Computation Offloading Research Articles

Quality of Experience Improvement and Service Time Optimization through Dynamic Computation Offloading Algorithms in Multi-access Edge Computing Networks

Quality of Experience Improvement and Service Time Optimization through Dynamic Computation Offloading Algorithms in Multi-access Edge Computing Networks

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.

Rethinking the mobile edge for vehicular services

The growing connected car market requires mobile network operators (MNOs) to rethink their network architecture to deliver ultra-reliable low-latency communications. In response, Multi-Access Edge Computing (MEC) has emerged as a solution, enabling the deployment of computing resources at the network edge. For MNOs to tap into the potential benefits of MEC, they need to transform their networks accordingly. Consequently, the primary objective of this study is to design a realistic MEC architecture and corresponding optimal deployment strategy – deciding on the placement and configuration of computing resources – as opposed to prior studies focusing on MEC run-time management and orchestration (e.g., service placement, computation offloading, and user allocation). To cater to the heterogeneous demands of vehicular services, we propose a multi-tier MEC architecture aligned with 5G and Beyond-5G radio access network deployments. Therefore, we frame MEC deployment as an optimization problem within this architecture, assuming 3 MEC tiers. Our data-driven evaluation, grounded in realistic assumptions about network architecture, usage, latency, and cost models, relies on datasets from a major MNO in the UK. We show the benefits of adopting a 3-tier MEC architecture over single-tier (centralized or distributed) architectures for heterogeneous vehicular services, in terms of deployment cost, energy consumption, and robustness.

Queuing‐based energy‐efficient processing algorithm for smart transportation through V2V communication

SummaryApplications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. Computational offloading based on Vehicle‐to‐Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computational tasks. Therefore, a decision‐making and intelligent task offloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete‐time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy‐efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy‐efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100–500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%.

Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

Dynamic computation scheduling for hybrid energy mobile edge computing networks

The rapid expansion of Mobile Edge Computing (MEC) is driven by the growing demand for resource-intensive applications within the Internet of Things. Computation offloading allows these applications to be executed at the network edge, but it leads to significant electricity expenses for network operators. To mitigate these costs, one promising solution is to power base stations (BSs) with a hybrid energy supply that combines unpredictable harvested energy with stable energy from the smart grid. This paper investigates joint computation scheduling for mobile devices (MDs) and resource allocation in a MEC network incorporating hybrid energy sources. Our objective is to maximize long-term time-averaged service utility by optimizing parameters such as BS battery supply, harvestable energy, CPU frequency, transmission power, task-partition factor, and MD-BS associations. To tackle this complex problem, we exploit the Lyapunov optimization framework to decompose it into deterministic subproblems for each time slot and propose an online network service utility maximization scheduling (NSUMS) algorithm. Experimental results show that our algorithm outperforms benchmark schemes in service utility and energy expenditure, improving the completion ratio by 32%, reducing the failure rate by 80%, and decreasing MD energy consumption by 28%.

Task Partition-Based Computation Offloading and Content Caching for Cloud–Edge Cooperation Networks

With the increasing complexity of applications, many delay-sensitive and compute-intensive services have posed significant challenges to mobile devices. Addressing how to efficiently allocate heterogeneous network resources to meet the computing and delay requirements of terminal services is a pressing issue. In this paper, a new cooperative twin delayed deep deterministic policy gradient and deep-Q network (TD3-DQN) algorithm is introduced to minimize system latency by optimizing computational offloading and caching placement asynchronously. Specifically, the task-partitioning technique divides computing tasks into multiple subtasks, reducing the response latency. A DQN intelligent algorithm is presented to optimize the offloading path to edge servers by perceiving network resource status. Furthermore, a TD3 approach is designed to optimize the cached content in the edge servers, ensuring dynamic popularity content requirements are met without excessive offload decisions. The simulation results demonstrate that the proposed model achieves lower latency and quicker convergence in asymmetrical cloud–edge collaborative networks compared to other benchmark algorithms.

Multi-objective optimization algorithm for multi-workflow computation offloading in resource-limited IIoT

Industrial internet of things (IIoT) connects traditional industrial devices with the network to provide intelligent services, which is regarded as the key technology for achieving Industry 4.0 and enabling the transformation of the manufacturing sector. Multi-access edge computing (MEC) has brought significant opportunities to expedite the development of IIoT. However, the unique task characteristics and dense deployment of IIoT devices, coupled with the resource starvation problem (RSP) arising from the limited resources of edge servers, pose challenges to the direct applicability of existing MEC algorithms in MEC-assisted IIoT scenarios. To this end, a multi-objective evolutionary algorithm is proposed to simultaneously optimize delay and energy consumption for multi-workflow execution in resource-limited IIoT. First, the initialization of execution location based on delay and the initialization of execution order satisfying the priority constraint can generate high-quality initial solutions. Then, the improved crossover and mutation operations guide the population evolution, which can span the large infeasible solution region. Finally, dynamic task scheduling (DTS) dynamically changes the execution location of tasks affected by RSP according to the execution efficiency, so as to avoid the tasks blindly waiting for server resources. The comprehensive simulation results demonstrate the effectiveness of the proposed method in achieving a balance between the execution delay and energy consumption of IIoT devices.

