Collaborative Computation Offloading Research Articles

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.

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

With the increasing dissemination of the Internet of Things and 5G, mobile edge computing has become a novel scheme to assist terminal devices in executing computation tasks. To elevate the coverage and computation capability of edge computing, a collaborative computation offloading and resource management architecture was proposed in space–air–ground integrated networking (SAGIN). In this manuscript, we established a novel model considering the computation offloading cost constraints of the communication, computing and cache model in the SAGIN. To be specific, the joint optimization problem of collaborative computation offloading and resource management was modeled as a mixed integer nonlinear programming problem. To address this issue, this paper proposed a computation offloading and resource allocation strategy based on deep reinforcement learning (DRL). Differing from traditional methods, DRL does not need a well-established formulation or previous information, and it is capable of revising the strategy adaptively according to the environment. The simulation results demonstrate the proposed approach can achieve the optimal reward values in the case of different terminal device numbers. Furthermore, this manuscript provided the analysis with variant parameters of the proposed approach.

Simulation Analysis and Comparative Study of MOSGT with Other Techniques

This paper presents a comparative study of the Multi-Objective Symmetric Game Theory (MOSGT) for task offloading in cloud robots with existing techniques such as Genetic Algorithm 4 Collaborative Computation Offloading (GA4CCO) and Approximation Collaborative Computation Offloading (ACCO). The study employs a simulation analysis to evaluate the performance of the proposed MOSGT model in a cloud robotics context. The results of the simulation study reveal that the MOSGT model reduces system cost by approximately 35% compared to GA4CCO and by 20% compared to ACCO. Furthermore, the MOSGT model exhibits a reduced rate of 30% less than GA4CCO and 25% minimal than ACCO in terms of iteration. These findings underscore the potential of MOSGT as a promising solution for efficient task offloading in cloud robots, offering significant improvements in computational complexity and service quality in edge computing.

Primal-Dual-Based Computation Offloading Method for Energy-Aware Cloud-Edge Collaboration

In the context of the Internet of Things (IoT), resource-constrained mobile edge computing (MEC) can no longer fully meet the needs of the rapidly growing number of mobile users; hence, cloud-edge collaborative computing has been developed. This paper focuses on the total energy consumption of the system and heterogeneity of scenarios, and a collaborative cloud-edge computation offloading approach with near real-time decision making is proposed. First, a general cloud-edge collaborative computation offloading model is abstracted from typical applications, and the energy consumption for edge and cloud offloading is calculated separately by considering both transmission and computational energy consumption. The problem is formulated as an integer linear program (ILP) with multidimensional resource constraints and is proven to be NP-hard. Then, a novel primal-dual computation offloading (PDCO) algorithm is designed to make near real-time offloading decisions one by one based on the sequential arrival of task requests. The approximation ratio of PDCO is derived through the weak duality property and the price-resource increment relationship. The experimental results show that under the guidance of total cost influenced by marginal prices, PDCO not only avoids blindly making offloading decisions but also effectively alleviates the shortage of resources on edge servers (ESs), approaching the optimal performance in terms of total energy consumption and resource utilization.

Deep reinforcement learning-based collaborative computation offloading and caching decision for internet of things

Deep reinforcement learning-based collaborative computation offloading and caching decision for internet of things

Value-based multi-agent deep reinforcement learning for collaborative computation offloading in internet of things networks

Value-based multi-agent deep reinforcement learning for collaborative computation offloading in internet of things networks

A Multi-edge Collaborative Computational Offloading Scheme Based on Game Theory

<p>A common approach in existing collaborative edge computing offloading schemes is to partition tasks into independent sub-tasks and offload them to participating servers. However, in practice, these sub-tasks often have dependencies, resulting in waiting time. To address this problem, we propose a collaborative computation offloading scheme based on Stackelberg game theory and graph theory (CCOSGG). First, we introduce a task clustering method based on graph theory, which uses task reconstruction and graph partition algorithm to cluster strongly related sub-tasks into appropriately sized clusters. Second, we use Stackelberg game theory to introduce an incentive mechanism that encourages remote edges to participate in the collaborative offloading. Finally, simulation results demonstrate that the proposed scheme can minimize latency and energy consumption at different network scales.</p> <p> </p>

