Joint Task Offloading Research Articles

Joint Task Offloading and Resource Allocation for Vehicular Edge Computing With Result Feedback Delay

In this paper, we study the problem of joint Task offloading and resource Allocation for vehicular edge computing with Result Feedback Delay (TARFD). Specifically, we consider a typical roadside unit (RSU) and vehicles within its coverage area, and optimize the task offloading decisions of vehicles as well as the uplink bandwidth allocation and the computation resources allocation on the RSU. The TARFD problem is formulated as a non-convex mixed integer nonlinear programming (MINLP) to minimize the average delay consisting of task offloading delay, task computation delay, and result feedback delay. We derive a lower bound of the optimum to the TARFD problem, based on which we propose an approximate algorithm of the TARFD problem, called A-TARFD. The A-TARFD algorithm can effectively deliver solutions for small-scale scenarios. To tackle large-scale scenarios, a low-complexity algorithm for the TARFD problem, called L-TARFD, is developed by constructing an iteratively updated sequence of locally tight approximate geometric programming (GP) problems. The L-TARFD algorithm can converge to a Karush-Kuhn-Tucker (KKT) point and forces the offloading decisions arbitrarily close to binary values. By comparison with the lower bound, simulation results show that the proposed two algorithms have near-optimal performance over a wide range of parameter settings.

Joint Power Control and Task Offloading in Collaborative Edge–Cloud Computing Networks

Mobile edge computing arises as a promising technology to allow mobile devices to offload delay-sensitive and computation-intensive tasks to the nearby edge servers. However, overloaded tasks from mobile devices (MDs) lead to a large latency due to limited computing and channel resources. To mitigate this situation, a collaborative edge-cloud computing network is considered in this paper. Based on power control and task offloading, we formulate a mixed-integer nonlinear programming (MINLP) problem to minimize the weighted sum of the energy consumption and the latency. This NP-hard problem is decomposed into a real variable problem and an integer linear programming problem. By leveraging the proposed extremevalue descent (EVD) method, the optimal power strategy is obtained by solving the real variable problem. The simplex method and branch-and-bound method are adopted to find the optimal offloading strategy. Although these two sub-problems are well solved, the solutions may be unsatisfactory for the original problem. To find a high-quality solution, we use an alternating optimization (AO) method to jointly optimize the decomposed problems. Theoretical analysis demonstrates that the EVD method converges at the rate of O(1/s) and the AO method can converge to a sub-optimal solution if not the optimal solution. Simulation results show that the proposed algorithms significantly outperform other benchmark schemes.

Joint Power Allocation and Task Offloading in NOMA Enhanced MEC for ABS-Assisted ITS

Joint Power Allocation and Task Offloading in NOMA Enhanced MEC for ABS-Assisted ITS

Federated Learning Assisted Deep Q-Learning for Joint Task Offloading and Fronthaul Segment Routing in Open RAN

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks, which requires routing. Since edge clouds do not have the same available computation resources and tasks’ computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD.

Joint DNN partitioning and task offloading in mobile edge computing via deep reinforcement learning

As Artificial Intelligence (AI) becomes increasingly prevalent, Deep Neural Networks (DNNs) have become a crucial tool for developing and advancing AI applications. Considering limited computing and energy resources on mobile devices (MDs), it is a challenge to perform compute-intensive DNN tasks on MDs. To attack this challenge, mobile edge computing (MEC) provides a viable solution through DNN partitioning and task offloading. However, as the communication conditions between different devices change over time, DNN partitioning on different devices must also change synchronously. This is a dynamic process, which aggravates the complexity of DNN partitioning. In this paper, we delve into the issue of jointly optimizing energy and delay for DNN partitioning and task offloading in a dynamic MEC scenario where each MD and the server adopt the pre-trained DNNs for task inference. Taking advantage of the characteristics of DNN, we first propose a strategy for layered partitioning of DNN tasks to divide the task of each MD into subtasks that can be either processed on the MD or offloaded to the server for computation. Then, we formulate the trade-off between energy and delay as a joint optimization problem, which is further represented as a Markov decision process (MDP). To solve this, we design a DNN partitioning and task offloading (DPTO) algorithm utilizing deep reinforcement learning (DRL), which enables MDs to make optimal offloading decisions. Finally, experimental results demonstrate that our algorithm outperforms existing non-DRL and DRL algorithms with respect to processing delay and energy consumption, and can be applied to different DNN types.

