Mobile Edge Computing Framework Research Articles

Joint Resource and Trajectory Optimization for Heterogeneous-UAVs Enabled Aerial-Ground Cooperative Computing Networks

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) is a promising paradigm for providing extensive coverage and additional computation services to the Internet of Things (IoT) devices with smart applications, which are normally computation-intensive and delay-critical, especially in some remote areas or emerging scenarios without available infrastructure. However, considering the restricted computation capabilities of UAVs, even multiple UAVs may not satisfy the quality of service (QoS) requirement of IoT users with heavy-computation applications. In this paper, a heterogenous-multiple-UAVs enabled collaborative aerial-ground MEC framework is proposed. The total computation bits maximization problem under the time-division multiple access (TDMA) scheme and partial offloading mode is formulated by jointly optimizing the user association, the CPU-cycle frequencies of users and computing UAVs, the transmit power of users and UAVs, the offloading time of users and UAVs, as well as the UAVs' trajectories. Due to the non-convexity of the original problem with the nonlinear coupling of different variables, a two-layered alternative optimization (TLAO) algorithm is proposed, where the outer layer optimizes the user association and resource allocation, while the inner layer optimizes the UAV trajectory scheduling. Simulation results demonstrate that our proposed TLAO scheme can improve the total computation bits compared with other benchmark schemes, and achieve fast convergence. Moreover, comparative results also verify that our proposed strategy is flexible to apply to homogeneous multi-UAVs and single-UAV scenarios.

Computation Capacity Enhancement by Joint UAV and RIS Design in IoT

Mobile-edge computing (MEC) networks are facing limited coverage and harsh wireless transmission environments that severely hinder the computation capacity of the Internet-of-Things (IoT) devices. To overcome these issues, this article proposes a novel MEC framework empowered by an unmanned aerial vehicle (UAV) relay and a reconfigurable intelligence surface (RIS). To fully exploit the potentials in terms of computation enhancement brought by the joint UAV and RIS design, we formulate a max–min computation capacity problem via determining the uplink signal detection, active beamforming of UAV, passive beamforming of RIS, time slot partition, computation bits of UAV, and UAV’s trajectory. We develop a concave–convex procedure (CCCP)-based algorithm in an alternating optimization manner over three subproblems to solve the formulated problem. It finds that the CCCP-based algorithm is conducive to decouple the intractable expressions by converting them into new but tractable second-order cone (SOC) constrains. To evaluate the performance of the proposed CCCP-based algorithm, we later design a direct algorithm by exploiting the implicit convexity of the problem. Simulation results demonstrate that the proposed CCCP-based algorithm derives a comparable performance as the direct algorithm, and achieves about 2.57-Mb max-min computation capacity higher compared with the straight flight case, and 8.08-Mb max–min computation capacity higher compared with the case without RIS, which validate the superiority of the joint UAV and RIS design for computation enhancement.

Energy Minimization for Intelligent Reflecting Surface-Assisted Mobile Edge Computing

Intelligent reflecting surface (IRS) has been increasingly considered in mobile edge computing (MEC), assisting smart terminals (STs) in offloading computationally-intense tasks to base stations (BSs). This paper presents a new IRS-assisted MEC framework, which jointly optimizes the local CPU frequencies of the STs, the receive beamformers of the BS, the ST offloading schedules, and the IRS phase configuration, to minimize the energy consumption of the STs. To this end, we reveal that the optimal CPU frequency is time-invariant for each ST. Under flat-fading channels, the IRS phases and the receive beamformers of the BS can be then decoupled from the offloading schedules. Based on this structure, we develop an alternating optimization to solve the IRS phase configuration and the receive beamformers, and then exploit the Lagrange duality method to solve the offloading schedules. We prove that the overall algorithm is guaranteed to compute a stationary point solution for the problem of interest with a low complexity. Under frequency-selective channels, we also develop a new alternating optimization algorithm to minimize the energy consumption, where manifold optimization is leveraged to effectively solve the IRS phase shifts. Numerical results show that the proposed algorithms are superior to existing techniques in terms of energy efficiency under both flat-fading and frequency-selective channels.