Edge–Cloud Collaborative Computation Offloading for Mixed Traffic

Edge computing can avoid long network transmission delay, thus reducing the task response time. However, edge servers cannot respond quickly to massive computing requests due to limited computing power, which may lead to long queuing delays, thus failing to meet low latency requirements of delay-sensitive Internet of Things (IoT) applications. Furthermore, the per-computing-unit energy consumption in edge computing is much higher than that in cloud computing. This article studies the edge–cloud collaborative computing for energy efficiency while meeting the delay requirements of IoT applications in mobile edge computing systems. We formulate the energy efficient computation offloading problem for mixed tasks, including divisible and indivisible tasks. We theoretically analyze the relationship between delay guarantee and energy consumption in edge–cloud collaborative environment. We further analyze the system stability based on Lyapunov stability theory. Then, an energy efficient and delay guaranteed edge–cloud collaborative computation offloading algorithm is proposed to provide delay guarantee for delay-sensitive IoT applications while minimizing the energy consumption of the edge–cloud computing system. Theoretical analysis and experimental results show that, compared with the benchmark algorithms, our algorithm can provide strict delay guarantee for stringent-delay-sensitive IoT applications, and low delay for soft-delay-sensitive IoT applications, while consuming lower energy.

A many-objective evolutionary algorithm based on constraints for collaborative computation offloading

A many-objective evolutionary algorithm based on constraints for collaborative computation offloading

Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode

Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates the tasks using a synchronization policy based on set theory to improve the accuracy effectively, and finally achieves load balancing through processor allocation. The experimental results show that the method shortens the processing time by improving computational resource utilization and employs a heterogeneous distributed system to achieve high computing performance when processing large-scale task sets.

A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload.

With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

Dual-Timescale Resource Allocation for Collaborative Service Caching and Computation Offloading in IoT Systems

Edge computing has been envisioned as a key enabler to provide computation-intensive and delay-sensitive services in the future Internet of Things systems. By offloading the computational tasks to the edge server, both the service latency and energy consumption can be reduced. Since devices may request various types of computing services, caching appropriate services in the edge server to immediately provide computing resources can improve the quality of service. Nevertheless, it brings new challenges to jointly optimize the resource allocation, where the timeliness of caching and offloading operations are different. In this article, we first formulate the collaborative service caching and computation offloading as a dual-timescale resource allocation problem to minimize the costs of latency and energy consumption. Under this framework, a novel scheme based on hierarchical deep reinforcement learning is proposed to output collaborative caching and computing actions. Specifically, the proposed approach contains the service caching policy and the device computing policy with hierarchical action–value functions, which allows a flexible configuration of caching timescales. The simulation results demonstrate that the proposed policy outperforms the existing schemes on convergence performance and various parameters.

Multi-index evaluation learning-based computation offloading optimization for power internet of things

Cloud–edge-device collaborative computation offloading can provide flexible and real-time data processing services for massive resource-constrained devices in power internet of things (PIoT). However, the computation offloading optimization in PIoT still faces several challenges such as high computation offloading delay caused by uncertain information, coupling between task offloading and computation resource allocation, and degraded optimization performance due to the lack of multi-index consideration. To address the above challenges, we formulate a joint optimization problem of task offloading and computation resource allocation to minimize the average computation offloading delay. Specifically, a multi-index evaluation learning-based two-stage computation offloading (MINCO) algorithm is proposed to decouple the joint optimization problem into two-stage subproblems and solve them with evaluation and learning of multiple indexes including data flow characteristic, service priority, empirical average computation offloading delay, and empirical arm selection times. Simulation results show that compared with the baseline 1 and baseline 2 algorithms, MINCO improves the average computation offloading delay by 14.67% and 30.71%. Moreover, MINCO can evaluate different service priorities and data flow characteristics to meet different requirements of computation offloading delay.