Joint task offloading and resource allocation for multi-user and multi-server MEC networks: A deep reinforcement learning approach with multi-branch architecture

Mobile Edge Computing (MEC) is a promising computing paradigm in the context of 5G networks, as it enables the migration of workloads from User Equipments (UEs) to nearby MEC servers, thereby providing additional computing resources to UEs. In this paper, we propose a joint optimization approach to offloading decisions and resource allocation in a multi-user and multi-server MEC system, which operates in a time-varying environment. Our objective is to minimize the average task latency and discard rate under the constraints of latency and limited computing resources of the server. While traditional optimization methods have been used to solve computational offloading problems in static environments, these methods are not suitable for time-varying systems. Deep reinforcement learning can be an effective method for solving optimization problems in time-varying environments, as it can be used to adjust strategies in real-time in response to changes in the environment. However, the increased number of UEs in the system leads to a combinatorial increase in the number of possible actions, making it difficult for the algorithm to learn. To address this issue, we propose a multi-branch network based Deep Q Network (DQN) algorithm called Branch Deep Q Network (BDQN), which modifies the action generation network into a multi-branch network structure, each branch generates one-dimensional action. This modification makes the number of network outputs increase linearly. Numerical results show that the BDQN algorithm outperforms other baseline algorithms in terms of average task latency and discard rate.

Joint task offloading and UAVs deployment for UAV-assisted mobile edge computing

Recent advances in artificial intelligence (AI) have impacts on the development of communication networks. Diversified applications and scenarios put forward AI-native requirement for communication network. The network is envisioned to offer high data rate and low-latency communications, pervasive artificial intelligence, full coverage and programmable service. With the aid of AI, autonomous unmanned aerial vehicles (UAVs) play an indispensable role in the emerging scenarios. Owing to their flexibility and scalability, UAVs can be utilized as the mobile platform to deliver various kinds of services in dynamic and adversarial environments, especially the emergency scenarios. In this article, we deal with the post-disaster scenarios that UAVs, equipped with computing servers, work with the remaining infrastructures to provide communication and computing services for the ground mobile users (GMUs). With the aim of minimizing the energy cost and computing delay of task offloading, a two-layer optimization mechanism that can dynamically orchestrate the task offloading decision and the UAVs deployment is proposed. In lower layer, the problem of making offloading decisions is formulated into an evolutionary game. Replicator dynamics is exploited to help users to make decisions. In upper layer, a clustering approach for users is introduced to avoid unbalanced load distribution. By suggesting the locations and number of UAVs based the clusters, system cost is further reduced. The fairness of service load among UAVs is also achieved. Numerical results demonstrate the effectiveness of the proposed two-layer optimization mechanism.

Joint Task Offloading and Resource Allocation for Space–Air–Ground Collaborative Network

The space–air–ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the issue of spectrum demand for the HAP drone to meet the access of a large number of users. In addition, the long propagation distance between devices and the HAP drone, and between the HAP drone and LEO satellites, will lead to high data transmission energy consumption. Motivated by these factors, we introduce a space–air–ground collaborative network that employs the non-orthogonal multiple access (NOMA) technique, enabling all ground devices to access the HAP drone. Therefore, all devices can share the same communication spectrum. Furthermore, the HAP drone can process part of the ground devices’ tasks locally, and offload the rest to satellites within the visible range for processing. Based on this system, we formulate a weighted energy consumption minimization problem considering power control, computing frequency allocation, and task-offloading decision. The problem is solved by the proposed low-complexity iterative algorithm. Specifically, the original problem is decomposed into interconnected coupled subproblems using the block coordinate descent (BCD) method. The first subproblem is to optimize power control and computing frequency allocation, which is solved by a convex algorithm after a series of transformations. The second subproblem is to make an optimal task-offloading strategy, and we solve it using the concave–convex procedure (CCP)-based algorithm after penalty-based transformation on binary variables. Simulation results verify the convergence and performance of the proposed iterative algorithm compared with the two benchmark algorithms.

