Offloading Strategy Research Articles

Dynamic adaptive workload offloading strategy in mobile edge computing networks

With the increasing popularity of mobile devices and explosive growth of task processing requirements, edge computing attracts more attention from researchers nowadays since it can improve the QoS and utilize resources of the cloudlets, including mobile devices and base stations, as much as possible. In a Mobile Edge Computing (MEC) network, the workload offloading problem is quite important since it directly influences the latency of the task processing, and many efficient algorithms have been proposed to deal with it. However, most of the existing algorithms only consider the static or quasi-static scenario, and are not suitable for a system with several fast-moving devices. Since that more and more mobile devices with high speed are involved in a MEC system, a new workload offloading strategy is required to adapt to such a dynamic scenario. In this paper, we sufficient consider the dynamic properties of a MEC system, and proposed a dynamic adaptive workload offloading algorithm based on Lyapunov theories and an FC-LSTM based schedule determining algorithm to balance the workload of different cloudlets and minimize the weighted average energy and time consumption of mobile devices. Theoretical analysis and extensive experimental results show that all the proposed algorithms achieve high performance in terms of energy consumption, convergence and latency.

Dynamic Selection Slicing-Based Offloading Algorithm for In-Vehicle Tasks in Mobile Edge Computing

With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of vehicular tasks are represented as directed acyclic graphs, and network slices are integrated within the edge server. The Dynamic Selection Slicing-based Offloading Algorithm for in-vehicle tasks in MEC (DSSO) is proposed. First, a computational offloading model for vehicular tasks is established based on available resources, wireless channel state, and vehicle loading level. Second, the solution of the model is transformed into a Markov decision process, and the combination of the DQN algorithm and Dueling Network from deep reinforcement learning is used to select the appropriate slices and dynamically update the optimal offloading strategy for in-vehicle tasks in the effective interval. Finally, an experimental environment is set up to compare the DSSO algorithm with LOCAL, MINCO, and DJROM, the results show that the system energy consumption of DSSO algorithm resources is reduced by 10.31%, the time latency is decreased by 22.75%, and the ratio of dropped tasks is decreased by 28.71%.

An O-MAPPO scheme for joint computation offloading and resources allocation in UAV assisted MEC systems

The combination of mobile edge computing (MEC) systems with unmanned aerial vehicle (UAV) has gained a high profile in recent years due to its high flexibility and ability to handle intensive tasks. The computation offloading strategy and the resource allocation scheme affect the system performance significantly. Moreover, the system complexity increases with the numbers of users and servers exponentially. It is challenging to consider jointly the computation offloading and the resource allocation in MEC systems that have multiple users and multiple servers. This paper formulates the joint resources management in the UAV assisted MEC network as a partially observable markov decision process and proposes an online multi-agent proximal policy optimization (O-MAPPO) scheme to improve the energy efficiency while guaranteeing the requirements in task, power consumption, computation, and time. Specifically, users and servers are set as agents. All agents cooperatively make decisions of computation offloading and resource allocation to maximize the energy efficiency. Simulation results show that the O-MAPPO scheme significantly outperforms benchmark algorithms in robustness and stability.

Delay Minimization Using Hybrid RSMA-TDMA for Mobile Edge Computing

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget.

Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm

With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are urgently needed, which can solve the insufficient computing capacity of vehicle terminals. Based on an improved gray-wolf algorithm designed, an adaptive joint offloading strategy for vehicular edge computing is proposed, which does not require cloud-computing support. This strategy first establishes an offloading computing model, which takes task computing delays, computing energy consumption, and MEC server computing resources as constraints; secondly, a system-utility function is designed to transform the offloading problem into a constrained system-utility optimization problem; finally, the optimal solution to the computation offloading problem is obtained based on an improved gray-wolf optimization algorithm. The simulation results show that the proposed strategy can effectively reduce the system delay and the total energy consumption.

