Joint Computation Offloading Research Articles

Joint Computation Offloading and Trajectory Optimization for Edge Computing UAV: A KNN-DDPG Algorithm

Unmanned aerial vehicles (UAVs) are widely used to improve the coverage and communication quality of wireless networks and assist mobile edge computing (MEC) due to their flexible deployments. However, the UAV-assisted MEC systems also face challenges in terms of computation offloading and trajectory planning in the dynamic environment. This paper employs deep reinforcement learning to jointly optimize the computation offloading and trajectory planning for UAV-assisted MEC system. Specifically, this paper investigates a general scenario where multiple pieces of user equipment (UE) offload tasks to a UAV equipped with a MEC server to collaborate on a complex job. By fully considering UAV and UE movement, computation offloading ratio, and blocked relations, a joint computation offloading and trajectory optimization problem is formulated to minimize the maximum computational delay. Due to the non-convex nature of the problem, it is converted into a Markov decision process, and solved by the deep deterministic policy gradient (DDPG) algorithm. To enhance the exploration capability and stability of DDPG, the K-nearest neighbor (KNN) algorithm is employed, namely KNN-DDPG. Moreover, the prioritized experience replay algorithm, where the constant learning rate is replaced by the decaying learning rate, is utilized to enhance the converge. To validate the effectiveness and superiority of the proposed algorithm, KNN-DDPG is compared with the benchmark DDPG algorithm. Simulation results demonstrate that KNN-DDPG can converge and achieve 3.23% delay reduction compared to DDPG.

Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning

Vehicular edge computing (VEC), a promising paradigm for the development of emerging intelligent transportation systems, can provide lower service latency for vehicular applications. However, it is still a challenge to fulfill the requirements of such applications with stringent latency requirements in the VEC system with limited resources. In addition, existing methods focus on handling the offloading task in a certain time slot with statically allocated resources, but ignore the heterogeneous tasks’ different resource requirements, resulting in resource wastage. To solve the real-time task offloading and heterogeneous resource allocation problem in VEC system, we propose a decentralized solution based on the attention mechanism and recurrent neural networks (RNN) with a multi-agent distributed deep deterministic policy gradient (AR-MAD4PG). First, to address the partial observability of agents, we construct a shared agent graph and propose a periodic communication mechanism that enables edge nodes to aggregate information from other edge nodes. Second, to help agents better understand the current system state, we design an RNN-based feature extraction network to capture the historical state and resource allocation information of the VEC system. Thirdly, to tackle the challenges of excessive joint observation-action space and ineffective information interference, we adopt the multi-head attention mechanism to compress the dimension of the observation-action space of agents. Finally, we build a simulation model based on the actual vehicle trajectories, and the experimental results show that our proposed method outperforms the existing approaches.

Joint Computation Offloading and Resource Optimization for Minimizing Network-Wide Energy Consumption in Ultradense MEC Networks

Joint Computation Offloading and Resource Optimization for Minimizing Network-Wide Energy Consumption in Ultradense MEC Networks

Joint Computation Offloading and Target Tracking in Integrated Sensing and Communication Enabled UAV Networks

Joint Computation Offloading and Target Tracking in Integrated Sensing and Communication Enabled UAV Networks

Joint Computation Offloading and Resource Allocation for Maritime MEC with Energy Harvesting

Joint Computation Offloading and Resource Allocation for Maritime MEC with Energy Harvesting

Computation Offloading and Resource Allocation for E2E Tasks in Satellite Edge Computing Networks

Satellite edge computing shows promise in delivering seamless, low-latency services in challenging contexts such as deserts, oceans, and during natural disasters. Previous studies focused on the selection of suitable offloading nodes for task processing and result transmission to the original node. However, for end-to-end tasks, exemplified by applications like satellite-based remote sensing and forestry monitoring, the disparity between source and destination nodes emphasizes the importance of routing selection in data transmission. This aspect substantially limits the effectiveness of traditional computation offloading methods. In this paper, we formulate a joint computation offloading, routing, and multiple resource allocation optimization problem for end-to-end tasks in satellite edge computing networks. Moreover, to reduce the complexity of this nondeterministic polynomial-time hard (NP-hard) problem, we decompose it into the multidimensional resource allocation in fixed offloading and routing condition and the computation offloading and routing selection problem. An algorithm based on binary particle swarm optimization for joint computation offloading, routing, and multiple resource allocation optimization problem is proposed. Simulation results are presented to demonstrate the performance of our scheme compared with other baseline schemes.

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks.

Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented "end-edge-cloud" multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.