Multi-Agent Deep Reinforcement Learning-Based Partial Task Offloading and Resource Allocation in Edge Computing Environment

In the dense data communication environment of 5G wireless networks, with the dramatic increase in the amount of request computation tasks generated by intelligent wireless mobile nodes, its computation ability cannot meet the requirements of low latency and high reliability. Mobile edge computing (MEC) can utilize its servers with mighty computation power and closer to tackle the computation tasks offloaded by the wireless node (WN). The physical location of the MEC server is closer to WN, thereby meeting the requirements of low latency and high reliability. In this paper, we implement an MEC framework with multiple WNs and multiple MEC servers, which consider the randomness and divisibility of arrival request tasks from WN, the time-varying channel state between WN and MEC server, and different priorities of tasks. In the proposed MEC system, we present a decentralized multi-agent deep reinforcement learning-based partial task offloading and resource allocation algorithm (DeMADRL) to minimize the long-term weighted cost including delay cost and bandwidth cost. DeMADRL is a model-free scheme based on Double Deep Q-Learning (DDQN) and can obtain the optimal computation offloading and bandwidth allocation decision-making policy by training the neural networks. The comprehensive simulation results show that the proposed DeMADRL optimization scheme has a nice convergence and outperforms the other three baseline algorithms.

Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.

Transaction Throughput Optimization for Integrated Blockchain and MEC System in IoT

The integration of blockchain and mobile edge computing (MEC), as a secure, efficient, and reliable edge computing paradigm, has been widely applied in many applications, such as large-scale Internet of Things (IoT), Internet of Vehicles (IoV), and smart grid. However, due to the restricted transaction throughput of blockchain, the combination of blockchain and MEC in most existing works cannot support applications with frequent transaction requirements. In this paper, we propose an integrated blockchain and MEC (IBM) framework based on a space-structured ledger to meet the transaction demands for IoT applications. In the framework, a collaborative mining process is designed, where we consider the cooperation between mobile devices (MDs) and MEC servers. To promote mining efficiency, we further develop a high-performance consensus mechanism called reputation-based proof of work (Re-PoW), in which differentiated mining targets are assigned according to the reputation of MDs. In the Re-PoW consensus mechanism, heterogeneous capabilities and historical behaviors of MDs are all considered for accurately evaluating their reputation. In addition, we present an alternating optimization algorithm by jointly optimizing bandwidth allocation and computation resource allocation to further enhance the performance of the proposed scheme. Simulation results show that the proposed approach can achieve significant throughput improvement.

A Network Function Parallelism-enabled MEC Framework for Supporting Low-Latency Services

Mobile edge computing (MEC) enables users to offload computing tasks to edge servers for provisioning low-latency and computation-intensive services. To manage heterogeneous resources and improve service flexibility, MEC is entailed by new technologies, \textit{i.e.}, software defined networking (SDN) and network function virtualization (NFV), which allow services running on common commodity hardware instead of proprietary hardware. However, data processing via software on commodity servers may induce high latency due to limited processing capacity, which impedes the quality of service. Meanwhile, MEC is a resource-sharing system and thus fairness should be considered. In this paper, we propose a network function parallelism (NFP)-enabled MEC (NFPMec) framework for supporting low-latency services. To reap the potential benefits of the NFPMec, we formulate the fairness-aware throughput maximization problem (FTMP) with aim of maximizing the fairness-aware system throughput while satisfying the QoS requirements. We propose a relaxation-based generalized benders algorithm (RGBA) to decouple the FTMP into two sub-problems based on the non-linear convex duality theory. After relaxation, the sub-problems are solved by the Karush-Kuhn-Tucker (KKT) approach. The convergence of the RGBA is theoretically proved. The simulation results demonstrate that the proposed NFPMec outperforms SDN-enabled MEC networks in terms of resource utilization, service latency and system throughput.

