Mobile Edge Computing System Research Articles

STAR-RIS-Aided Mobile Edge Computing: Computation Rate Maximization With Binary Amplitude Coefficients

In this paper, simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) is investigated in the multi-user mobile edge computing (MEC) system to improve the computation rate. Compared with traditional RIS-aided MEC, STAR-RIS extends the service coverage from half-space to full-space and provides new flexibility for improving the computation rate for end users. However, the STAR-RIS-aided MEC system design is a challenging problem due to the non-smooth and non-convex binary amplitude coefficients with coupled phase shifters. To fill this gap, this paper formulates a computation rate maximization problem via the joint design of the STAR-RIS phase shifts, reflection and transmission amplitude coefficients, the receive beamforming vectors, and energy partition strategies for local computing and offloading. To tackle the discontinuity caused by binary variables, we propose an efficient smoothing-based method to decrease convergence error, in contrast to the conventional penalty-based method, which brings many undesired stationary points and local optima. Furthermore, a fast iterative algorithm is proposed to obtain a stationary point for the joint optimization problem, with each subproblem solved by a low-complexity algorithm, making the proposed design scalable to a massive number of users and STAR-RIS elements. Simulation results validate the strength of the proposed smoothing-based method and show that the proposed fast iterative algorithm achieves a higher computation rate than the conventional method while saving the computation time by at least an order of magnitude. Moreover, the resultant STAR-RIS-aided MEC system significantly improves the computation rate compared to other baseline schemes with conventional reflect-only/transmit-only RIS.

Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System

Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System

Robust Task Offloading and Resource Allocation in Mobile Edge Computing With Uncertain Distribution of Computation Burden

In mobile edge computing (MEC) supporting multiple mobile users (MUs), it is essential to optimize the offloading policy and communication and computation resource allocation. A main challenge is that the computation burden of a computation task may be random and even with uncertain probabilistic distribution. To address this challenge, we investigate a multiple-MU MEC system with random computation burden. For the random computation burden of an MU, only the mean and variance are known, but its distribution is unknown. Robustness is provided such that computation outage probabilities (due to uncertain distribution of computation burden) are bounded by a predefined threshold. We minimize the weighted sum of the MUs’ energy consumption. The formulated optimization problem is non-deterministic and non-convex, and thus, is hard to solve. To deal with the challenge, we transform the formulated problem into a deterministic and convex problem by applying the Chebyshev-Cantelli inequality and some mathematical manipulations. We further decompose the convex problem to a lower-level and an upper-level problem. Low-complexity algorithms are developed for the lower-level and upper-level problems. The overall complexity of our proposed method is linear with the number of MUs.

Red Deer Optimisation-Based Delay Minimisation for mmWave Communication System Enabled with Mobile Edge Computing

Mobile edge computing (MEC)-oriented solutions are essential for various 5G wireless communication systems. It is considered a key technology for future communication systems because of its capability for fulfilling a broad variety of necessities of the developing wireless terminals in the form of Intelligent Vehicles, augmented reality and virtual reality devices like huge computation, low latency and high data rate. Further, the resource package has made the research on mobile data offloading. One probable novel spectrum in the subsequent generation networks is the millimetre wave (mmWave) communication systems. It attains important attention because of its high rate. This research work, this area focuses on the delay of minimisation strategy in mmWave MEC by jointly optimising the hybrid beam-forming and also resource allocation. Here, the utilisation of a well-performing Red Deer Algorithm (RDA) is the ultimate aim of the suggested model that intends to optimise the analogue beam-forming vectors at the users, the analogue and digital beam-forming matrices at the Base Station (BS), the computation task offloading ratios and resource allocation at the MEC server. Here, the minimisation of the delay or latency is attained. The comparative analysis of the proposed model over the other models demonstrates the superiority of the proposed algorithm in assisting the mmWave MEC system.

Computation bits maximization in data compression-assisted MEC networks

In this article, we study the weighted sum computation bits (WSCB) maximization for a multiuser mobile edge computing (MEC) system while considering data compression at each edge user (EU). Considering the partial offloading scheme and data compression at each EU, we formulate a WSCB maximization problem by jointly optimizing the compression time, the computing and compression frequencies of all the EUs as well as the transmit power and time of EUs when performing task offloading. Since the formulated problem is non-convex and difficult to solve, we first replace the original problem with an equivalent one by using the proof by contradiction. Subsequently we introduce auxiliary variables for transforming the equivalent problem into a convex one that can be optimally solved by using the existing convex tools. Further, we devise the closed-form expressions for the optimal transmit power, compression and computing frequencies at each EU by using Karush-Kuhn–Tucker (KKT) conditions. Simulation results verify that our proposed scheme is better than several baseline schemes from the WSCB perspective.