Collaborative computation offloading and resource allocation based on dynamic pricing in mobile edge computing

The development of collaborative computing architecture in mobile edge computing provides an effective solution for the task processing of mobile devices with low computing capability, which can make up for computation resource and physical size restrictions of mobile devices and improve the performance of mobile communication networks in the future. In this paper, we focus on network congestion caused by increased load in hotspots. In particular, we consider the integrated network architecture of mobile edge computing (MEC) and device-to-device (D2D) communication to reduce the sum overhead of all task request users (TRUs). Accordingly, the computing offloading decision, wireless resource allocation strategy, computing resource allocation strategy, and assistant selection decision are optimized to minimize the sum overhead of the energy consumption and the cost of purchasing computing resource of all TRUs. The formulated overhead minimization problem is a mixed integer nonlinear programming problem. In order to tackle this problem, an algorithm based on the distance and the transaction price is first proposed to determine the target assistant user (TAU) of each TRU. Second, for a given TAU selection strategy, the original problem is further transformed into a convex problem and decomposed to make it more tractable. Finally, a distributed algorithm based on the alternating direction method of multipliers (ADMM) is adopted to solve the optimization problem. The simulation results prove the effectiveness of the proposed scheme in reducing user overhead.

Collaborative Computation Offloading in the Multi-UAV Fleeted Mobile Edge Computing Network via Connected Dominating Set

In these years, Unmanned Aerial Vehicle (UAV) is widely employed as a flying server into the Mobile Edge Computing (MEC) network to carry stochastic computation task offloaded from mobile users. However, currently, much efforts are made on the individual computation capability of each UAV during the offloading task, while neglecting the coordination advantage of multiple UAVs in a fleeted way, leading to low efficiency on computing massive bursty data. In this work, therefore, we propose a twofold computation offloading mechanism in a collaborative way for a multi-UAV assisted MEC network using the Connected Dominating Set (CDS). In particular, we solve a system energy efficiency-maximizing optimization problem by using a well-tailored Alternating Direction Method of Multipliers (ADMM) algorithm and Lyapunov optimization. In the proposed mechanism, we develop a two-stage computation task partitioning strategy and a user scheduling scheme with optimized UAV trajectory via designing a CDS virtual backbone network. The numerical results indicate that the superiority of our proposed mechanism compared with the benchmark algorithm.

Deep Reinforcement Learning-Based Cloud-Edge Collaborative Mobile Computation Offloading in Industrial Networks

With the rapid development of mobile industrial applications and due to the limited coverage of static edge servers, traditional edge computing technology has great limitations in dynamic environmental applications. This paper proposes a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading mechanism for satisfying the dynamic service requirements in industrial networks. Specifically, a three-layer network model of digital twins and a decentralized network of task resources are first constructed to handle the mobility of user terminals and the relevance of tasks. Then, based on the comprehensive consideration of mobility, associated tasks, computing resources and offloading decisions, an optimization problem is formulated to minimize the weighted sum of the execution delay and energy consumption of all tasks for all users. Additionally, a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) algorithm is proposed to solve this optimization problem. Based on the differences in each edge cloud, this algorithm sets the priority of the shared experience pool and selects the most effective experience samples to complete better learning and training. It also utilizes a distributed learning method to learn the probability of an approximate reward distribution and optimizes network parameters through cloud-edge collaboration to achieve faster optimal offloading decision. Finally, a large number of simulation results show that the proposed algorithm has the characteristics of fast convergence and high stability, and it can obtain the optimal offloading decision with the lowest total cost.

Collaborative Computation Offloading and Resource Allocation in Multi-UAV-Assisted IoT Networks: A Deep Reinforcement Learning Approach

In the fifth-generation (5G) wireless networks, Edge-Internet-of-Things (EIoT) devices are envisioned to generate huge amounts of data. Due to the limitation of computation capacity and battery life of devices, all tasks cannot be processed by these devices. However, mobile-edge computing (MEC) is a very promising solution enabling offloading of tasks to nearby MEC servers to improve quality of service. Also, during emergency situations in areas where network failure exists, unmanned aerial vehicles (UAVs) can be deployed to restore the network by acting as Aerial Base Stations and computational nodes for the edge network. In this article, we consider a central network controller who trains observations and broadcasts the trained data to a multi-UAV cluster network. Each UAV cluster head acts as an agent and autonomously allocates resources to EIoT devices in a decentralized fashion. We propose model-free deep reinforcement learning (DRL)-based collaborative computation offloading and resource allocation (CCORA-DRL) scheme in an aerial to ground (A2G) network for emergency situations, which can control the continuous action space. Each agent learns efficient computation offloading policies independently in the network and checks the statuses of the UAVs through Jain’s Fairness index. The objective is minimizing task execution delay and energy consumption and acquiring an efficient solution by adaptive learning from the dynamic A2G network. Simulation results reveal that our scheme through deep deterministic policy gradient, effectively learns the optimal policy, outperforming A3C, deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network and greedy-based offloading for local computation in stochastic dynamic environments.