Deep Reinforcement Learning for Shared Offloading Strategy in Vehicle Edge Computing

Vehicular edge computing (VEC) effectively reduces the computing load of vehicles by offloading computing tasks from vehicle terminals to edge servers. However, offloading of tasks increase in quantity the transmission time and energy of the network. In order to reduce the computing load of edge servers and improve the system response, a shared offloading strategy based on deep reinforcement learning is proposed for the complex computing environment of Internet of Vehicles (IoVs). The shared offloading strategy exploits the commonality of vehicles task requests, similar computing tasks coming from different vehicles can share the computing results of former task submitted. The shared offloading strategy can be adapted to the complex scenarios of the IoVs. Each vehicle can share the offloading conditions of the VEC servers, and then adaptively select three computing modes: local execution, task offloading, and shared offloading. In this article, the network state and offloading strategy space are the input of the deep reinforcement learning (DRL). Through the DRL, each task unit selects the offloading strategy with the optimal energy consumption at each time period in the dynamic IoVs transmission and computing environment. Compared with the existing proposals and DRL-based algorithms, it can effectively reduce the delay and energy consumption required for tasks offloading.

Delay-Minimization Offloading Scheme in Multi-Server MEC Networks

In this letter, we consider a multi-server mobile edge computing (MEC) network where a user with limited computation ability aims to complete an intensive computation task in a short time under the help of multiple edge servers. We study the delay-minimization offloading schemes with and without user’s energy constraint, which involve the joint optimization of task allocation ratio for local computation, the wireless offloading manner (i.e., non-orthogonal multiple access (NOMA) offloading or single-server offloading mode), and the server pair selection. Specifically, the joint optimal solutions are derived in semi-closed forms and the critical conditions of switching offloading manners are derived in closed form. Numerical results corroborate the superiority of our proposed offloading scheme in terms of overall latency under various settings.

Joint Multi-Task Offloading and Resource Allocation for Mobile Edge Computing Systems in Satellite IoT

For multi-task mobile edge computing(MEC) systems in satellite Internet of Things(IoT), there are dependencies between different tasks, which need to be collected and jointly offloaded. It is crucial to allocate the computing and communication resources reasonably due to the scarcity of satellite communication and computing resources. To address this issue, we propose a joint multi-task offloading and resource allocation scheme in satellite IoT to improve the offloading efficiency. We first construct a novel resource allocation and task scheduling system in which tasks are collected and decided by multiple unmanned aerial vehicles(UAV) based aerial base stations, the edge computing services are provided by satellites. Furthermore, we investigate the multi-task joint computation offloading problem in the framework. Specifically, we model tasks with dependencies as directed acyclic graphs(DAG), then we propose an attention mechanism and proximal policy optimization (A-PPO) collaborative algorithm to learn the best offloading strategy. The simulation results show that the A-PPO algorithm can converge in 25 steps. Furthermore, the A-PPO algorithm reduces cost by at least 8.87 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> by default compared to several baseline algorithms. In summary, this paper provides a new insight for cost optimization of multi-task MEC system in satellite IoT.

Achieving Cooperative Mobile-Edge Computing Using Helper Scheduling

This paper investigates computing task offloading from an Internet-of-Thing (IoT) device with limited transmit power to a mobile-edge computing (MEC) server located beyond the communication range of the IoT device. We propose an opportunistic cooperative offloading (OCO) strategy that recruits the IoT device’s nearby spatially random idle-state users as helpers and opportunistically schedules one of them to partially execute the latency-critical task, and forwards the rest portion of the task to the MEC server. For the OCO strategy, we investigate the helper scheduling under three cases of system information availability, i.e., the global, partial, and distance information cases, and develop an offloading-outage optimal scheduling scheme for each case. For each scheduling scheme, an approximate expression is derived for the offloading-outage probability, with which the achieved diversity order is also theoretically evaluated. Simulation results verify our performance analysis for the OCO strategy using helper scheduling and show its achieved offloading-outage/energy consumption reduction over multi-helper cooperative offloading that fully uses all helpers for cooperation. In addition, the advantages of the proposed helper scheduling schemes over existing scheduling schemes are also demonstrated by simulations.