Dynamic adaptive workload offloading strategy in mobile edge computing networks

With the increasing popularity of mobile devices and explosive growth of task processing requirements, edge computing attracts more attention from researchers nowadays since it can improve the QoS and utilize resources of the cloudlets, including mobile devices and base stations, as much as possible. In a Mobile Edge Computing (MEC) network, the workload offloading problem is quite important since it directly influences the latency of the task processing, and many efficient algorithms have been proposed to deal with it. However, most of the existing algorithms only consider the static or quasi-static scenario, and are not suitable for a system with several fast-moving devices. Since that more and more mobile devices with high speed are involved in a MEC system, a new workload offloading strategy is required to adapt to such a dynamic scenario. In this paper, we sufficient consider the dynamic properties of a MEC system, and proposed a dynamic adaptive workload offloading algorithm based on Lyapunov theories and an FC-LSTM based schedule determining algorithm to balance the workload of different cloudlets and minimize the weighted average energy and time consumption of mobile devices. Theoretical analysis and extensive experimental results show that all the proposed algorithms achieve high performance in terms of energy consumption, convergence and latency.

Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

Multiagent Meta-Reinforcement Learning for Optimized Task Scheduling in Heterogeneous Edge Computing Systems

Mobile edge computing (MEC) brings the potential to address the ever increasing computation demands from the mobile users (MUs). In addition to local processing, the resource-constrained MUs in an MEC system can also offload computation to the nearby servers for remote execution. With the explosive growth of mobile devices, computation offloading faces the challenge of spectrum congestion, which in turn deteriorates the overall quality of computation experience. This paper hence investigates computation task scheduling in a heterogeneous cellular and WiFi MEC system. Such a system provides both licensed and unlicensed spectrum opportunities. Due to the sharing of communication and computation resources as well as the uncertainties, we formulate the problem of computation task scheduling among the competing MUs in a stationary heterogeneous edge computing system as a non-cooperative stochastic game. We propose an approximation-based multi-agent Markov decision process without the global system state observations, under which a multi-agent proximal policy optimization (PPO) algorithm is derived to solve the corresponding Nash equilibrium. When expanding to a non-stationary heterogeneous edge computing system, the obtained algorithm suffers from the slow convergence due to constrained adaptability. Accordingly, we explore meta-learning and propose a multi-agent meta-PPO algorithm, which rapidly adapts the control policy learning to the non-stationarity. Numerical experiments demonstrate performance gains from our proposed algorithms.

Joint Optimization of Energy and Task Scheduling in Wireless-Powered IRS-Assisted Mobile-Edge Computing Systems

This paper studies the design of an intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) system, which consists of a mobile user, an IRS equipped with radio-frequency (RF) energy harvesting (EH) circuits, and a hybrid access point (HAP) connected with an MEC server. The IRS is deployed to reflect the users task offloading signals to enhance the received power at HAP, and it needs to harvest energy from the RF signals emitted by the HAP before reflecting signals. To save energy consumption at the user, we first propose a novel MEC protocol, in which the system is enabled to operate in three modes, namely an EH mode, an IRS-assisted task offloading mode, and an IRS-inactive task offloading mode, so that the energy at IRS and the tasks generated at user can be flexibly scheduled within a finite time horizon, depending on channel conditions, IRS energy states and users task queue states. Under the protocol, we optimize the operation mode selection and resource allocation in each mode with a task execution delay constraint. Due to the randomness of wireless channels and task arrivals, the optimization problem is a stochastic programming. To solve this problem, we first transform it into a deterministic one by assuming that noncausal channel state information (CSI) and task state information (TSI) are available, and then derive a practical algorithm where only causal CSI and TSI are required. Simulation results verify that our proposed design can save at most 80% energy consumption as compared with existing baseline schemes.

An O-MAPPO scheme for joint computation offloading and resources allocation in UAV assisted MEC systems

The combination of mobile edge computing (MEC) systems with unmanned aerial vehicle (UAV) has gained a high profile in recent years due to its high flexibility and ability to handle intensive tasks. The computation offloading strategy and the resource allocation scheme affect the system performance significantly. Moreover, the system complexity increases with the numbers of users and servers exponentially. It is challenging to consider jointly the computation offloading and the resource allocation in MEC systems that have multiple users and multiple servers. This paper formulates the joint resources management in the UAV assisted MEC network as a partially observable markov decision process and proposes an online multi-agent proximal policy optimization (O-MAPPO) scheme to improve the energy efficiency while guaranteeing the requirements in task, power consumption, computation, and time. Specifically, users and servers are set as agents. All agents cooperatively make decisions of computation offloading and resource allocation to maximize the energy efficiency. Simulation results show that the O-MAPPO scheme significantly outperforms benchmark algorithms in robustness and stability.

