Mobile Edge Computing Network Research Articles

Task Partitioning and Offloading in DNN-Task Enabled Mobile Edge Computing Networks

Deep neural network (DNN)-task enabled mobile edge computing (MEC) is gaining ubiquity due to outstanding performance of artificial intelligence. By virtue of characteristics of DNN, this paper develops a joint design of task partitioning and offloading for a DNN-task enabled MEC network that consists of a single server and multiple mobile devices (MDs), where the server and each MD employ the well-trained DNNs for task computation. The main contributions of this paper are as follows: First, we propose a layer-level computation partitioning strategy for DNN to partition each MD's task into the subtasks that are either locally computed at the MD or offloaded to the server. Second, we develop a delay prediction model for DNN to characterize the computation delay of each subtask at the MD and the server. Third, we design a slot model and a dynamic pricing strategy for the server to efficiently schedule the offloaded subtasks. Fourth, we jointly optimize the design of task partitioning and offloading to minimize each MD's cost that includes the computation delay, the energy consumption, and the price paid to the server. In particular, we propose two distributed algorithms based on the aggregative game theory to solve the optimization problem. Finally, numerical results demonstrate that the proposed scheme is scalable to different types of DNNs and shows the superiority over the baseline schemes in terms of processing delay and energy consumption.

Resource Allocation in Decentralized Vehicular Edge Computing Network

Computation-intensive vehicle tasks sharply increase with the rapid growth of intelligent vehicles. The technology of Mobile Edge Computing (MEC) has the possibility of assisting vehicles with computation offloading. To solve the problem of computation resource management and guarantee the security of resource transaction, we jointly combine the MEC network and the blockchain networks to build a blockchain based MEC offloading model. The non-cooperative interactions between MEC server and vehicles formulate a two-stage Stackelberg game in an aim to maximize their benefits and information security. We theoretically demonstrate the unique existence of Nash equilibrium, which enables participants to decide their optimal strategies. Finally, the performance of the proposed model is analyzed by conducting simulation experiments. Our proposed model optimizes resource allocation and also improves the security of the whole network.

A parallel computing based model for online binary computation offloading in mobile edge computing

Mobile-edge computing (MEC) with wireless power transfer has recently emerged as a viable concept for improving the data processing capacity of limited powered networks like wireless sensor networks (WSN) and the internet of things (IoT). In this research, we explore a wireless MEC network with a binary offloading strategy. Each mobile device’s (MDs) computation task is either performed locally or entirely offloaded to a MEC server. We aim to develop an online system that adapts task offloading decisions and resource allocations to changing wireless channel conditions in real-time. This necessitates solving difficult combinatorial optimization problems quickly within the channel coherence time, which is difficult to achieve with traditional optimization approaches. To address this issue, we offer a parallel computing architecture in which several parallel offloading actors as deep neural networks (DNNs) are used as a scalable method to learn binary offloading decisions from experience. It avoids the need to solve combinatorial optimization issues, reducing computational complexity significantly, especially in large networks. Compared to existing optimization approaches, numerical results demonstrate that the proposed algorithm can achieve optimal performance while reducing computing time by an acceptable margin. For instance, our algorithm achieves a latency rate of 0.033 s in a network of 30 MDs.

A novel Q-learning-based hybrid algorithm for the optimal offloading and scheduling in mobile edge computing environments

Mobile Edge Computing (MEC) has arisen as a promising computing paradigm consisting of three tiers: Smart Mobile Devices (SMDs), fog nodes, and the cloud. The MEC enables computational offloading and execution schedules to cope with the problems of insufficient resources for the SMDs and the computational tasks' deadlines. The offloading problem determines in what order and source of the network the tasks should be performed to minimize execution time and power consumption. The main aim of the current paper is to reduce execution time and energy consumption by optimizing tasks' offloading and scheduling in MEC networks. As a result, the task scheduling and offloading are modeled as an optimization problem. Then, an enhanced hybridization of Artificial Ecosystem-based Optimization (AEO) and Arithmetic Optimization Algorithm (AOA), named E-AEO-AOA, is presented to optimize it. In the E-AEO-AOA, the AOA and AEO algorithms are initially discretized. Next, the Q-learning strategy is modified and recruited to hybridize the algorithms in a complementary manner. Subsequently, chaos theory is utilized in a local search procedure to enhance the exploitation capability of the E-AEO-AOA. Eventually, the performance of E-AEO-AOA is examined on fifteen MEC networks. In the experiments, the E-AEO-AOA is compared with AEO, AO, AOA, JS, MRFO, STOA, SCA, and TSA algorithms statistically. Besides, the algorithms' convergence rate and solutions dispersity are visually compared. Moreover, the algorithms are compared by the Wilcoxon signed-rank test. The experimental results indicate that the E-AEO-AOA surpassed competitor algorithms in 90% of cases. Likewise, in 6% of the cases, the E-AEO-AOA produced the same results as AEO, AOA and MRFO.

