Computation Offloading Research Articles

A new approach based on genetic algorithm for computation off-loading optimization in multi-access edge computing networks

The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, op- timizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel genetic algorithm-based ap- proach for efficient computation offloading in MEC, considering processing and transmission delays, user preferences, and system constraints. The pro- posed approach integrates computation offloading and resource allocation al- gorithm based on evolutionary principles, combined with a greedy strategy to maximize overall system performance. By utilizing genetic algorithms, the pro- posed method enables dynamic adaptation to changing conditions, eliminating the need for intricate mathematical models and providing an appealing solution to the complexities inherent in MEC. The urgency of this research arises from the critical need to enhance mobile application performance. Simulation re- sults demonstrate the robustness and efficacy of our approach in achieving near- optimal solutions while efficiently balancing computation offloading, minimiz- ing latency, and maximizing resource utilization. Our approach offers flexibility and adaptability, contributing to advancement of MEC networks and addressing the requirements of latency-sensitive applications.

UAV-Assisted Target Tracking and Computation Offloading in USV-Based MEC Networks

UAV-Assisted Target Tracking and Computation Offloading in USV-Based MEC Networks

Joint Computation Offloading and Resource Allocation in Multi-Edge Smart Communities With Personalized Federated Deep Reinforcement Learning

Joint Computation Offloading and Resource Allocation in Multi-Edge Smart Communities With Personalized Federated Deep Reinforcement Learning

Joint edge caching and computation offloading for heterogeneous tasks in MEC-enabled vehicular networks

Edge caching is an effective paradigm that can significantly reduce the computation task offloading latency for mobile edge computing (MEC) in vehicular networks, while also alleviating the backhaul transmission pressure for retrieving content data from the cloud server. However, most existing works fail to address how to handle heterogeneous tasks generated by vehicle terminals (VTs), especially in complex scenarios where both computation and content tasks are generated simultaneously. In this paper, we consider a mobility-aware vehicular network model where VTs simultaneously generate heterogeneous task requests, i.e., a computation task and a content task, and investigate joint optimization of caching for heterogeneous tasks data, computation offloading, and computing resource allocation. In order to optimize the latency for processing the heterogeneous tasks, an average execution latency minimization problem with sojourn time and caching capacity constraints is formulated. We decompose this problem into two tractable subproblems, i.e., caching optimization subproblem, and computation offloading and resource allocation optimization subproblem. We first develop a dynamic programming (DP) algorithm to obtain the optimal caching strategies for heterogeneous tasks data. We compare the obtained content retrieval latency with the local computing latency, and derive the optimal computation offloading and edge computing resource allocation solutions. On this basis, we propose a joint computation offloading and resource allocation (JCORA) algorithm to determine the computing resources allocated to each VT and corresponding computation offloading strategy. Numerical results indicate that the proposed algorithm, which integrates DP algorithm and JCORA algorithm, can achieve lower execution latency for heterogeneous tasks compared to the benchmark schemes. Additionally, for task loss scenarios where the sojourn time constraint cannot be met, the impact of VT mobility on the task loss probability is also revealed.

Offloading Strategy for Forest Monitoring Network Based on Improved Beetle Optimization Algorithm

In forest monitoring networks, the computational capabilities of sensors cannot meet the latency requirements for complex tasks, and the limited battery capacity of these sensors hinders the long-term execution of monitoring tasks. Mobile edge computing (MEC) acts as an effective solution for this issue by offloading tasks to edge servers, significantly reducing both task latency and energy consumption. However, the computational capacity of MEC servers and the bandwidth in the system are limited, and the communication environment in forested areas is complex. To simulate the complexity of the forest communication environment, we incorporate empirical path loss and multipath fading into the calculation of signal transmission rates. The computational offloading problem is then converted into a minimum-cost optimization problem with multiple constraints related to energy consumption and latency, which we formulate as an NP-hard problem. We propose a dung beetle optimization (DBO) strategy for computational offloading, enhancing it with an improved circle chaotic mapping, a dimension decomposition strategy, and Cauchy disturbance. This algorithm has the beauty of symmetry in the search range, and the symmetrical features can comprehensively search for existing solutions. Experimental results demonstrate that the improved dung beetle optimization algorithm (IDBO) achieves better convergence, lower complexity, and superior optimization outcomes compared to local offloading strategies and other metaheuristic algorithms, confirming the effectiveness of the proposed algorithm and ensuring the service quality of the forest monitoring network.

