Performance Of Mobile Edge Computing Research Articles

An online energy-saving offloading algorithm in mobile edge computing with Lyapunov optimization

Online computing offloading is an effective method to enhance the performance of mobile edge computing (MEC). However, existing research ignores the impact of system stability and device priority on system performance during task processing.To address the problem of computing offloading for computing-intensive tasks, an online partial offloading algorithm combining task queue length and energy consumption is proposed without any prior information. Firstly, a queue model of IoT devices is created to describe their workload backlogs and reflect the system stability. Then, using Lyapunov optimization, computing offloading problem is decoupled into two sub-problems by calculating the optimal CPU computing rate and device priority, which can determine the task offloading amount and offloading location to complete resource allocation. Finally, the online partial offloading algorithm based on devices priority is solved by minimizing the value of the drift-plus-penalty function’s upper bound to ensure system stability and reduce energy consumption. Theoretical analysis and the outcomes of numerous experiments demonstrate the effectiveness of the proposed algorithm in minimizing system energy consumption while adhering to system constraints, even in dealing with dynamically varying task arrival rates.

Hybrid learning of predictive mobile-edge computation offloading under differently-aged network states

By offloading computationally demanding applications to edge servers, mobile edge computing (MEC) can alleviate the stringent hardware requirements and save energy consumption of resource-restrained devices. Mobile edge computation offloading (MECO, i.e., optimizing computation offloading and resource allocation) is critical to the performance of MEC. However, the existing study typically assumed the availability of all the network states for the upcoming time slot, not applicable in practical MECO with differently-aged network states (e.g., fast-changing practical wireless channels and instantly observable local task arrivals). This paper proposes a novel hybrid learning approach that optimizes the instantaneous local processing and predictive computation offloading decisions. We establish a new hybrid learning approach for instantaneous local processing and predictive computation offloading decisions by integrating the learning techniques of stochastic gradient descent (SGD) and online convex optimization (OCO). By decomposing the primal problems, the proposed approach can be implemented in a decentralized manner. We prove that the optimality loss resulting from the differently-aged network states can diminish with the decreasing stepsizes of SGD and OCO. Simulation results validate the asymptotic optimality and superiority of the proposed hybrid learning approach, as compared to state-of-the-art (e.g., based on OCO techniques).

Securing Mobile Edge Computing Using Hybrid Deep Learning Method

In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques.

Intelligent UAV planning for task-offloading with limited buffer and multiple computing servers

Dynamically moving Unmanned Aerial Vehicles (UAVs) have emerged as an effective means to significantly enhance the flexibility and transmission performance of mobile edge computing (MEC). However, in practical scenarios, UAVs often face limitations in terms of data storage capacity and computational power. In this paper, a UAV-enabled MEC network with multiple users and multiple edge computing servers is proposed, where the UAV is equipped with limited-size buffers. An optimization problem is formulated to jointly optimize UAV flight trajectories, offload server pairings, task offload ratios, and UAV transmit power to minimize transmission delay, computation delay, and system energy consumption. To tackle the intractable non-convex optimization issue, an intelligent optimization algorithm based on Deep Dueling Double Q-Network (D3QN)-Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed, which is able to efficiently determine the optimal solution. Simulation results demonstrate that our proposed intelligent algorithm exhibits good convergence and achieves a favorable balance between delay and energy consumption.

Deep reinforcement learning enabled UAV-IRS-assisted secure mobile edge computing network

The deployment of intelligent reflecting surfaces (IRS) on dynamically moving unmanned aerial vehicles (UAVs) can enhance the communication performance of mobile edge computing (MEC), improve the system flexibility, and alleviate eavesdropping on air–ground channels. In this paper, an IRS-equipped unmanned aerial vehicle (UAV)-assisted secure MEC network is proposed. By jointly optimizing the Relay-UAV stopping point, IRS-UAV stopping point, IRS reflection coefficients and the task offloading ratio, the objective of our proposed optimization scheme is to minimize the transmission delay and computing delay while considering the secure transmission performance. To solve this non-convex optimization problem with coupled variables, we propose an intelligent optimization algorithm based on dueling double deep Q networks (D3QN)-deep deterministic policy gradient (DDPG) that can efficiently explore the trajectories and a great number of the IRS reflection elements. Simulation results demonstrate that the intelligent algorithm exhibits good convergence and our proposed scheme can achieve a good balance between system consumption and secrecy rate.