QoS and Fairness Oriented Dynamic Computation Offloading in the Internet of Vehicles Based on Estimate Time of Arrival

QoS and Fairness Oriented Dynamic Computation Offloading in the Internet of Vehicles Based on Estimate Time of Arrival

Multi-objective task offloading for highly dynamic heterogeneous Vehicular Edge Computing: An efficient reinforcement learning approach

Vehicular Edge Computing (VEC) provides a flexible distributed computing paradigm for offloading computations to the vehicular network, which can effectively solve the problem of limited vehicle computing resources and meet the on-vehicle computing requests of users. However, the conflict of interest between vehicle users and service providers leads to the need to consider a variety of conflict optimization goals for computing offloading, and the dynamic nature of vehicle networks, such as vehicle mobility and time-varying network conditions, make the offloading effectiveness of vehicle computing requests and the adaptability to complex VEC scenarios challenging. To address these challenges, this paper proposes a multi-objective optimization model suitable for computational offloading of dynamic heterogeneous VEC networks. By formulating the dynamic multi-objective computational offloading problem as a multi-objective Markov Decision Process (MOMDP), this paper designs a novel multi-objective reinforcement learning algorithm EMOTO, which aims to minimize the average task execution delay and average vehicle energy consumption, and maximize the revenue of service providers. In this paper, a preference priority sampling module is proposed, and a model-augmented environment estimator is introduced to learn the environmental model for multi-objective optimization, so as to solve the problem that the agent is difficult to learn steadily caused by the highly dynamic change of VEC environment, thus to effectively realize the joint optimization of multiple objectives and improve the decision-making accuracy and efficiency of the algorithm. Experiments show that EMOTO has superior performance on multiple optimization objectives compared with advanced multi-objective reinforcement learning algorithms. In addition, the algorithm shows robustness when applied to different environmental settings and better adapting to highly dynamic environments, and balancing the conflict of interest between vehicle users and service providers.

New Methods for Optimal Power Allocation and Joint Resource Scheduling in 5G Network which Use Mobile Edge Computing

Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds, and make the offloading process energetically expensive for devices. Therefore, it is very important to design an energy-efficient offloading strategy. To this end, we first formulate the transmission power allocation (PA) problem for mobile phone users to minimize power consumption. Using a quasi-convex technique, we address the (PA) problem using a (Hybrid GAPSO) algorithm resulting from combining the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) algorithm. Next, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed- nonlinear program to minimize the response delay of requests. The (JRORS) problem can be divided into two problems, namely the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a dual decision problem and propose a multi-objective particle swarm optimization algorithm referred as (MO-PSO). The simulation results show that (HGAPSO) can save transmission power consumption and has good convergence property, and (MO-PSO) outperforms existing methods in terms of response rate and can maintain good performance in a dynamic network.

Federated Learning-Oriented Edge Computing Framework for the IIoT.

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less.

Collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach

Computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users’ (MUs’) tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks continuously increase, it is difficult for a single UAV to meet all computational demands, result in the decline of QoS. In order to address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without recharging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to encourage the adjacent UAVs with unused computation resources to cooperate with busy UAVs, so as to maximize the total revenue obtained from UAVs’ collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the maximum overall system utility under different scenarios.

A two-level game theoretic approach for task offloading in mobile edge computing

In the mobile edge computing system subject to wireless interference, the Edge Server Provider (ESP) aims to offer profitable computing resources to Device Managers (DMs), who make optimal strategies based on the provided prices. However, the existence of mixed variables typically constitutes an NP-hard problem, posing a significant challenge for optimization. In response to address this issue, we formulate a bi-level optimization problem, where the upper level is devoted to optimizing the pricing of computing resources. The lower level optimizes DM migration strategies and resource allocation at specified prices. Leveraging game theory, we achieve distributed and efficient computation offloading by formulating the distributed computing offloading strategy among lower-level DMs as a task offloading game. Our analysis identifies Nash equilibrium and finite improvement characteristics within the game. Based on these insights, we present a bi-level distributed computation offloading algorithm capable of reaching Nash equilibrium, thus optimizing the profit for both DMs and ESP. The experiments have demonstrated the algorithm’s effectiveness in reducing system costs and maximizing the profits of ESP with DMs across diverse scenarios.