An Adaptive Alternating Direction Method of Multipliers for Vehicle-to-Everything Computation Offloading in Cloud–Edge Collaborative Environment

An Adaptive Alternating Direction Method of Multipliers for Vehicle-to-Everything Computation Offloading in Cloud–Edge Collaborative Environment

Enabling Efficient Vehicle–Road Cooperation Through AIoT: A Deep Learning Approach to Computational Offloading

Enabling Efficient Vehicle–Road Cooperation Through AIoT: A Deep Learning Approach to Computational Offloading

Multi-agent deep reinforcement learning for computation offloading in cooperative edge network

Multi-agent deep reinforcement learning for computation offloading in cooperative edge network

Hierarchical federated deep reinforcement learning based joint communication and computation for UAV situation awareness

The computation-intensive situational awareness (SA) task of unmanned aerial vehicle (UAV) is greatly affected by its limited power and computing capability. To solve this challenge, we consider the joint communication and computation (JCC) design for UAV network in this paper. Firstly, a multi-objective optimization (MOO) model, which can optimize UAV computation offloading, transmit power, and local computation resources simultaneously, is built to minimize energy consumption and task execution delay. Then, we develop Thompson sampling based double-DQN (TDDQN) learning algorithm which allows the agent to explore more deeply and effectively, and propose a joint optimization algorithm that combines TDDQN and sequential least squares quadratic programming (SLSQP) to handle the MOO problem. Finally, to enhance the training speed and quality, we incorporate federated learning (FL) into the presented joint optimization algorithm and propose hierarchical federated TDDQN with SLSQP (HF TDDQN-S) to implement the JCC design. Simulation results show that the introduced HF TDDQN-S can efficiently learn the best JCC strategy and minimize the average cost contrasted with the DDQN with SLSQP (DDQN-S) and TDDQN with SLSPQ (TDDQN-S) approach, and achieve the low average delay SA with power efficient.

COPSA: a computation offloading strategy based on PPO algorithm and self-attention mechanism in MEC-empowered smart factories

With the dawn of Industry 5.0 upon us, the smart factory emerges as a pivotal element, playing a crucial role in the realm of intelligent manufacturing. Meanwhile, mobile edge computing is proposed to alleviate the computational burden presented by substantial workloads in smart factories. Nonetheless, it is very challenging to effectively incorporate edge computing resources to improve the efficiency of resource deployment in smart factories. Accordingly, we devise a novel approach based on Proximal Policy Optimization algorithm with the Self-Attention Mechanism to implement computing resource allocation in MEC-Empowered Smart Factories. More specifically, the self-attention mechanism is incorporated to enable dynamic focus on state information, accelerates convergence and facilitates global control. A great number of experiments conducted on both simulated and real datasets have verified the superiority of our proposed approach compared to the state-of-the-art baselines.

Joint Data Caching and Computation Offloading in UAV-Assisted Internet of Vehicles via Federated Deep Reinforcement Learning

Joint Data Caching and Computation Offloading in UAV-Assisted Internet of Vehicles via Federated Deep Reinforcement Learning

Edge Intelligence-Driven Joint Offloading and Resource Allocation for Future 6G Industrial Internet of Things

The 6G will undergo an unprecedented transformation to revolutionize the wireless system evolution from connected things to connected intelligence. Additionally, data scattered around the industrial environments can be collected for the sake of enabling intelligent operations. In this paper, the promising multi-access edge computing (MEC) service is introduced into the IIoT system to assist the computation offloading and resource allocation for different compelling applications. Moreover, relying on defining a total cost function as a weighted sum of task delay and energy consumption, a novel deep reinforcement learning (DRL) based framework is proposed to jointly optimize task offloading and resource allocation. More specifically, the task offloading is decomposed with the aid of the new isotone action generation technique (IAGT) and adaptive action aggregation update strategy (3AUS) based on the proposed DRL framework, and the initial problem can be transformed into a convex optimization problem to solve the resource allocation for each IIoT device. Additionally, we periodically renovate the offloading policy in the DRL framework so that our proposed DRL-based decision-making algorithm can beneficially adapt to different network environments. Finally, extensive simulation demonstrate that our proposed algorithm for each IIoT device can obtain quasi-optimal system performance compared with some conventional baseline algorithms