Secure intelligent reflecting surface assisted mobile edge computing system with wireless power transfer

In this paper, we study an Intelligent Reflecting Surface (IRS) assisted Mobile Edge Computing (MEC) system under eavesdropping threats, where the IRS is used to enhance the energy signal transmission and the offloading performance between Wireless Devices (WDs) and the Access Point (AP). Specifically, in the proposed scheme, the AP first powers all WDs with the wireless power transfer through both direct and IRS-assisted links. Then, powered by the harvested energy, all WDs securely offload their computation tasks through the two links in the time division multiple access mode. To determine the local and offloading computational bits, we formulate an optimization problem to jointly design the IRS's phase shift and allocate the time slots constrained by the security and energy requirements. To cope with this non-convex optimization problem, we adopt semidefinite relaxations, singular value decomposition techniques, and Lagrange dual method. Moreover, we propose a dichotomy particle swarm algorithm based on the bisection method to process the overall optimization problem and improve the convergence speed. The numerical results illustrate that the proposed scheme can boost the performance of MEC and secure computation rates compared with other IRS-assisted MEC benchmark schemes.

A hybrid GA-PSO strategy for computing task offloading towards MES scenarios.

As a new type of computing paradigm closer to service terminals, mobile edge computing (MEC), can meet the requirements of computing-intensive and delay-sensitive applications. In addition, it can also reduce the burden on mobile terminals by offloading computing. Due to cost issues, results in the deployment density of mobile edge servers (MES) is restricted in real scenario, whereas the suitable MES should be chosen for better performance. Therefore, this article proposes a task offloading strategy under the sparse MES density deployment scenario. Commonly, mobile terminals may reach MES through varied access points (AP) based on multi-hop transmitting mode. The transmission delay and processing delay caused by the selection of AP and MES will affect the performance of MEC. For the purpose of reducing the transmission delay due to system load balancing and superfluous multi-hop, we formulated the multi-objective optimization problem. The optimization goals are the workload balancing of edge servers and the completion delay of all task offloading. We express the formulated system as an undirected and unweighted graph, and we propose a hybrid genetic particle swarm algorithm based on two-dimensional genes (GA-PSO). Simulation results show that the hybrid GA-PSO algorithm does not outperform state-of-the-art GA and NSA algorithms in obtaining all task offloading delays. However, the workload by standard deviation approach is about 90% lower than that of the GA and NSA algorithms, which effectively optimizes the performance of load balancing and verifies the effectiveness of the proposed algorithm.

Dynamic edge computing empowered by reconfigurable intelligent surfaces

In this paper, we propose a novel algorithm for energy-efficient low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end delay. The proposed strategy enables dynamic control of the system, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC.

Structure-Aware Reinforcement Learning for Node-Overload Protection in Mobile Edge Computing

Mobile Edge Computing (MEC) involves placing computational capability and applications at the edge of the network, providing benefits such as reduced latency, reduced network congestion, and improved performance of applications. The performance and reliability of MEC degrades significantly when the edge server(s) in the cluster are overloaded. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings — computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic) but requires an order of magnitude less time to train, outputs easily interpretable policy, and can be deployed in an online manner.