Service Home Identification of Multiple-Source IoT Applications in Edge Computing

The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one without the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.

Heuristic Algorithm for Resource Allocation in an Internet of Battle Things

Internet of Things has been experiencing unprecedented deployment from the civilian to the military ones like the Internet of battle Things (IoBT). Different from the civilian applications, the presence of various electronic countermeasures (ECM) in IoBT would destroy the infrastructure of a network, making the mobile edge computing (MEC) with quality of service (QoS) guaranteed in such a resource-limited IoBT more challenging. To improve the QoS of mobile users under an ECM environment, we address the optimization scheme in an MEC network with heterogeneous edge servers, considering that the connections in a network might be destroyed and therefore switch to the other connections for data transmission due to ECM. Mathematically, we characterize the optimization of wireless resource allocation as a mixed-integer nonlinear problem to maximize the relative improvement in task computation time and energy consumption. We decompose the original problem into a few suboptimization problems and propose a heuristic algorithm that can achieve a suboptimal solution in polynomial time by solving this NP-hard problem. The analytic results show that our proposed scheme can save task computation time and energy consumption over traditional approaches under an ECM environment.

Joint Optimization of Multi-User Partial Offloading Strategy and Resource Allocation Strategy in D2D-Enabled MEC

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least.

Deep Q-Learning based resource allocation and load balancing in a mobile edge system serving different types of user requests

Abstract With the expansion of the communicative and perceptual capabilities of mobile devices in recent years, the number of complex and high computational applications has also increased rendering traditional methods of traffic management and resource allocation quite insufficient. Recently, mobile edge computing (MEC) has emerged as a new viable solution to these problems. It can provide additional computing features at the edge of the network and allow alleviation of the resource limit of mobile devices while increasing the performance for critical applications especially in terms of latency. In this work, we addressed the issue of reducing the service delay by choosing the optimal path in the MEC network, which consists of multiple MEC servers that has different capabilities, applying network load balancing where multiple requests need to be handled simultaneously and routing selection based on a deep- Q network (DQN) algorithm. A novel traffic control and resource allocation method is proposed based on deep Q-learning (DQL) which allows reducing the end-to-end delay in cellular networks and in the mobile edge network. Real life traffic scenarios with various types of user requests are considered and a novel DQL resource allocation scheme which adaptively assigns computing and network resources is proposed. The algorithm optimizes traffic distribution between servers reducing the total service time and balancing the use of available resources under varying environmental conditions.

JUTAR: Joint User-Association, Task-Partition, and Resource-Allocation Algorithm for MEC Networks.

Mobile edge computing (MEC) is a promising technique to support the emerging delay-sensitive and compute-intensive applications for user equipment (UE) by means of computation offloading. However, designing a computation offloading algorithm for the MEC network to meet the restrictive requirements towards system latency and energy consumption remains challenging. In this paper, we propose a joint user-association, task-partition, and resource-allocation (JUTAR) algorithm to solve the computation offloading problem. In particular, we first build an optimization function for the computation offloading problem. Then, we utilize the user association and smooth approximation to simplify the objective function. Finally, we employ the particle swarm algorithm (PSA) to find the optimal solution. The proposed JUTAR algorithm achieves a better system performance compared with the state-of-the-art (SOA) computation offloading algorithm due to the joint optimization of the user association, task partition, and resource allocation for computation offloading. Numerical results show that, compared with the SOA algorithm, the proposed JUTAR achieves about 21% system performance gain in the MEC network with 100 pieces of UE.

Mobility-Aware Offloading and Resource Allocation Strategies in MEC Network Based on Game Theory

With the emergence and development of communication technology and new computing paradigm named mobile edge computing (MEC), fast response and ultralow latency are given higher requirements. Nevertheless, due to the low penetration and coverage of the MEC network, it is difficult to guarantee the large-scale connection needs of all user groups in industry 4.0. In addition, user mobility is closely related to the network connection between edge nodes (ENs) and mobile devices (MDs) in industry 4.0, the frequent mobility of MDs makes the computation offloading process not smooth and the channel unstable, which can reduce the network performance. Hence, this paper constructs an edge network environment for MEC-based industrial internet of things (IIoT), considering the combined benefits of energy consumption, time delay, and computing resource cost to tackle the aforementioned problem by maximizing the utility of the entire system. In order to solve this problem, this paper proposes a mobility-aware offloading and resource allocation scheme (MAORAS). This scheme first employs the Lagrange multiplier method to solve the problem of computing resource allocation; then, a noncooperative game between MDs is established and the existence of Nash equilibrium (NE) has been proven. Simulation results demonstrate that the practical performance of the MAORAS optimization scheme could improve the system utility significantly.