UAV-assisted dependency-aware computation offloading in device–edge–cloud collaborative computing based on improved actor–critic DRL

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has become a popular research topic, addressing challenges posed by the pressure of cloud computing and the limited service scope of MEC. However, the limited computing resources of UAVs and the data dependency of specific tasks hinder the practical implementation of efficient computational offloading (CO). Accordingly, a device–edge–cloud collaborative computing model is proposed in this study to provide complementary offloading services. This model considers stochastic movement and channel obstacles, representing the dependency relationships as a directed acyclic graph. An optimization problem is formulated to simultaneously optimize system costs (i.e., delay and energy consumption) and UAV endurance, taking into account resource and task-dependent constraints. Additionally, a saturated training SAC-based UAV-assisted dependency-aware computation offloading algorithm (STS-UDCO) is developed. STS-UDCO learns the entropy and value of the CO policy to efficiently approximate the optimal solution. The adaptive saturation training rule proposed in STS-UDCO dynamically controls the update frequency of the critic based on the current fitted state to enhance training stability. Finally, extensive experiments demonstrate that STS-UDCO achieves superior convergence and stability, while also reducing the system total cost and convergence speed by at least 11.83% and 39.10%, respectively, compared with other advanced algorithms.

Non-orthogonal multiple access-based MEC for energy-efficient task offloading in e-commerce systems

Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespread attention as a technology that effectively reduces server load. However, how to improve energy efficiency while ensuring computing requirements is an important challenge facing computation offloading. To solve this problem, using non-orthogonal multiple access (NOMA) to increase the efficiency of multi-access wireless transmission, MEC supporting NOMA is investigated in the research. Computing resources will be divided into separate sub-computing that will be handled via e-commerce terminals or transferred to edge sides by reutilizing radio resources, we put forward a Group Switching Matching Algorithm Based on Resource Unit Allocation (GSM-RUA) algorithm that is multi-dimensional. To this end, we first formulate this task allocation problem as a long-term stochastic optimization problem, which we then convert to three short-term deterministic sub-programming problems using Lyapunov optimization, namely, radio resource allocation in a large timescale, computation resource allocating and splitting in a small-time frame. Of the 3 short-term deterministic sub-programming problems, the first sub-programming problem can be remodeled into a 1 to n matching problem, which can be solved using the block-shift-matching-based radio resource allocation method. The latter two sub-programming problems are then transformed into two continuous convex problems by relaxation and then solved easily. We then use simulations to prove that our GSM-RUA algorithm is superior to the state-of-the-art resource management algorithms in terms of energy consumption, efficiency and complexity for e-commerce scenarios.

Energy saving computation offloading for dynamic CR-MEC systems with NOMA via decomposition based soft actor–critic

This paper investigates resource allocation in cognitive radio (CR) based mobile edge computing (MEC) systems under time-varying channels while considering non-orthogonal multiple access (NOMA) technique. In contrast to prior research on NOMA-MEC, we introduce a generalized user grouping scheme, which makes the secondary users (SUs) have the flexibility to participate in multiple NOMA groups simultaneously, enabling the partial offloading of their task data to multiple MEC servers. To tackle the non-convex long-term energy minimization problem, we propose a decomposition based soft actor–critic (DB-SAC) approach combining conventional convex optimization with deep reinforcement learning (DRL) algorithms, which divides the problem into a joint communication and computation resource allocation subproblem and an offloading ratios optimization subproblem. The former one is further divided into a series of local computation resource and offloading energy minimization problems, addressed through theoretical analysis and convex optimization techniques. For the latter subproblem, we adopt the SAC algorithm to dynamically allocate offloading ratios for SUs. Simulation results showcase that when compared to the conventional non-generalized user grouping scheme, the generalized user grouping scheme can achieve remarkable energy savings of up to 87.5%.

Dynamic resource management for 6G vehicular networks: CORA-6G offloading and allocation strategies

Given the burgeoning demands of real-time applications and limited on-board computational resources, the rapid proliferation of 6G-enabled vehicular networks brings forth the critical challenge of efficient computation offloading. This paper introduces the CORA-6G algorithm, a novel solution specifically designed to address this issue by determining the optimal offloading ratio. Using a robust convex optimization problem, CORA-6G maximizes the computational utility, ensuring efficient resource allocation and enhanced responsiveness. Quantitative evaluations reveal that, when compared to conventional methods, CORA-6G improves offloading efficiency by up to 25%, thereby significantly boosting the overall performance of vehicular applications. This research underscores the potential of CORA-6G as a cornerstone for future 6G vehicular network advancements.

Lyapunov-Based Joint Flight Trajectory and Computation Offloading Optimization for UAV-Assisted Vehicular Networks

Lyapunov-Based Joint Flight Trajectory and Computation Offloading Optimization for UAV-Assisted Vehicular Networks

Soft computing approaches for dynamic multi-objective evaluation of computational offloading: a literature review

Soft computing approaches for dynamic multi-objective evaluation of computational offloading: a literature review