Cooperative Offloading and Resource Management for UAV-Enabled Mobile Edge Computing in Power IoT System

The lack of the computation services in remote areas motivates power Internet of Things (IoT) to apply unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) technology. However, the computation services will be significantly affected by the UAVs' capacities, and distinct power IoT applications. In this paper, we firstly propose a cooperative UAV-enabled MEC network structure in which the UAVs are able to help other UAVs to execute the computation tasks. Then, a cooperative computation offloading scheme is presented while considering the interference mitigation from UAVs to devices. To maximize the long-term utility of the proposed UAV-enabled MEC network, an optimization problem is formulated to obtain the optimal computation offloading decisions, and resource management policies. Considering the random devices' demands and time-varying communication channels, the problem is further formulated as a semi-Markov process, and the deep reinforcement learning based algorithms are proposed in both of the centralized and distributed UAV-enabled MEC networks. Finally, we evaluate the performance of the proposed DRL-based schemes in the UAV-enabled MEC framework by giving numerical results.

Unmanned-Aerial-Vehicle-Assisted Computation Offloading for Mobile Edge Computing Based on Deep Reinforcement Learning

Users in heterogeneous wireless networks may generate massive amounts of data that are delay-sensitive or require computation-intensive processing. Owing to computation ability and battery capacity limitations, wireless users (WUs) cannot easily process such data in a timely manner, and mobile edge computing (MEC) is increasingly being used to resolve this issue. Specifically, data generated by WUs can be offloaded to the MEC server for processing, which has greater computing power than WUs. However, as the location of MEC servers is fixed, unmanned aerial vehicles (UAVs) have been considered a promising solution in heterogeneous wireless networks. In this study, we design an UAV-assisted computation offloading scheme in an MEC framework with renewable power supply. The proposed model considers the instability of energy arrival, stochastic computation tasks generated by WUs, and a time-varying channel state. Owing to the complexity of the state, it is difficult to use traditional Markov decision process (MDP) with complete prior knowledge for offloading optimization. Accordingly, we propose UAV-assisted computation offloading for MEC based on deep reinforcement learning (UACODRL) to minimize the total cost, which is the weighted sum of the delay, energy consumption, and bandwidth cost. We first use the K-Means algorithm for classification to reduce the dimension of the action space. Subsequently, we use UACODRL to find the near-optimal offloading scheme to minimize the total cost. Simulations demonstrate that UACODRL converges satisfactorily and performs better than four baseline schemes with different parameter configurations.

Computation Migration and Resource Allocation in Heterogeneous Vehicular Networks: A Deep Reinforcement Learning Approach

With the development of 5G technology, the requirements for data communication and computation in emerging 5G-enabled vehicular networks are becoming increasingly stringent. Computation-intensive or delay-sensitive tasks generated by vehicles need to be processed in real time. Mobile edge computing (MEC) is an appropriate solution. Wireless users or vehicles can offload computation tasks to the MEC server due to it has strong computation ability and is closer to the wireless users or vehicles. However, the communication and computation resources of the single MEC are not sufficient for executing the continuously generated computation-intensive or delay-sensitive tasks. We consider migrating computation tasks to other MEC servers to reduce the computation and communication pressure on current MEC server. In this article, we construct an MEC-based computation offloading framework for vehicular networks, which considers time-varying channel states and stochastically arriving computation tasks. To minimize the total cost of the proposed MEC framework, which consists of the delay cost, energy computation cost, and bandwidth cost, we propose a deep reinforcement learning-based computation migration and resource allocation (RLCMRA) scheme that requires no prior knowledge. The RLCMRA algorithm can obtain the optimal offloading and migration policy by adaptive learning to maximize the average cumulative reward (minimize the total cost). Extensive numerical results show that the proposed RLCMRA algorithm can adaptively learn the optimal policy and outperform four other baseline algorithms.

Energy consumption optimisation based on mobile edge computing in power grid internet of things nodes

With deployment and application of the power grid internet of things (IoT), all the nodes need more and more resources which bring a great challenge to the power grid system. Mobile edge computing (MEC) and rational use of renewable energy in the grid are effective way to solve above problem of grid resource allocation. In this paper, we propose a MEC framework and an offloading policy that considers renewable energy. The model fully considers the stochasticity of renewable energy arrival randomness and task offloading. By analysing the renewable energy's dedicate character and adopt alternating direction method of multipliers (ADMM) algorithm, the method introduce a task computing offloading method based on renewable energy is proposed. The algorithm realises the optimisation strategy of the power grid internet node delay and grid energy consumption minimisation. The simulation results show that the proposed algorithm performance exceeds the two baseline computation and offloading strategies, which can effectively optimise the grid energy consumption of the nodes.