An online auction-based incentive mechanism for soft-deadline tasks in Collaborative Edge Computing

Recently, Collaborative Edge Computing (CEC) has been proposed as an effective method to improve the performance of Mobile Edge Computing (MEC) systems by offloading workload from busy MEC servers to idle ones. Although extensive research has been conducted to study the system design and scheduling algorithms in CEC, the incentive mechanism between end-users and collaborative servers yet receives much less attention. To fill the gap, in this paper, we propose an online auction-based incentive mechanism where the task requests arrive stochastically and MEC servers need to decide whether to accept them without future information. To guarantee the applicability of our mechanism, we consider a comprehensive model where both the valuation of tasks and operation costs of edge servers are represented by general functions. Through theoretical analysis, we formally proved our mechanism achieves desirable properties, such as truthfulness and polynomial time complexity. Based on the primal–dual optimization framework, we further demonstrate the competitive ratio of our mechanism with respect to the optimal offline social welfare. Extensive simulations are conducted to verify our theoretical analysis and validate the effectiveness of our mechanism.

On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-$m$ Fading

In this paper, we study the system performance of mobile edge computing (MEC) wireless sensor networks (WSNs) using a multiantenna access point (AP) and two sensor clusters based on uplink nonorthogonal multiple access (NOMA). Due to limited computation and energy resources, the cluster heads (CHs) offload their tasks to a multiantenna AP over Nakagami-m fading. We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining (SC) or maximal ratio combining (MRC) and each cluster selects a CH to participate in the communication process by employing the sensor node (SN) selection. We derive the closed-form exact expressions of the successful computation probability (SCP) to evaluate the system performance with the latency and energy consumption constraints of the considered WSN. Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas, number of SNs in each cluster, task length, working frequency, offloading ratio, and transmit power allocation. Furthermore, to determine the optimal resource parameters, i.e., the offloading ratio, power allocation of the two CHs, and MEC AP resources, we proposed two algorithms to achieve the best system performance. Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies. We use Monte Carlo simulations to confirm the validity of our analysis.

Quantifying the Influence of Intermittent Connectivity on Mobile Edge Computing

Mobile edge computing (MEC) is a key technology that enables the deployment of applications (or services) at the proximity of mobile users. However, the performance of mobile edge computing is sensitive to the quality and availability of underlying connection links. It is still unclear to what extent intermittent connectivity affects the performance of mobile edge computing. In this paper, we make the first attempt to quantify the influence of intermittent connectivity on mobile edge computing from a theoretical perspective. Specifically, we propose an analytical framework based on discrete-time Markov chain and derive a closed-form expression of the task processing time under different network conditions. Our model can be further extended to account for the case with group task arrivals. We also conduct extensive simulations to examine the accuracy of our proposed analytical models with both synthetic and real-world user mobility traces. The results show that our model can well capture the influence of intermittent connectivity on MEC. Our model sheds important insights into the impact of intermittent connectivity on task processing in MEC, which we believe should be taken into account when designing future MEC systems.

Code Caching-Assisted Computation Offloading and Resource Allocation for Multi-User Mobile Edge Computing

Utilizing the data caching technology to reduce data transmission is a promising technique for improving the performance of mobile edge computing (MEC), because the delay and energy consumption produced by data transmission constitute the dominant cost of task execution in MEC. Besides, computation tasks generally consist of input parameters, executive codes, and computation results. The executive codes are fixed and can output difference computation results under different input parameters. Motivated by this, we consider to proactively cache executive codes of tasks at the MEC server to reduce the weighted sum of task execution delay and users’ energy consumption. Aiming at establishing optimal system design, we formulate the problem as a non-linear programming problem which involves jointly optimizing the executive code caching strategy, computation offloading decision, wireless resource allocation, and computing resource allocation. We propose to find the optimal solution by employing an alternating optimization framework. The optimal wireless resource and computing resource allocation problem are firstly addressed by utilizing convex optimization technology. Then, a dynamic programming-based algorithm has been developed to achieve the optimal executive code caching and computation offloading strategies. Extensive simulation results show that the proposed scheme operates well and can substantially reduce the system cost over other benchmark schemes.