Reinforcement Learning Based Energy-Efficient Collaborative Inference for Mobile Edge Computing

Collaborative inference in mobile edge computing (MEC) enables mobile devices to offload the computation tasks for the computation-intensive perception services, and the inference policy determines the inference latency and energy consumption. The optimal inference policy depends on the inference performance model of deep learning, the data generation model and the network model that are rarely known by mobile devices in time. In this paper, we propose a multi-agent reinforcement learning (RL) based energy-efficient MEC collaborative inference scheme, which enables each mobile device to choose both the partition point of deep learning and the collaborative edge of each mobile device based on the image quantity, the channel conditions and the previous inference performance. A learning experience exchange mechanism exploits the Q-values of the neighboring mobile devices to accelerate the inference policy optimization with less energy consumption. We also provide a deep multi-agent RL based inference scheme to accelerate learning for large-scale MEC networks, in which an actor network yields the collaborative inference policy probability distribution and a critic network guides the weight update of the actor network to enhance sample efficiency. We provide the inference performance bound and analyze the computational complexity. Both simulation and experimental results show that our proposed schemes reduce the inference latency and save the MEC energy consumption.

On the application of uplink/downlink decoupled access in heterogeneous mobile edge computing

Mobile edge computing (MEC) is a key player in low latency 5G networks with the task to resolve the conflict between computationally-intensive mobile applications and resource-limited mobile devices (MDs). As such, there has been intense interest in this topic, especially in multi-user single-server and homogeneous multi-server scenarios. However, the research in the heterogeneous multi-server scenario is limited, where the servers are located at small base-stations (SBSs), macro base-stations (MBSs), or the cloud with different computing and communication capabilities. On the other hand, computational-tasks offloading is limited by the type of MD-BS association with almost all previous works focusing on offloading the MD’s computational tasks to the MEC servers/cloudlets at its serving BS. However, in multi-BS association, or downlink/uplink decoupled (DUDe) scenarios, an MD can be served by multiple BSs and hence has multiple offloading choices. Motivated by this, we proposed a joint BS association and subchannel allocation algorithm based on a student-project allocation (SPA) matching approach to minimize the network sum-latency, which breaks the constraint that one MD must connect to the same BS in the UL and DL, and jointly consider the communication and computational disparity of SBS and MBS cloudlets in heterogeneous MEC networks. Moreover, an optimal power allocation scheme is proposed to optimize the system performance subject to the predefined quality of service constraints. Our results show that the proposed scheme is superior to benchmark techniques in enabling the effective use of computational and communication resources in heterogeneous MEC networks.

Joint Optimization of DNN Partition and Continuous Task Scheduling for Digital Twin-Aided MEC Network With Deep Reinforcement Learning

Nowadays, mobile application services face the challenges of high speed, low latency and high reliability. The combination of digital twin (DT) technology and mobile edge computing (MEC) network can effectively solve these challenges. DT technology can help MEC network monitor and predict the network states. In this paper, we propose a DT-aided MEC network scenario with deep neural network (DNN) inference as the computing task of end devices (EDs). ED can offload part of DNN layers to MEC server. To allocate communication resources, we propose an algorithm based on asynchronous advantage actor-critic (A3C), which manages the transmission power and channel selection of EDs. Since DNN inference is continuous in real scenes, we consider the continuous DNN inference tasks. We convert the DNN optimal partition point solving problem to a min st-cut problem, and propose a graph theory based DNN optimal partition point solving algorithm to minimize the inference latency. Simulation results show that the proposed algorithm can effectively reduce the inference latency. Compared with actor-critic (AC) and deep Q network (DQN), the proposed algorithm has faster convergence speed and better convergence value. Compared with the traditional one-time DNN model partition algorithm, the proposed algorithm is more suitable for DNN continuous task arrival scenario.

HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues – especially when multiple users issue concurrent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FL requests</i> . Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications.

Budget-Aware User Satisfaction Maximization on Service Provisioning in Mobile Edge Computing

Mobile Edge Computing (MEC) promises to provide mobile users with delay-sensitive services at the edge of network, and each user service request usually is associated with a Service Function Chain (SFC) requirement that consists of Virtualized Network Functions (VNFs) in order. The satisfaction of a user on his requested service is heavily impacted by the service reliability. In this paper, we study user satisfaction on services provided by an MEC network through introducing a submodular function based metric to measure user satisfaction. We first formulate a novel user satisfaction problem with the aim to maximize the accumulative user satisfaction, assuming that all available computing resource in the MEC network can be used for service reliability enhancement. We show that the problem is NP-hard, and devise an approximation algorithm with a provable approximation ratio for it. We then consider the problem under a given computing resource budget constraint, for which we devise an approximation algorithm with a provable approximation ratio, at the expense of moderate budget violations. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, improving the performance by more <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$16.1\%$</tex-math></inline-formula> in comparison with the baseline algorithms.