Offloading Schemes in Mobile Edge Computing With an Assisted Mechanism

Mobile edge computing (MEC) is a promising paradigm for providing computing and storage capabilities in close proximity to mobile devices. To solve the scenario in which massive mobile devices have tasks to be processed at the same time, this paper proposes an assisted mechanism for the MEC system. When the primary MEC server is unable to meet the delay requirements of the mobile devices within its coverage area, a portion of the tasks can be offloaded to secondary MEC servers to obtain extra resources for processing. This MEC framework effectively reduces the computing and communication burden of the primary MEC server and improves the resource utilization of the secondary MEC servers. To maximize the system offloading utility in terms of latency, we formulated an optimization problem that jointly optimizes the task assignment, computing resource allocation and offloading decision of all mobile devices. Since the formulated problem is a mixed integer nonlinear problem, we use the decomposition method to convert the optimization problem into several subproblems. In addition, a heuristic algorithm based on the priorities of mobile devices and the MEC servers is proposed to obtain the suboptimal device offloading strategy. The numerical results show that the assisted mechanism can effectively reduce system latency and improve system reliability. In addition, the performance of our proposed algorithm is close to the optimal solution.

Energy consumption optimisation based on mobile edge computing in power grid internet of things nodes

With deployment and application of the power grid internet of things (IoT), all the nodes need more and more resources which bring a great challenge to the power grid system. Mobile edge computing (MEC) and rational use of renewable energy in the grid are effective way to solve above problem of grid resource allocation. In this paper, we propose a MEC framework and an offloading policy that considers renewable energy. The model fully considers the stochasticity of renewable energy arrival randomness and task offloading. By analysing the renewable energy's dedicate character and adopt alternating direction method of multipliers (ADMM) algorithm, the method introduce a task computing offloading method based on renewable energy is proposed. The algorithm realises the optimisation strategy of the power grid internet node delay and grid energy consumption minimisation. The simulation results show that the proposed algorithm performance exceeds the two baseline computation and offloading strategies, which can effectively optimise the grid energy consumption of the nodes.

EEDOS: an energy-efficient and delay-aware offloading scheme based on device to device collaboration in mobile edge computing

Device to device (D2D) communication and mobile edge computing (MEC) are two promising technologies in fifth generation (5G) cellular mobile communication. Besides MEC, a new task offloading technique attracts the attention as D2D collaboration. However, there is lack of integrated D2D and MEC framework to address the energy and delay costs in a joint approach. This work, proposes an energy efficient and delay-aware offloading scheme (EEDOS) based on D2D collaboration in MEC. In EEDOS, mobile devices can offload their task to the MEC or an idle mobile device in their proximity. The task execution and offloading to the MEC or an idle nearby device is formulated, and the optimization problem is defined. The whole process of allocating proper offloading destination is designed in the edge server. EEDOS, classifies offloading requests according to the deadline and energy constraint of requesting device. Then, it finds the proper offloading destination by utilising the maximum matching with minimum cost graph algorithm. Through simulation, we show that EEDOS achieves 95 percent of energy efficiency in comparison of no-offloading task execution and outperforms existing studies in term of energy efficiency with an improved delay in task execution. Moreover, EEDOS is capable of performing more successful task offloading and requires less edge server resources.

An Air-Ground Integration Approach for Mobile Edge Computing in IoT

Mobile edge computing (MEC), which enables delay-sensitive computing tasks to be executed at network edges, has been proposed to accommodate the explosive growth of the Internet of Things. However, the growing demands for massive connectivity, ultra-low latency, and high reliability in various emerging resource-hungry and computation-intensive applications pose new challenges in network access capacity. This motivates us to conceive a new MEC framework from an air-ground integration perspective. First, we present a review of the state-of-the-art literature. Then the architecture and technological benefits of the proposed framework are elaborated. Next, four typical use cases are introduced, and a case study is conducted to demonstrate the significant performance improvements in computation capability and communication connectivity based on real-world road topology. Finally, we present challenges and research directions, and conclude this article.