Deploying an efficient and reliable scheduling for mobile edge computing for IoT applications

Mobile Edge Computing (MEC), which is the main technology behind 5th Generation (5G) networks, is a nascent paradigm that meets the demands of IoT (Internet of Things) and localized computing. From an end-user point of view, it can be regarded as a refinement of cloud computing and Fog computing. The transfer of computational tasks to the closet MEC servers leads to energy efficiency and low latency. It is important to apply the most suitable policies for scheduling, especially when configuring different modules of an IoT application in MEC. In this paper, three algorithms, namely the heuristic Bald Eagle Search Optimisation [BESO] algorithm, Particle Swarm Optimization algorithm [PSO], and Genetic Algorithm [GA], are presented to carry out heuristic offloading of computational tasks with a view to improving the latency and performance of MEC. However, the most effective algorithm must be adopted to conduct these tasks. As a result, this paper attempts to find an algorithm that is most appropriate for MEC. To demonstrate this. the three algorithms were tested in the Long-Term Evolution [LTE] based Orthogonal frequency-division multiplexing [OFDM] network during a period when the edge nodes had no adequate resources. The performance and efficiency of the three algorithms, BESO, PSO and GA, were determined and compared. After generating the results, the comparative results were evaluated. In terms of offloading the computational tasks, the BESO algorithm was discovered to perform better, with greater energy efficiency and lower latency, than the other two algorithms.

Analytical Modeling of NOMA-Based Mobile Edge Computing Systems With Randomly Located Users

Non-orthogonal multiple access (NOMA) has been treated as a promising technology to improve the computation offloading performance of mobile edge computing (MEC). A complete understanding of the impact of applying NOMA on MEC is essential for network operators to deploy MEC systems. In this letter, we develop a mathematical framework to analyze the impact of NOMA on offloading decisions in MEC, which captures the interaction among the offloading decisions of multiple co-channel users. Using stochastic geometry and order statistics, we derive the expression for the computation offloading probability of the $n$ -th ranked user in terms of serving distances in a NOMA-based MEC system. Numerical simulations are performed to verify the accuracy of our analysis and demonstrate the performance gain of applying NOMA on MEC.

High-performance flow classification using hybrid clusters in software defined mobile edge computing

Mobile Edge Computing (MEC) provides different storage and computing capabilities within the access range of mobile devices. This moderates the burden of offloading compute/storage-intensive processes of the mobile devices to the centralized cloud data centers. As a result, the network latency is reduced and the quality of service provided for the mobile end users is improved. Different applications benefit from the large-scale deployments of MEC servers. However, the considerable complexity of managing large scale deployments of the sheer number of applications for the millions of mobile devices is a challenge. Recently, Software Defined Networking (SDN) is leveraged to resolve the problem by providing unified and programmable interfaces for managing network devices. Most of the current SDN packet processing services are tightly dependent on the packet classification service. This primary service classifies any incoming packet based on matching a set of specific fields of its header against a flow table. Acceleration of this basic process considerably increases the performance of the SDN-based MEC. In this paper, the hierarchical tree algorithm, which is a packet classification method, is parallelized using popular platforms on a cluster of Graphics Processing Units (GPUs), a cluster of Central Processing Units (CPUs), and a hybrid cluster. The best scenario for the parallel implementation of this algorithm on the CPU cluster is that which combines OpenMP and MPI.In this case, the throughput of the classifier is 4.2 million packets per second (MPPS). On the GPU cluster, two different scenarios have been used. In the first scenario, the global memory is used to store the rules and the Hierarchical-trie of the classifier while in the second scenario we break the filter set in a way that the resulting Hierarchical-trie of each subset could be stored in the shared memory of GPU. According to the results, although the first GPU cluster scenario achieves a throughput of 29.19 MPPS and a speedup 58 times as great as the serial mode, the second scenario is 12 times faster due to using the shared memory. The best performance, however, belongs to the hybrid cluster mode. The hybrid cluster achieves a throughput of 30.59 which is 1.4 MPPS more than the GPU cluster.