Federated Multiagent Actor–Critic Learning Task Offloading in Intelligent Logistics

Intelligent logistics empowered by artificial intelligence (AI) has become an inevitable trend in the development of modern logistics, thus a convenient and efficient logistics system has attracted widespread attention. However, how to use AI to execute computation-intensive applications on resource-constrained logistics vehicles still faces enormous challenges. For the dependent applications in intelligent logistics, this paper investigates a task dynamic offloading strategy for logistics vehicles-edge collaboration with multiple dependent tasks, considering the inter-task dependency, to guarantee the quality of service (QoS) requirements of logistics vehicles. Firstly, the dependent application ARCore is modeled and transformed into a model with a linear execution sequence. Then, based on this task model, the joint task offloading and resource allocation problem is formulated. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the delay tolerance and computing resource constraints of the tasks. Furthermore, we propose a federated logistics vehicles-edge collaborative computation (FECC) offloading framework to solve the optimization problem, which only requires each agent to share its model parameters without sharing local training data, thereby reducing the computation complexity and signaling overhead of the multi-agent training process. Numerical results show that the proposed strategy has significant advantages in terms of total system cost compared to the baseline strategy.

Joint Task Offloading and Dispatching for MEC With Rational Mobile Devices and Edge Nodes

Multi-access Edge Computing has come forth as a promising paradigm to provide low-latency computing service to mobile end users. Its basic idea is to deploy computation resources at the edge of core networks such as wireless access points, and then users can offload their tasks to nearby edge nodes for processing. Plenty of works have well studied the task offloading problem, aiming to reduce task completion delays. Also, a few recent works have focused on task dispatching among edge nodes to balance their workloads and improve resource utilization. In this work, we jointly consider the task offloading and dispatching problem in an edge computing system with interconnected access points. Furthermore, we assume both end devices and access points are rational, which only care about their own benefits. To solve the joint problem, we firstly formulate it as a multi-leader multi-follower Stackelberg game, and rigorously prove the existence of a Stackelberg equilibrium. Then, we propose two algorithms for task offloading and dispatching, respectively. Extensive simulations are conducted to show the superiority of our proposed approach. We also demonstrate that an upper bound with a constant approximation ratio is achieved by our approach.

Joint Task Offloading and Resource Allocation for Device-Edge-Cloud Collaboration With Subtask Dependencies

With more computational intensive applications deployed involving mobile edge computing (MEC), the collaboration among mobile devices, edge and cloud servers becomes an effective mechanism to fully utilize all available distributed computing resources. However, two main challenges have yet to be addressed to enable this three-way collaboration for securing necessary computational resources and further guaranteeing the quality of service (QoS) of task handling. The first challenge is related to the partitioning of an application task into several dependent subtasks and schedule them among the collaborating device-edge-cloud (DEC). The second is focused on the allocation of necessary computing resources of device, edge and cloud servers for effective subtask handling. To this end, we study the joint task offloading and resource allocation for DEC collaboration in this paper by formulating a new optimization problem with the objective of minimizing the task handling latency. To solve this problem, we decompose the original problem into two subproblems, which include the first one of calculating the optimal task partitioning ratio by mathematical analytical method, as well as the second on using the Lagrangian dual (LD) method for obtaining the optimal task offloading and resource allocation policy. Finally, we conduct simulation experiments on a real-life dataset obtained from the central business district (CBD) of Melbourne, Australia, and the experimental results validate the efficacy of our approach in minimizing latency.

Joint task offloading and resource allocation in mixed edge/cloud computing and blockchain empowered device-free sensing systems

Recent studies have shown that wireless signals can not only be used as communication medium for data transmission, but also can be employed for device-free sensing (DFS). However, the sensed raw big data processing needs large amount of processing resources. Moreover, the sensed data are usually privacy-sensitive, where blockchain can be employed to guarantee the privacy related issues, and however, blockchain tasks are usually also computation-intensive. To provide the processing capabilities for sensed big data processing and blockchain task executing in DFS, mobile edge computing (MEC) and cloud computing are essential enabling technologies. In this paper, we consider a multiuser mobile offloading network consisting a edge node (or fog node) and a remote cloud server. The tasks could be processed locally, or offloaded to the edge nodes, or further migrated to the cloud relayed by the edge node. We formulate the offloading problem as the joint optimization of task offloading decision making of all the users, the computation resource allocation among the edge-executing applications, and radio resource assignment among all the remote-processing applications, aiming to minimize the maximum weighted cost of all the users. It is demonstrated that the problem is NP-hard. To tackle this challenge, we decoupled the problem into subproblems, where offloading decisions are obtained using semi-definite relaxation (SDR), and next, a swarm intelligence algorithm, i.e., fireworks algorithm, is adopted in radio resource allocation. Simulation results exhibit that as a result of the collaboration of the collaborate of edge and cloud, the proposed joint algorithm could achieves nearly optimal performance in the aspects of energy consumption and delay compared with other benchmark algorithms.

