. This paper proposes an all-encompassing framework for MEC-based computation offloading and interference control in wireless networks. The allocation of physical resource blocks (PRBs), MEC computation resource allocation, and computation offloading decisions are all formulated as optimization issues within this integrated framework. Depending on each UE’s estimated local computational load and the MEC server’s estimated offloading cost, the MEC server chooses whether or not to offload work. Following this, the MEC server runs the PRB allotment graph colouring procedure. Computational resources on the MEC server are divided up among UEs results on basis of offloading choice and PRB distribution. Simulated results are shown to illustrate the effectiveness of the proposed technique using a range of system parameters.

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

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.

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.

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.

The offloading policy has attracted much attention in the study of Device-to-Device (D2D) Communication underlaying cellular network integrated with Mobile Edge Computing (MEC) service. D2D communication can improve energy efficiency and prolong the battery life of user equipment (UEs) by offloading computation tasks from a UE to a nearby UE via a D2D link. How to coordinate UEs to offload tasks in D2D underlay communication integrated with MEC Service for reducing interference and improving network energy efficiency is a very important subject. In this paper, we study a D2D underlay communication integrated with MEC service system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in local region, (iii) offloading tasks to a nearby UE. Furthermore, we formulate the computation offloading problem as a potential game and propose a D2D communication integrated with MEC service offloading computation algorithm for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system energy consumption under time constraints.

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.

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 joint communication and computation resource allocation problem for a single cell massive MIMO based mobile edge computing (MEC) network is discussed. In particular, we study the joint optimization of the pilot transmission power, data transmission power and the computing resource at the MEC server with the target of minimizing the maximal offloading delay over multiple users. Due to the non-convexity of the formulated problem and the defined optimal solution conditions, two suboptimal algorithms, i.e., the external point iteration and gradient descent based penalty function algorithm (EPF-GDA) and the external penalty function iteration based fruit fly optimization algorithm (EPF-FOA), are proposed to obtain the suboptimal solutions. The performance of the proposed algorithms, such as the users’ offloading delay, is verified by numerical simulations. And our results confirm the great performance gain of the algorithm EPF-FOA over EPF-GDA under different circumstances.