Throughput Maximization of NFV-Enabled Multicasting in Mobile Edge Cloud Networks

Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of end users at the mobile network edge. This provides end users with swift and powerful computing and storage capacities, energy efficiency, and mobility- and context-awareness support. Furthermore, Network Function Virtualization (NFV) is another promising technique that implements various network functions for many applications as pieces of software in servers or cloudlets in MEC networks. The provisioning of virtualized network services in MEC can improve user service experiences, simplify network service deployment, and ease network resource management. However, user requests arrive dynamically and different users demand different amounts of resources, while the resources in MEC are dynamically occupied or released by different services. It thus poses a significant challenge to optimize the performance of MEC through efficient computing and communication resource allocations to meet ever-growing resource demands of users. In this paper, we study NFV-enabled multicasting that is a fundamental routing problem in an MEC network, subject to resource capacities on both its cloudlets and links. Specifically, we first devise an approximation algorithm for the cost minimization problem of admitting a single NFV-enabled multicast request. We then develop an efficient algorithm for the throughput maximization problem for the admissions of a given set of NFV-enabled multicast requests. We third devise an online algorithm with a provable competitive ratio for the online throughput maximization problem when NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

Cooperative Computation Offloading and Resource Allocation for Blockchain-Enabled Mobile-Edge Computing: A Deep Reinforcement Learning Approach

Mobile-edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues; 2) cooperative computation offloading; and 3) dynamic optimization. To address the security and privacy issues, we employ the blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this article, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multiobjective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size, and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an asynchronous advantage actor–critic-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. The simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

Efficient Dynamic Service Maintenance for Edge Services

The emergence of many new computing applications, such as Internet of Vehicles (IoV) and smart homes, has been made possible by the large pool of cloud resources and services. However, the cloud computing paradigm is unable to meet the requirements of delay-sensitive business applications, such as low latency, mobility support, and location awareness. In this context, Mobile Edge Computing (MEC) is introduced to improve the quality of experience (QoE) by bringing cloud resources and services closer to the user by leveraging available resources in the edge networks. However, the performance of MEC is dynamic in nature due to its location awareness, mobility and proximity. As a result, an effective mechanism is needed for providing efficient dynamic service maintenance for edge services. In this paper, we propose applying the Skyline Graph Model and employing the Directed Acyclic Graph theory to store and update mobile edge services. Specifically, the Skyline Graph (SG) algorithm is designed to solve the insertion, deletion, updating and searching of mobile edge services to achieve efficient maintenance for edge services. Comprehensive experiments are conducted on both real-world web services and simulated datasets to evaluate the effectiveness and efficiency of our approaches. The results show that our algorithms can achieve significantly better performance and robustness than the baseline algorithm.

Mobile Edge Computing Empowered Fiber-Wireless Access Networks in the 5G Era

The expected stringent requirements of future 5G networks such as ultra-low latency, user experience continuity, and high reliability will drive the need for highly localized services within RANs in close proximity to mobile subscribers. In light of this, the mobile edge computing (MEC) concept has emerged, which aims to unite telco, IT, and cloud computing to deliver cloud services directly from the network edge. To facilitate better understanding of MEC, this article first discusses its potential service scenarios and identifies design challenges of MEC-enabled networks. Given the importance of scaling up research in the area of network integration and convergence in support of MEC toward 5G, the article explores the possibilities of empowering integrated fiber-wireless (FiWi) access networks to offer MEC capabilities. More specifically, envisioned design scenarios of MEC over FiWi networks for typical RAN technologies (i.e., WLAN, 4G LTE, LTE-A HetNets) are investigated, accounting for both network architecture and enhanced resource management. The performance of MEC over Ethernet-based FiWi networks in terms of delay, response time efficiency, and battery life of edge devices is then analyzed. The obtained results demonstrate the feasibility and effectiveness of the proposed MEC over FiWi concept.