System-centric energy efficient computation offloading and resource allocation in latency-sensitive MEC systems

Mobile edge computing (MEC) enables computation tasks of end users to be offloaded and processed at nearby edge servers, which is a promising technology that can effectively alleviate end users’ computing pressure. However, how to balance two important metrics for MEC computation offloading, i.e., latency and energy efficiency, is still a key challenge. In this paper, we investigate joint computation offloading and resource allocation for latency-sensitive applications in order to maximize system-centric computation energy efficiency (SCEE) in MEC systems. An optimization problem is formulated to maximize the sum computation energy efficiency of all users which contains the energy consumption at both user and edge server sides. Meanwhile, task completion latency constraints for all users are considered. We decompose the original optimization problem into two tractable subproblems. The optimal computation offloading as well as communication and computing resource allocation solutions are obtained by solving these subproblems. Furthermore, a hierarchical system-centric energy efficient computation offloading and resource allocation (SEECR) algorithm is developed. Numerical results show that compared with the benchmark schemes, the proposed algorithm can effectively improve the computation energy efficiency performance while guaranteeing the latency requirements of all users.

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA-MEC Networks.

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system’s efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications’ inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle’s proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge Computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle’s computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the scheduling overhead, improves the utilization of system resources, and minimizes the data transmission delay caused by vehicle motion.

Joint MU-MIMO Precoding and Computation Optimization for Energy Efficient Industrial IoT With Mobile Edge Computing

In the fourth industrial revolution, the industrial Internet of things (IIoT) will bring fundamental changes to human communities. This paper proposes to make full use of the under-utilized computing resources of wired edge devices to alleviate the computing burden of the processing center in delay-constrained multi-user networks. Our goal is to minimize the weighted energy consumption while ensuring the required latency. The formulated problems are NP-hard involving joint optimization of the computation task assignment, transmit association design, multiple-input multiple-output (MIMO) precoding, and computing resource allocation with binary and partial offloading protocols. By utilizing the weighted minimum mean-squared-error method, quadratic transformation, and difference of convex functions algorithm, we propose two joint computation offloading and resource allocation algorithms for binary and partial offloading protocols, respectively. Simulation results confirm the efficiency of the proposed algorithms and demonstrate that the proposed algorithms achieve a significant computation performance enhancement.

Computation Offloading and Resource Allocation in NOMA–MEC: A Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) has emerged as a powerful paradigm for increasing the computation performance of mobile devices (MDs). Applying non-orthogonal multiple access (NOMA) to MEC can further improve the spectrum efficiency and reduce offloading delays caused by the upload congestion. In this paper, we examine the joint computation offloading and resource allocation problem in the NOMA-MEC system, which benefits from the combination of NOMA and MEC. Our optimization objective is to minimize the computational overhead (the weighted sum of the execution delay and the energy consumption) in dynamic environments with time-varying wireless fading channels. The optimization problem is formulated as a mixed integer programming (MIP), which involves jointly optimizing the task offloading decisions, channel assignment, and transmit power allocation. To solve such an optimization problem, we formalize the task offloading and the resource allocation as a Markov decision process (MDP). Then, we propose a deep reinforcement learning (DRL) based approach, which combines multiple deep neural networks (DNNs) to directly approximate different statistical models for continuous and discrete control. The simulation results demonstrate that the proposed approach can rapidly converge and efficiently decrease the total computational overhead compared to other baseline approaches in different scenarios.

Joint computation offloading and resource allocation in space-air-terrestrial integrated networks for IoT Applications

Internet of Things (IoT) devices can reduce their energy consumption by computation offloading. However, IoT devices located in areas without deployed ground communication facilities face significant challenges in computation offloading. For this reason, we propose the space-air-terrestrial integrated networks (SATINs) and design a three-tier computing framework for providing computing services to IoT devices. In the computing framework, device's task can be computed locally, on mobile edge computing (MEC) servers in the air layer, or on cloud servers in the ground. In this article, we jointly optimize the computation offloading decisions of tasks and computing resource allocation of MEC servers. We aim at minimizing the total cost of executing tasks while satisfying both the time constraints of tasks and capacity constraints of MEC servers. And the total cost includes the energy cost of IoT devices and the usage cost of servers. Since the computation offloading decisions are binary variables, the joint optimization problem is a mixed integer nonlinear programming (MINLP) problem and is NP-hard. To tackle this problem, we use relaxation technique and Majorize-minimize (MM) method to transform the optimization problem into a series of convex problems for solving. Moreover, we propose a distributed algorithm based on Lagrange dual decomposition method with low time complexity. Experimental results demonstrate that our proposed distributed algorithm can effectively reduce the total cost of the system compared with other benchmark algorithms.