Hybrid Multi-Server Computation Offloading in Air–Ground Vehicular Networks Empowered by Federated Deep Reinforcement Learning

Hybrid Multi-Server Computation Offloading in Air–Ground Vehicular Networks Empowered by Federated Deep Reinforcement Learning

UAV-Assisted Heterogeneous Multi-Server Computation Offloading With Enhanced Deep Reinforcement Learning in Vehicular Networks

UAV-Assisted Heterogeneous Multi-Server Computation Offloading With Enhanced Deep Reinforcement Learning in Vehicular Networks

Distributed Neuroadaptive Formation Control for Aerial Base Station-Assisted Hovercraft Systems with Mixed Disturbances

Effectively addressing the formation control of ABS-assisted hovercraft systems with heterogeneities, unavailable leaders’ convex combination states, nonlinearities, and mixed disturbances poses significant challenges. This paper proposes a distributed neuroadaptive formation tracking strategy of ABS-assisted hovercraft systems for the first time, where aerial base stations (ABSs) are composed of unmanned aerial vehicles (UAVs) for data distribution and computation offloading. Firstly, UAVs are designed to track the virtual-leader while shaping a fixed formation, and the observer is devised for each follower hovercraft to estimate the convex combination states of UAVs. Then, output regulation equations are employed to transform heterogeneous systems into a compact form via the Kronecker product, while neural networks (NNs) are introduced to compensate for model nonlinearities. Furthermore, based on random differential equations (RDEs) combined with Lyapunov theory, the noise-to-state practical stability in probability (NSPS-P) property of the error dynamics under mixed disturbances can be obtained. Finally, simulation examples demonstrate that the outputs of follower hovercrafts rapidly achieve a time-varying formation and rotate around convex combination states of leader UAVs simultaneously.

Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

Game-based computation offloading and resource allocation in stochastic geometry-modeling vehicular networks

Game-based computation offloading and resource allocation in stochastic geometry-modeling vehicular networks

Cache-assisted computation offloading for workflow applications in industrial internet of things

The Internet of Things plays an important role in the process of industrial intelligence. It facilitates the connection between devices and the Internet to enable the gathering and analysis of industrial data. However, industrial Internet of Things (IIoT) applications are highly susceptible to latency, have strict energy consumption limitations, and impose specific demands on the resources of IIoT devices. Fortunately, edge computing can migrate tasks to the edge server or cloud platform to deal with the above shortcomings. Nevertheless, tasks generated by IIoT devices in industrial production have a higher rate of repetition, and repetitive execution of the same task diminishes the operational efficiency of the edge system. In this regard, we can cache high-value tasks through task caching to reduce redundant execution. The aforementioned idea can be described as a joint optimization problem of computation offloading and task caching with resource constraints in IIoT. To address this challenge, we establish an edge-empowered service model and propose a cache-assisted computation offloading algorithm for IIoT applications. Extensive experiments demonstrate that the proposed algorithm outperforms some critical methods in terms of energy consumption and latency.

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Utility optimization for computation offloading and splitting in time-varying HAP and LEO satellite integrated MEC networks

To provide ubiquitous and low-latency communication and computation services for remote and disaster areas, high altitude platform (HAP) and low earth orbit (LEO) satellite integrated multi-access edge computing (HLS-MEC) networks have emerged as a promising solution. However, most current studies directly assume that the number of connected satellites is fixed and neglect the modeling of the time-varying multi-satellite computing process. Motivated by this, we establish an M/G/K(t) queuing model to illustrate task computation on satellites. To evaluate the efficiency and quality of computation offloading and splitting, we develop a utility model. This model is defined as a difference between a value function that assesses the trade-offs of task offloading, considering latency reductions and energy savings, and a cost function that quantifies expenses related to latency and energy consumption. After formulating the utility maximization problem, we propose the deep reinforcement learning-based offloading and splitting (DBOS) scheme that can overcome the time-varying uncertainties and high dynamics in the HLS-MEC network. Specifically, the DBOS scheme can learn the best computation offloading and splitting policy to maximize the utility by sensing the number of connected satellites, the distance between the HAP and satellites, the available computing resources, and the task arrival rate. Finally, we evaluate and validate the computational complexity and convergence property of the DBOS scheme. Numerical results show that the DBOS scheme outperforms the other three benchmarks and maximizes the utility under time-varying dynamics.