Deep Reinforcement Learning for Collaborative Computation Offloading on Internet of Vehicles

With the increase of Internet of vehicles (IoVs) traffic, the contradiction between a large number of computing tasks and limited computing resources has become increasingly prominent. Although many existing studies have been proposed to solve this problem, their main consideration is to achieve different optimization goals in the case of edge offloading in static scenarios. Since realistic scenarios are complicated and generally time‐varying, these studies in static scenes are imperfect. In this paper, we consider a collaborative computation offloading in a time‐varying edge‐cloud network, and we formulate an optimization problem with considering both delay constraints and resource constraints, aiming to minimize the long‐term system cost. Since the set of feasible solutions to the problem is nonconvex, and the complexity of the problem is very large, we propose a Q‐learning‐based approach to solve the optimization problem. In addition, due to the dimensional catastrophes, we further propose a DQN‐based approach to solve the optimization problem. Finally, by comparing our two proposed algorithms with typical algorithms, the simulation results show that our proposed approaches achieve better performance. Under the same conditions, by comparing our two proposed algorithms with typical algorithms, the simulation results show that our proposed Q‐learning‐based method reduces the system cost by about 49% and 42% compared to typical algorithms. And in the same case, compared with the classical two schemes, our proposed DQN‐based scheme reduces the system cost by 62% and 57%.

Edge-Learning-Based Hierarchical Prefetching for Collaborative Information Streaming in Social IoT Systems

For smart cities, ubiquitous user connectivity and collaborative computation offloading are significant for the ever-increasing information requirements to promote the quality of citizens’ life. In this article, we design an information prefetching architecture, which investigates a hierarchical data storage and selection strategy, including local to edge and edge to cloud. Building on collected data in the social media system or sensor networks, we specifically focus on analyzing mobile terminals’ behaviors to assure the precision of our prefetching strategy in different kinds of information streaming. To assemble edge agents (EAs) prefetching, we also consider the characteristics of wireless backhaul. This scheme is carried out to optimize the EAs prefetching framework by the independent and joint action modules that are based on the theory of deep reinforcement learning (DRL). It paves a better way of collaborative edge computing (CEC) that can be built by using an independent/joint edge-learning model to help and promote the algorithm efficiency and cost-effectiveness. Furthermore, for hiding the information of data transmission between the cloud and the edge servers during data prefetching, this hierarchical scheme is designed as an implicit index maintained by edge servers. Our results show rationales on the obtainable performance of EAs architectures and their reciprocity with the dynamic change of mobile terminals’ requirements.

Collaborative Mobile Computation Offloading to Vehicle-Based Cloudlets

This paper investigates collaborative computation offloading in a vehicular network. Although there are increasingly more smart vehicles on roads, a significant number of legacy vehicles that are not equipped with powerful computing devices are expected to exist for a long time. When mobile devices located in these legacy vehicles require computation offloading, they can offload the tasks to nearby smart vehicles that are available to serve as cloudlet servers. Due to high mobility of the vehicles, multiple tasks of an application may have to be offloaded to different vehicle-based cloudlets. The offloading problem is formulated as a Markov decision process (MDP) by considering the randomness of the vehicle moving speeds and wireless channel conditions. The objective is to minimize the average completion time of the application. The complexity for solving the problem directly, however, is prohibitively high due to the large size of the state space and state transition probability matrix. The problem is solved by exploring the special structure of the state space, which helps reduce the computational complexity. A heuristic solution, namely, site-by-site and task-by-task (SSTT), is then proposed that makes the offloading decisions for individual tasks with much lower complexity. Simulation results show that the proposed SSTT solution not only achieves much lower average completion time, compared to executing all tasks locally and using distance-based offloading decisions, but also significantly reduces the energy consumption of the mobile device.