Full-Duplex Aided User Virtualization for Mobile Edge Computing in 5G Networks

Driven by the increasing demand of intensive computing services and the resource limitation of mobile devices at the edge of mobile networks, mobile edge computing (MEC) is concerned with being an emerging paradigm towards the 5G communications. A main issue of MEC is the coordination of communication, computation, and storage. In this paper, we propose a novel MEC framework with a user virtualization scheme in the software-defined network virtualization cellular network, in which radio resources are virtualized along with computation and storage resources to cooperatively finish MEC services. Besides, we introduce the user virtualization assisted by the full-duplex communication which can extend the radio resource of wireless networks and provide the potential to increase system performance. Moreover, enabled by user virtualization, users can offload the edge computation tasks directly to the MEC server implemented at infrastructure providers or via the virtualized mobile device attributed to different mobile virtual network operators (MVNOs). Under this MEC framework, we formulate the virtual resource allocation as a joint optimization problem. A distributed resource allocation algorithm based on an alternating direction method of multipliers is proposed which can reduce computational complexity and signaling overhead. We evaluate the proposed algorithm through extensive simulations, and the results show that the total utility of MVNOs can be improved significantly which benefited from user virtualization.

Communication-Constrained Mobile Edge Computing Systems for Wireless Virtual Reality: Scheduling and Tradeoff

Mobile edge computing (MEC) is expected to be an effective solution to deliver virtual reality (VR) videos over wireless networks. In contrast to previous computation-constrained MEC, which reduces the computation-resource consumption at the mobile device by increasing the communication-resource consumption, we develop a communications-constrained MEC framework to reduce communication-resource consumption by fully exploiting the computation and caching resources at the mobile VR device in this paper. Specifically, according to a task modularization, the MEC server only delivers the components which have not been stored in the VR device, and then the VR device uses the received components and other cached components to construct the task, yielding low communication cost but high delay. The MEC server also computes the task by itself to reduce the delay, however, it consumes more communication-resource due to the delivery of entire task. Therefore, we propose a task scheduling strategy to decide which computation model should the MEC server operates to minimize the communication-resource consumption under the delay constraint. Finally, the tradeoffs among communications, computing, and caching are also discussed, and we analytically find that given a target communication-resource consumption, the transmission rate is inversely proportional to the computing ability of mobile VR device.

Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things

Mobile-Edge computing (MEC) is an emerging technology currently recognized as a key enabler for 5G networks. Compatible with current 4G networks, MEC will address many key uses of the 5G system, motivated by the massive diffusion of the Internet of Things (IoT). This article aims to provide a tutorial on MEC technology and an overview of the MEC framework and architecture recently defined by the European Telecommunications Standards Institute (ETSI) MEC Industry Specification Group (ISG) standardization organization. We provide some examples of MEC deployment, with special reference to IoT cases, since IoT is recognized as a main driver for 5G. Finally, we discuss the main benefits and challenges for MEC moving toward 5G.

Cloud Support Data Management Infrastructure for Upcoming Smart Cities

This paper presents a novel large scale data management model for future smart cities. The system is exploiting the emerging cloud computing services availability over the globe by processing the collected data at the edge. To handle the large scale requirements in smart city, we introduce a Mobile Edge Computing (MEC) framework in order to increase the reliability of the deployed applications. MEC is a promising framework to reduce the cloud core utilization as well as to provide applications that require low latency to mobile end users. As near as possible to the location of the end user, MEC relates to the mobile network applications and data stream acceleration through caching and/or compressing of relevant data at the edge of the mobile network. Although MEC is not yet deployed in real-life systems, recent studies discussed some technical details and related concepts. This paper studies the definition of MEC and similar concepts with respect to typical application scenarios, like in smart cities, and provides scope and limitations that may be encountered when implementing and deploying MEC. Then, a MEC framework is used to explore the data management infrastructure for upcoming smart cities.