Near Optimal Locality-Aware Task Allocation towards Stable Blockchain-Based MEC System: A Potential Game Approach

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

Mobile edge computing (MEC) systems provide mobile devices (MDs) with low-latency cloud services by deploying edge servers (ESs) in the vicinity. In fact, various mobile applications may generate cotasks, each of which is completed only if all its constituent tasks are finished. Existing works have been devoted to the design of MEC offloading and scheduling, but none of them exploits the cotask feature to better utilize the networked computing resources. In this paper, we investigate the problem of joint cotask-aware offloading and scheduling in MEC systems (Cool-Edge), and we formulate it as a mixed integer non-linear program (MINLP), the objective of which is to minimize average cotask completion time (ACCT). To cope with the Cool-Edge problem, we propose two low-complexity algorithms to offload cotasks based on an LP rounding technique and schedule them according to an earliest-cotask-arrival-first rule, respectively, and we further prove the approximation factor jointly achieved by the two algorithms. Finally, we conduct testbed experiments and simulations to demonstrate the effectiveness of our proposed solution, and we also show how ACCT varies with the network environment.

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose an SDN (Software Defined Network)-based MEC (Mobile Edge Computing) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDNs programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Secondly, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multi-user stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PER) to minimize energy consumption and processing latency with Multi-Agent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG, Q-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.

In current world, processing the data by the users, researchers, organizations, etc. through online source (ie. cloud) is increasing tremendously, where it offers various services like storing the data, allocating the resources, retrieval of data, security, etc. Since the data is stored in cloud, processing it leads to increase in latency, due to its long-distance communication. As distance increases, data transmission produces a delay between source and destination which is called as latency. To get over this problem, MEC (Mobile Edge Computing) system can be positioned between the cloud infrastructure and the user equipment(UE) in the IoT environment. Instead of sending data to the cloud directly which is collected by IoT sensors, edge computing processes those data within the network. The study of this review focuses on the various techniques such as deep learning algorithms, deep reinforcement learning approaches for offloading decisions and optimization techniques in 5G networks for minimizing the latency in the MEC systems, for producing higher processing capability to the clients. For increasing the MEC system performance, efficient offloading strategies can be used to minimize latency for various tasks.

As more and more compute-intensive and delay-sensitive applications are deployed on smart mobile devices, mobile edge computing is considered an effective way to solve the limited computing ability of smart mobile devices (SMDs). At present, latency has become the most critical indicator of the quality of service (QoS), and more and more studies focus on this aspect. Unlike previous work, our work fully takes into account the limited storage and computing ability of edge servers. To effectively reduce the delay of SMDs and improve QoS, we propose a Delay Control Strategy Joint Service Caching and Task Offloading (DCS-OCTO) in a three-tier mobile edge computing (MEC) system consist of multi-user, multi-edge server and remote cloud servers. Some of the key challenges include service heterogeneity, unknown system dynamics, spatial demand coupling, and decentralized coordination. In particular, a very compelling but rarely studied issue is the dynamic service caching in the three-tier MEC system. The DCS-OCTO strategy is proposed based on Lyapunov optimization and Gibbs sampling. It works online without requiring prior information and achieves provable near-optimal performance. Finally, simulation results show that the strategy effectively reduces the overall system delay while ensuring low energy consumption.

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

In a mobile edge computing (MEC) system with a large number of low power mobile terminals, proper computation offloading and resource allocation is crucial to achieving desirable system performance. In this paper, we consider the joint computation offloading and resource allocation problem for an uplink MEC system under the min-max fairness criterion. The proposed optimization problem is difficult to solve due mainly to the nonconvex nondifferentialbe objective and the nonlinear coupling of design variables in the constraints. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then develop a novel algorithm based on the concave-convex procedure (CCCP) technique to address the problem. Furthermore, by exploiting the problem structure, an efficient algorithm based on inexact block coordinate descent (IBCD) method is proposed to reduce the computational complexity. Numerical results validate the efficiency of the proposed algorithms.

Energy-efficient computation is important in mobile edge computing (MEC) systems. However, the computation efficiency problem in the unmanned aerial vehicle (UAV)-enabled MEC systems has been rarely researched. In this paper, a UAV-enabled MEC system under partial computation offloading mode is investigated. The computation efficiency is maximized by jointly optimizing the offloading times, the central processing unit frequencies, the transmit powers of the user and the trajectory of the UAV. For the non-convex computation efficiency problem, a two-stage iterative algorithm is proposed. Moreover, we derive the closed-form expressions for the local computation frequency and the transmit power of the user, which facilitates our algorithm implementation. Simulation results demonstrate that the computation efficiency of our proposed joint optimization scheme is better than those of other benchmark schemes.

This article establishes a new multiunmanned aerial vehicle (multi-UAV)-enabled mobile edge computing (MEC) system, where a number of unmanned aerial vehicles (UAVs) are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This article presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV's location and the entire population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves an adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV-enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

By providing spatial diversity gain, the incorporation of multiple antennas into mobile edge computing (MEC) systems can improve the transmission performance. Meanwhile, employing energy harvesting (EH) helps enhance the system sustainability. In this paper, we focus on multi-input multi-output (MIMO) MEC systems with EH and studies the computation offloading. The design objective is to minimize the time average of a weighted sum of energy consumption and execution delay, meanwhile stabilizing the battery energy queue. To this end, we formulate the problem as a statistic program and propose a dynamic computation offloading (DCO) algorithm in which the transmitter covariance matrix, CPU-cycle frequencies for local computing, and partial offloading ratio are jointly optimized. Based on Lyapunov optimization, the program is first transformed into a nonconvex per-time slot problem. Then, we solve it by the successive convex approximation (SCA) technique, where a sequence of convex problems are created and solved. Simulation results demonstrate that the proposed algorithm is asymptotically optimal and outperforms several benchmark schemes in terms of both the average system cost and task drop ratio.