Two Time-Scale Energy and Spectrum Allocation for MEC Networks with Hybrid Energy Supplies

Integrating power grid and renewable energy to charge mobile-edge computing (MEC) server is a promising solution for green networks, which can reduce the energy consumption of power grid and carbon emission. Intelligent management of energy and bandwidth resources in the two time-scale framework can increase the energy efficiency and network revenue in MEC networks with heterogeneous energy supplies and multiple mobile users (MUs). In this paper, we investigate the joint energy management (for workloads offloading and execution) and bandwidth allocation (for workloads transmission) problem that maximizes the network revenue in the MEC network with hybrid energy supplies. We formulate it into a challenging nonlinear optimization problem because of the data randomness and the temporal coupling effect. Based on the Lyapunov optimization approach with quick convergence speed and low complexity, we propose an online algorithm to overcome the obstacles by relaxing the battery constraints, allowing us to achieve close-to-optimal maximum network revenue with the stability of battery level and different throughput requirements of MUs. Extensive numeral simulations verify the theoretical analysis and demonstrate the superior performance of the proposed algorithm in different algorithms and scenarios.

A SURVEY ON MOBILITY-AWARE AND DELAY-SENSITIVE SERVICE PROVISIONING IN MOBILE EDGE-CLOUD NETWORKS

Mobile edge computing (MEC) has emerged as a viable technique for pushing the cloud frontier to the network edge, enabling network services to be provisioned in close proximity to mobile consumers. Serving customers at the edge of the cloud can minimize service latency, lower operational costs, and increase network resource availability. Along with MEC, network function virtualization (NFV) is a viable strategy for implementing various network service functions as software in cloudlets (servers or clusters of servers). Giving mobile consumers virtualized network services can improve their service experience, simplify network service deployment, and make network resource management easier. However, mobile users roam arbitrarily inside networks, and different users often request various services with varying resource needs and latency requirements. As a result, it is a significant issue to provide dependable and smooth virtualized network services for mobile users in a MEC network while satisfying their specific latency needs, subject to network resource limits. This research focuses on the supply of virtualized network function services for mobile users in MEC while taking user mobility and service latency requirements into mind. We begin by posing two novel optimization issues for user service request admissions, with the goals of maximizing accumulative network utility and accumulative network throughput over a certain time horizon. Then, for the utility maximization issue, we design a constant approximation approach. We also create an online method for the problem of accumulative throughput maximization. Finally, we use experimental simulations to assess the performance of the suggested methods. The results of the experiments show that the proposed algorithms are promising.

Network Slicing for NOMA-Enabled Edge Computing

The 5G network presents a new horizon with tremendous opportunities for future generation wireless networks. Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing (NS) are some of the key enablers for 5G. MEC reduces the latency to a great extent for a wireless network, while NOMA gives access to more users with resource constraints. NS provides users with a better quality of service and network operators with more flexibility. In this work, we propose an NS technique enabled with NOMA for a MEC network. The proposed NS technique improves service latency for MEC users and reduces the unnecessary allocation of radio resources in NOMA. The saved resources can be leveraged to accommodate more users, thus increasing the spectral efficiency of the network. We consider different types of services based on the task completion time of users in this work. The primary focus is to optimize the total energy consumption for wireless uplink transmission for the NOMA-enabled sliced MEC network. We also propose a heuristic algorithm as an alternative to reduce the time and computational complexities of the optimization algorithm and simulate the results extensively to show the effectiveness of our proposed algorithm.

Deep Reinforcement Learning Based Resource Allocation in Multi-UAV-Aided MEC Networks

Resource allocation for mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks has been a popular research issue. Different from existing works, this paper considers a multi-UAV-aided uplink communication scenario and investigates a resource allocation problem of minimizing the total system latency and the energy consumption, subject to constraints on transmit power of mobile users (MUs), system latency caused by transmission and computation. The problem is confirmed to be a challenging time-series mixed-integer non-convex programming problem, and we propose a joint UAV Movement control, MU Association and MU Power control (UMAP) algorithm to solve it effectively, where three sub-problems are optimized iteratively. Specifically, UAV movement and MU association are optimized utilizing deep reinforcement learning (DRL) to decrease the energy consumption and system latency. Next, a closed-form solution of the MU transmit power is derived. Finally, simulation results show that the UMAP algorithm can significantly decrease the system latency and energy consumption and increase the coverage rate compared with benchmark algorithms.

Task Co-Offloading for D2D-Assisted Mobile Edge Computing in Industrial Internet of Things

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.