Joint Computation Offloading and Resource Allocation for MIMO-NOMA Assisted Multi-User MEC Systems

This paper investigates the resource allocation and computation offloading problem for multi-access edge computing (MEC) systems, where multiple mobile users (MUs) equipped with multiple antennas access the base station in a non-orthogonal multiple access manner. We jointly optimize the offloading ratio, computational frequency and transmit precoding matrix of each MU to minimize the total energy consumption of all MUs while satisfying the latency constraints. The problem is formulated as a non-convex optimization problem and a two-layer iterative method is proposed to solve the problem efficiently with low complexity. Specifically, we first decompose the original problem into several subproblems, and then sequentially solve these subproblems in an alternative fashion. Furthermore, we also discuss the optimal decoding order of MUs under two different scenarios. Firstly, when the MUs’ channel conditions are similar, by deriving closed-form expressions for energy consumptions of all MUs, we prove that the optimal decoding order is only determined by the latency requirements. On the other hand, when the MUs’ channel conditions are different, we show that the optimal decoding order is determined by both the channel conditions and the latency requirements. As such, we propose a metric aiming to balance the effects of channel conditions and latency requirements on the MUs’ decoding order. Simulation results validate the convergence of the proposed method and demonstrate its superiority over benchmark algorithms.

Joint computation offloading and resource allocation based on deep reinforcement learning in C-V2X edge computing

Joint computation offloading and resource allocation based on deep reinforcement learning in C-V2X edge computing

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.

Joint Computation Offloading and Resource Allocation for Edge-Cloud Collaboration in Internet of Vehicles via Deep Reinforcement Learning

Mobile edge computing (MEC) and cloud computing (CC) have been considered as the key technologies to improve the task processing efficiency for Internet of Vehicles (IoV). In this article, we consider a random traffic flow and dynamic network environment scenario where MEC and CC are collaborated for processing delay-sensitive and computation-intensive tasks in IoV. We study the joint optimization of computation offloading and resource allocation (CORA) with the objective of minimizing the system cost of processing tasks subject to the processing delay and transmission rate constraints. To attack the challenges brought by the dynamic environment, we use the Markov decision process model for formulating the dynamic optimization problem, and apply a deep reinforcement learning (DRL) technique to deal with high-dimensional and continuous states and action spaces. Then, we design a CORA algorithm, which is able to effectively learn the optimal scheme by adapting to the network dynamics. Extensive simulation experiments are conducted, in which we compare the CORA algorithm with both non-DRL algorithms and DRL algorithms. The experimental results show that the CORA algorithm outperforms others with excellent training convergence and performance in processing delay and processing cost.

RIS-assisted edge-D2D cooperative edge computing for industrial applications

Intelligence and low latency are particularly desired in the vision of industrial intelligence. To support intelligence, massive amount of data is generated from distributed Internet of Things (IoT) devices, and expected to quickly process with artificial intelligence (AI) for data value maximization. To reduce the network transmission delay from data source to computing nodes, edge computing is promising. However, the scarce and dynamic wireless resource as well as limited computing capability of edge servers challenge the edge intelligence. This paper studies a reconfigurable intelligent surface (RIS)-assisted edge-device-to-device (D2D) cooperative edge computing for end-to-end delay reduction in industrial environments. In special, we consider such an edge-D2D cooperative edge computing system, where the IoT devices could offload their tasks to edge server via cellular links for edge computing, or transmit the tasks to helper nodes via D2D links for local computing. We also consider the base station (BS)-based and D2D-based RISs deployed in cellular and D2D networks respectively. We formulate the joint computation offloading, beamforming optimization of both BS-based and D2D based RISs, and CPU resource allocation problem for average end-to-end delay minimization. Then, a distributed and cooperative scheme, called RIS-assisted edge-D2D cooperative computation offloading (RIS-assisted EDCO), is proposed to address the problem. The simulation results have illustrated the efficiency of the proposal for low end-to-end delay performance provisioning.

Water Pumping and Refilling (WPR): A Resource Allocation Algorithm for Maximizing Acceptance Ratio in Asymmetrical Edge Computing Networks

Computation offloading has received a significant amount of attention in recent years, with many researchers proposing joint offloading decision and resource allocation schemes. However, although existing delay minimization schemes achieve minimum delay costs, they do so at the cost of losing possible further maximization of the number of serviced requests. Furthermore, the asymmetry between uplink and downlink poses challenges to resource allocation in edge computing. This paper addresses this issue by formulating the joint computation offloading and edge resource allocation problem as a mixed-integer nonlinear programming (MINLP) problem in an edge-enabled asymmetrical network. Leveraging the margin between a delay-minimum scheme and a near-deadline scheme, a water pumping and refilling (WPR) algorithm is proposed to maximize the number of accepted requests. The WPR algorithm can function both as a supplementary algorithm to a given offloading scheme and as a standalone algorithm to obtain a resource allocation scheme following a customizable refilling policy. The simulation results demonstrated that the proposed algorithm outperforms delay-minimum schemes in achieving a high acceptance ratio.