To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles (IoV) and Industrial Internet of Things (IIoT), the blockchain-enabled Mobile Edge Computing (MEC) system has received extensive attention. However, blockchain is a computing and communication intensive technology due to the complex consensus mechanisms. To facilitate the implementation of blockchain in the MEC system, this paper adopts the committee-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm and focuses on the committee selection problem. Vehicles and IIoT devices generate the transactions which are records of the application tasks. Base Stations (BSs) with MEC servers, which serve the transactions according to the wireless channel quality and the available computing resources, are blockchain nodes and candidates for committee members. The income of transaction service fees, the penalty of service delay, the decentralization of the blockchain and the communication complexity of the consensus process constitute the performance index. The committee selection problem is modeled as a Markov decision process, and the Proximal Policy Optimization (PPO) algorithm is adopted in the solution. Simulation results show that the proposed PPO-based committee selection algorithm can adapt to the system design requirements with different emphases and outperforms other comparison methods.

In mobile edge computing (MEC) systems with energy harvesting, the mobile devices are empowered with the energy that harvested from renewable energy sources. On the other hand, mobile devices can offload their computation-intensive tasks to the MEC server to further save energy and reduce the task execution latency. However, the energy harvested is unstable and the mobile devices have to make sure that the energy should not be run out. Moreover, the wireless channel condition between the mobile device and the MEC server is dynamically changing, leading to unstable communication delay. Considering the energy constraints and unstable communication delay, the benefit of computation offloading is limited. In this paper, we investigate D2D-assisted computation offloading for mobile edge computing systems with energy harvesting. In our method, the mobile device is allowed to offload its tasks to the MEC server with the help of its neighbor node. More Specifically, the neighbor node acts as a relay to help the mobile device to communicate with the MEC server. Our goal is to minimize the average task execution time by selecting an optimal execution strategy for each task, i.e., whether to execute the task locally, or offload it to the MEC server directly, or offload it to the MEC server with the help of the most suitable neighbor node, or just to drop it. We propose a low-complexity online algorithm, which stem from Lyapunov Optimization-based Dynamic Computation Offloading (LODCO) algorithm, to solve this problem. Extensive simulations verified the effectiveness of the proposed algorithm, where the average task execution time is reduced around 50% as compared to that of the original LODCO algorithm.

In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.

In modern mobile edge computing (MEC) systems, effectively integrating service caching, task offloading, and service delivery is crucial for ensuring high network quality and enhanced user experience. In this article, we introduce a novel joint optimization framework that employs Uncrewed aerial vehicles (UAVs) as dynamic edge computing nodes, collaborating with service collection units (SCUs) that aggregate service requests from Internet of Things (IoT) devices. In the proposed system, UAVs equipped with small-capacity edge servers can process computational tasks locally or forward them to a base station (BS) via the backhaul when necessary. Both user layers (UEs) and UAVs dynamically cache programs using an adaptive algorithm based on task frequency and program weight, which maximizes cache hit rates and expedites service execution. A joint optimization problem is formulated to minimize the overall end-to-end latency through coordinated task offloading, CPU allocation, caching, and UAV scheduling, subject to energy, storage, and mobility constraints. Our resource-aware task offloading and UAV trajectory planning further enable efficient service responsiveness and minimize reliance on the backhaul link. The overall objective is to reduce maximum task latency and backhaul congestion across the network. Extensive simulation results show that our framework significantly outperforms standard baselines, including random caching, most caching, and calculation delay and energy consumption multiobjective optimization problem (CDECMOP), achieving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18.5\%$</tex-math> </inline-formula> lower energy consumption and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$23.6\%$</tex-math> </inline-formula> lower latency across diverse network scenarios. These findings underscore the effectiveness of the proposed UE-UAV cooperative caching and computation strategy for scalable, energy-efficient, and low-latency UAV-assisted MEC systems.

Unmanned aerial vehicle (UAV) with moderate computing resources has been deployed in current mobile edge computing (MEC) system to enhance the service coverage and computing capabilities of MEC servers. In such a UAV-assisted MEC system, UAV flies to the users with poor communication condition and executes the computation tasks in cooperation with the terrestrial MEC server, then the task latency perceived by users can be greatly reduced. However, frequent flight and computation offloading may drain its limited on-board battery and further deteriorate the service performance. Thus, we investigate the problem of joint online task offloading, flying trajectory and charging scheduling for UAV-assisted MEC system, with the goal of maximizing the system-wide energy efficiency in the long-term. Deriving such complicated but efficient strategies is non-trivial, since the long-term problem is NP-hard and requires future uncertain information (e.g., stochastic arrival of user tasks). To address these dual challenges, we carefully combine an online optimization technique with an approximate optimization method into a joint optimization framework, through (1) decomposing the long-term problem into a series of one-shot problems with Lyapunov optimization technique, and (2) solving the resulted NP-hard and non-linear one-shot problem with block coordinate descent and successive convex approximation methods. The efficacy of proposed online algorithm is verified by both rigorous theoretical analysis and extensive trace-driven evaluations.KeywordsMobile edge computingUnmanned aerial vehicleLyapunov optimizationTask offloadingTrajectory planning