Delay Minimization Using Hybrid RSMA-TDMA for Mobile Edge Computing

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget.

Selfish-Aware and Learning-Aided Computation Offloading for Edge–Cloud Collaboration Network

Mobile edge computing (MEC) raises the problem of selfish user devices that utilize less computing resources than expected to execute offloading tasks or maliciously discard computation tasks. However, most of the existing work either focused on the task offloading or concentrated on the trust mechanism in MEC systems. By jointly considering the two challenges, in this paper, we propose a selfish-aware and learning-aided computation offloading scheme for edge-cloud collaboration network. Specifically, we first design a selfishness evaluation mechanism to evaluate the selfishness of the user devices based on the historical interaction records of the edge-cloud collaboration network. Then, we construct the task offloading model which introduces the selfishness evaluation mechanism to suppress the selfish user devices. On this basis, we further formalize the selfish-aware task offloading as an optimization problem of the weighted sum of time latency and energy consumption. Thereafter, we take one step further formalizing the optimization problem as a Markov Decision Process (MDP), and design a task offloading algorithm based on deep reinforcement learning (DRL) to find the optimized task offloading decision. The simulation results demonstrate that our work can decrease the time latency and energy consumption as well as suppress the selfish user devices.

Energy-Efficient Optimization of Multi-User NOMA-Assisted Cooperative THz-SIMO MEC Systems

The various requirements in terms of data rates and latency in beyond 5G and 6G networks have motivated the integration of a variety of communications schemes and technologies to meet these requirements in such networks. Among these schemes are Terahertz (THz) communications, cooperative non-orthogonal multiple-access (NOMA)-enabled schemes, and mobile edge computing (MEC). THz communications offer abundant bandwidth for high-data-rate short-distance applications and NOMA-enabled schemes are promising schemes to realize the target spectral efficiencies and low latency requirements in future networks, while MEC would allow distributed processing and data offloading for the emerging applications in these networks. In this paper, an energy-efficient scheme of multi-user NOMA-assisted cooperative THz single-input multiple-output (SIMO) MEC systems is proposed to allow the uplink transmission of offloaded data from the far cell-edge users to the more computing resources in the base station (BS) through the cell-center users. To reinforce the performance of the proposed scheme, two optimization problems are formulated and solved, namely, the first problem minimizes the total users’ energy consumption while the second problem maximizes the total users’ computation energy efficiency (CEE) for the proposed scheme. In both problems, the NOMA user pairing, the BS receive beamforming, the transmission time allocation, and the NOMA transmission power allocation coefficients are optimized, while taking into account the full-offloading requirements of each user as well as the predefined latency constraint of the system. The obtained results reveal new insights into the performance and design of multi-user NOMA-assisted cooperative THz-SIMO MEC systems. Particularly, with relatively high offloading rate demands (several Gbits/user), we show that (i) the proposed scheme can handle such demands while satisfying the predefined latency constraint, and (ii) the full-offloading model can be considered the most effective solution in conserving mobile devices’ resources as compared to the system with the partial-offloading model or the system without offloading.

Joint Offloading and Resource Allocation With Diverse Battery Level Consideration in MEC System

Most of existing works related to mobile edge computing (MEC) focused on the total energy consumption or task completion time. However, for the applications with multiple players as a whole, the diverse battery levels of players should be considered to achieve better quality of experience (QoE), which is reflected in the metrics of task completion time and the lifespan of service. In this paper, we focus on QoE and diversity of devices. By jointly considering the individual battery level of different devices, we formulate an optimization problem to minimize the task completion time of the system. Firstly, we propose two necessary conditions to achieve the optimal solution of the formulated problem. In addition, we transform the formulated problem into another problem, which can be used to judge whether a given value is feasible for the formulated problem based on the proposed optimal conditions and bisection searching (BSS) algorithm. Then, we propose a method named sub-channel (SC) matching based on virtual computation resource (SMVCR) to solve the transformed problem with low computational complexity. Simulation results indicate that the BSS-SMVCR algorithm has a good performance in task completion time compared with some existing algorithms, and also can significantly extend the lifespan of the corresponding service, which is reflected in the supporting number of offloading tasks.

Joint Multi-Task Offloading and Resource Allocation for Mobile Edge Computing Systems in Satellite IoT

For multi-task mobile edge computing(MEC) systems in satellite Internet of Things(IoT), there are dependencies between different tasks, which need to be collected and jointly offloaded. It is crucial to allocate the computing and communication resources reasonably due to the scarcity of satellite communication and computing resources. To address this issue, we propose a joint multi-task offloading and resource allocation scheme in satellite IoT to improve the offloading efficiency. We first construct a novel resource allocation and task scheduling system in which tasks are collected and decided by multiple unmanned aerial vehicles(UAV) based aerial base stations, the edge computing services are provided by satellites. Furthermore, we investigate the multi-task joint computation offloading problem in the framework. Specifically, we model tasks with dependencies as directed acyclic graphs(DAG), then we propose an attention mechanism and proximal policy optimization (A-PPO) collaborative algorithm to learn the best offloading strategy. The simulation results show that the A-PPO algorithm can converge in 25 steps. Furthermore, the A-PPO algorithm reduces cost by at least 8.87 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> by default compared to several baseline algorithms. In summary, this paper provides a new insight for cost optimization of multi-task MEC system in satellite IoT.