Dependent tasks offloading in mobile edge computing: A multi-objective evolutionary optimization strategy

Due to the proliferation of applications such as virtual reality and online games with high real-time requirements, Mobile Edge Computing (MEC) has become a promising computing paradigm that can improve user experience and reduce the task offloading latency. The cloud–edge-end collaborative offloading further addresses the problem of insufficient computing resources of edge servers owing to large-scale computing-intensive applications in MEC. However, existing offloading solutions often ignore the important factor of economic cost, making it hard for these solutions to achieve a sustainable cloud–edge-end collaborative computation. To this end, this paper considers a multi-user multi-server, cloud–edge-end collaborative offloading scenario in the presence of dependent offloading tasks for the sake of maximizing rewards and minimizing latency. Each user issues a computing-intensive application consisting of multiple dependent tasks, which are offloaded collaboratively by various computational resources. With the goal of maximizing the yield of offloading for users and server providers, a multi-objective optimization problem of joint task offloading and execution rewards is studied. Technically, a multivariate multi-objective optimization problem with three objectives is modeled. An efficient multi-objective evolutionary optimization algorithm based on MOEA/D is then developed to solve the latency minimization and reward maximization problems. Extensive simulation results verify the effectiveness of the algorithm and illustrate that the proposed algorithm can significantly improve user offloading benefits. In addition, a scalability evaluations of our proposed algorithm is conducted for demonstrating its feasibility in large-scale task offloading scenarios.

Game-Based Task Offloading and Resource Allocation for Vehicular Edge Computing With Edge-Edge Cooperation

Vehicular Edge Computing (VEC) enables task offloading from vehicles to the edge servers deployed on Road Side Units (RSUs), thus enhancing the task processing performance of the vehicles. However, in a multi-RSU VEC scenario, the uneven geographical distribution of the vehicles naturally causes the load imbalance among the edge servers and leads to the overload and performance degradation problems of the edge servers in hot areas. To this end, in this paper, we propose a joint task offloading and resource allocation for VEC with edge-edge cooperation, in which the tasks offloaded to a high-load edge server can be further offloaded to the other low-load edge servers. Our objective is to minimize the total task processing delay of all the vehicles while guaranteeing the task processing delay tolerance and the holding time of each vehicle. An M/M/1 queue is used to model the task queuing and task computing processes on each RSU. An exact potential game is adopted to model the competition process for the task offloading among the RSUs. A two-stage iterative algorithm is designed to decompose the optimization problem into two stages and solve them iteratively. We analyze the computational complexity of the algorithm and conduct extensive simulations by varying different crucial parameters. The superiority of our scheme is demonstrated in comparison with 3 other reference schemes.

Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks

With the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represents a non-trivial problem. In this paper, we propose a non-orthogonal multiple access (NOMA)-based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the quality of experience (QoE) of the UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimensionality, which, respectively, select the offloading and resource allocation action, as well as the resolution scaling action. The experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of edge computing and has better performance in terms of convergence compared to other baseline schemes.

Joint Microservices Caching and Task Offloading Framework in VEC Based on Deep Reinforcement Learning

Vehicle capacities and intelligence are rapidly increasing, which will likely support a wide range of novel and interesting applications. However, these resources are not effectively utilized. To take advantage of these invaluable capabilities in smart vehicles, they can be used in the cloud environment and can be operated through distributed computing platforms in order to benefit from their combined processing power, storage capacity, and memory resources. Vehicular edge computing (VEC) is a promising field that allows computing tasks to be transferred from cloud servers to vehicular edge servers for processing, allowing data and apps to be placed closer to vehicles (users).
 This paper proposes a framework that combines two modules, the first one for managing microservice caching in vehicle-mounted edge networks, such that we use cluster-based caching technique to deal with the case where similar microservices are frequently requested in VEC. The second one integrates the computational capabilities of the edge servers with the capabilities of vehicles to perform task offloading in a collaborative manner.
 Our solution addresses the limitations of existing edge computing platforms during peak times by combining microservices caching with computational task offloading to improve overall system performance.