Thermal-Aware Task Scheduling and Resource Allocation for UAV-and-Basestation Hybrid-Enabled MEC Networks

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide services in the resource-shortage areas, e.g., rural and disaster rescue fields. However, to provide satisfactory services, especially for computation-intensive and time-sensitive tasks, UAVs are required to operate at their full capacity due to their limited computation resources. This may lead to excessive heat dissipation and damage computing units. To tackle this dilemma, this paper takes central processing unit (CPU) thermal constraints during UAV operating hours into consideration. To further relieve the burden on the UAV, we explore a hybrid MEC system with a base station and a hover-fly-hover UAV that is able to compute during flight. We aim to minimize the cruise duration of the UAV by jointly optimizing user admission policy, task scheduling, UAV hovering trajectory, traveling time, and computation-and-communication resource allocations. The formulated problem is complicated and we further divide it into four sub-problems which are alternating to be solved in sequence. Simulation results demonstrate that the proposed algorithm can achieve better performance than other baseline algorithms, and keep CPU temperature under control.

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

Energy Efficient Joint Computation Offloading and Service Caching for Mobile Edge Computing: A Deep Reinforcement Learning Approach

Mobile Edge Computing (MEC) meets the delay requirements of emerging applications and reduces energy consumption by pushing cloud functions to the edge of the networks. Service caching is to cache application services and related databases at Edge Servers (ESs) in advance, and then ESs can process the relevant computation tasks. Due to the limited resources in the ESs, how to determine an effective service caching strategy is very crucial. In addition, the heterogeneity of ESs makes it impossible to make full use of the computing and caching resources without considering the collaboration among ESs. This paper considers a joint optimization of computation offloading, service caching, and resource allocation in a collaborative MEC system with multi-users, and formulates the problem as Mixed-Integer Non-Linear Programming (MINLP) which aims at minimizing the long-term energy consumption of the system. To solve the optimization problem, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed for determining the strategies of computation offloading, service caching, and resource allocation. Simulation results demonstrate that the proposed DDPG based algorithm can reduce the long-term energy consumption of the system greatly, and can outperform some other benchmark algorithms under different scenarios.

Federated Deep Reinforcement Learning for Energy-Efficient Edge Computing Offloading and Resource Allocation in Industrial Internet

Industrial Internet mobile edge computing (MEC) deploys edge servers near base stations to bring computing resources to the edge of industrial networks to meet the energy-saving requirements of Industrial Internet terminal devices. This paper considers a wireless MEC system in an intelligent factory that has multiple edge servers and mobile smart industrial terminal devices. In this paper, the terminal device has the choice of either offloading the task in whole or in part to the edge server, or performing it locally. Through combined optimization of the task offload ratio, number of subcarriers, transmission power, and computing frequency, the system can achieve minimum total energy consumption. A computing offloading and resource allocation approach that combines federated learning (FL) and deep reinforcement learning (DRL) is suggested to address the optimization problem. According to the simulation results, the proposed algorithm displays fast convergence. Compared with baseline algorithms, this algorithm has significant advantages in optimizing the performance of energy consumption.

Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAV Networks: A Metaheuristic Approach

Nowadays, unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) systems have been exploited as a promising solution for providing computation services to mobile users outside of terrestrial networks. However, it remains challenging for standalone UAVs to meet the computation requirement of numerous users due to their limited computation capacity and battery lives. Therefore, we propose a collaborative scheme among UAVs to share the workload between them. Furthermore, this work is the first to consider the task topology of offloading in MEC-enabled UAVs networks while restricting their power consumption. We study the task topology, in which a task consists of a set of sub-tasks, and each sub-task has dependencies upon other sub-tasks. In the real world, sub-tasks with dependencies must wait for their preceding sub-tasks to complete before being executed, and this affects the offloading strategy. Next, we formulate an optimization problem to minimize the average latency of users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem is NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two sub-problems: offloading decision problem and communication resource allocation problem. Then, a meta-heuristic method is proposed to find the sub-optimal solution to the former problem, while the latter problem is solved by using convex optimization. Finally, we conduct simulation experiments to prove that our proposed offloading technique outperforms several benchmark schemes in minimizing the average latency of users for dependency tasks and achieving higher uplink transmission rates.