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial vehicle (UAV)-based edge computing is a good solution. However, the UAV cannot accomplish the computing tasks and efficiently achieve better resource allocation considering the limited communication and computing resources of the UAV. In this paper, a multi-UAV-assisted mobile edge computing (MEC) system is considered where multiple UAVs cooperate to provide a service to IoT devices. We formulate an optimization function to minimize the energy consumption of a multi-UAV-assisted MEC system. The optimization function is a complex problem with non-convex and multivariate coupling. Thus, a joint UAV deployment and task scheduling optimization algorithm are designed to achieve optimal values of UAV numbers, the hovering position of each UAV, and the best strategy for offloading and resource allocation. Experimental results demonstrate that the algorithm has positive convergence performance and can accomplish more tasks under the constraint of delay compared to the two benchmark algorithms. The proposed algorithm can effectively reduce the system energy consumption compared to the two state-of-the-art algorithms.

Latency Optimization for Hybrid GEO–LEO Satellite-Assisted IoT Networks

Benefiting from the development of satellite onboard processing capability, the orbital computing can be realized by deploying edge computing servers on satellites to reduce the task processing latency. However, edge computing based on geostationary earth orbit (GEO) or low earth orbit (LEO) alone can hardly meet the latency requirements of satellite assisted internet of things (SIoT) services. Moreover, the uneven distribution of tasks generated by SIoT devices will also cause the load unbalancing among different satellites. In this paper, hybrid GEO-LEO SIoT networks is investigated with joint computing and communication resource allocation. To tackle the load unbalancing problem, tasks generated by SIoT devices can be processed by collaborative LEO satellites or forwarded to gateways on ground via GEO satellite. Thus, the joint task offloading, communication and computing resources allocation for the hybrid SIoT network can be formulated as a mixed integer dynamic programming problem with satellites-ground cooperation and inter-satellite cooperation via the inter-satellite links. Then, an intelligent task offloading and multi-dimensional resources allocation algorithm (TOMRA) is proposed to minimize the latency of task offloading and processing. Firstly, a method base on deep reinforcement learning is utilized to solve the subproblem of task offloading and channel allocation. And then, convex optimization is adopted to solve the sub-problem of computing resource allocation under fixed offloading and channel allocation decisions. Simulation results show that the proposed TOMRA can achieve better performance than the reference schemes.

Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes

In an internet of vehicle (IoV) scenario, vehicular edge computing (VEC) exploits the computing capabilities of the vehicles and roadside unit (RSU) to enhance the task processing capabilities of the vehicles. Resource management is essential to the performance improvement of the VEC system. In this paper, we propose a joint task offloading and resource allocation scheme to minimize the total task processing delay of all the vehicles through task scheduling, channel allocation, and computing resource allocation for the vehicles and RSU. Different from the existing works, our scheme: 1) considers task diversity by profiling the tasks of the vehicles by multiple attributes including data size, computation amount, delay tolerance, and task type; 2) considers vehicle classification by dividing the vehicles into 4 sets according to whether they have task offloading requirements or provide task processing services; 3) considers task processing flexibility by deciding for each vehicle to process its tasks locally, to offload the tasks to the RSU via V2I (Vehicle to Infrastructure) connections, or to the other vehicles via V2V (Vehicle to Vehicle) connections. An algorithm based on the Generalized Benders Decomposition (GBD) and Reformulation Linearization (RL) methods is designed to optimally solve the optimization problem. A heuristic algorithm is also designed to provide the sub-optimal solution with low computational complexity. We analyze the convergence and complexity of the proposed algorithms and conduct extensive simulations in 6 scenarios. The simulation results demonstrate the superiority of our scheme in comparison with 4 other schemes.

Multi-Slice Joint Task Offloading and Resource Allocation Scheme for Massive MIMO Enabled Network

Multi-Slice Joint Task Offloading and Resource Allocation Scheme for Massive MIMO Enabled Network