Data-Driven Task Offloading Method for Resource-Constrained Terminals via Unified Resource Model

In recent years, with an increasing number of Internet of Things (IoT) devices, general cloud computing mode is hard to process large amounts of data with high quality of service (QoS). Edge computing is put forward to relieve the pressure of cloud servers, but most of them only focused on allocating tasks depending on cloud servers or edge servers with virtualization technology. Resource-constrained smart mobile terminals (RC-SMTs) produce most of the data to be processed but some of them are usually not able to support even Docker technology. The cooperative computation capacity of RC-SMTs is potential but is often neglected by most researchers. However, there is little research focus on edge computing only among RC-SMTs without computing ability supported by servers. For this reason, this paper proposes a framework named data-drive task offloading with a unified resource model (DDTO-URM) to manage the limited resource of IoT which enables the allocation of tasks constantly generated from the edge of the network. Then a meta-heuristic algorithm called grouped crossover genetic algorithm (GCGA) is designed to obtain task offloading strategy under a resource-constrained environment. As a result, the computation capacity of the system is enhanced to cover the requirement by improving the utilization of RC-SMTs. Through the analysis of simulation, the proposed approach can deal with the problem of DDTO-URM better than benchmark algorithms under constraints, ensuring the real-time and ultra-lightweight of the collaborative edge computing system.

PASTO: Enabling Secure and Efficient Task Offloading in TrustZone-Enabled Edge Clouds

Offloading tasks to edge servers has been regarded as a potential way to solve the computation resource poverty problem of the end devices like autonomous vehicles. However, due to the sharing and openness features of edge computing, it raises severe security problems. In order to combat such issues, Trust Execution Environment (TEE), such as TrustZone, is advocated to strength the security of task offloading to edge computing in the hardware level. For the exploration of TrustZone, the inevitable involvement of cryptographic operations make existing offloading strategies not applicable, or not efficient enough, any more. In addition, TrustZone does not allow multiple tasks to coexist and execute on one CPU core at the same time. Taking the above issues into consideration, we investigate a secure task offloading problem for minimizing the total task completion time under the constraint of energy budget. To address this problem, we propose a Priority-aware Secure Task Offloading (PASTO) algorithm and evaluate the performance of PASTO by both numerical analysis and prototype based experiments. All the experiment results show that PASTO can effectively reduce the total task completion time in comparison with other state-of-the-art offloading approaches in TrustZone-enabled edge clouds.

Latency minimization for multiuser computation offloading in fog-radio access networks

Recently, the Fog-Radio Access Network (F-RAN) has gained considerable attention, because of its flexible architecture that allows rapid response to user requirements. In this paper, computational offloading in F-RAN is considered, where multiple User Equipments (UEs) offload their computational tasks to the F-RAN through fog nodes. Each UE can select one of the fog nodes to offload its task, and each fog node may serve multiple UEs. The tasks is computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link. In order to compute all UEs' tasks quickly, joint optimization of UE-Fog association, radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs. This min-max problem is formulated as a Mixed Integer Nonlinear Program (MINP). To tackle it, first, MINP is reformulated as a continuous optimization problem, and then the Majorization Minimization (MM) method is used to find a solution. The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM, thereby reducing the complexity of MM iteration. In addition, a cooperative offloading model is considered, where the fog nodes compress-and-forward their received signals to the cloud. Under this model, a similar min-max latency optimization problem is formulated and tackled by the inexact MM. Simulation results show that the proposed algorithms outperform some offloading strategies, and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.

Computing Offloading Strategy in Mobile Edge Computing Environment: A Comparison between Adopted Frameworks, Challenges, and Future Directions

With the proliferation of the Internet of Things (IoT) and the development of wireless communication technologies such as 5G, new types of services are emerging and mobile data traffic is growing exponentially. The mobile computing model has shifted from traditional cloud computing to mobile edge computing (MEC) to ensure QoS. The main feature of MEC is to “sink” network resources to the edge of the network to meet the needs of delay-sensitive and computation-intensive services, and to provide users with better services. Computation offloading is one of the major research issues in MEC. In this paper, we summarize the state of the art in task offloading in MEC. First, we introduce the basic concepts and typical application scenarios of MEC, and then we formulate the task offloading problem. In this paper, we analyze and summarize the state of research in the industry in terms of key technologies, schemes, scenarios, and objectives. Finally, we provide an outlook on the challenges and future research directions of computational offloading techniques and indicate the suggested direction of follow-up research work.

Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles

An edge computing offloading strategy was proposed with the goal of addressing the issue of low edge computing efficiency and service quality in the multi-service and multi-user intersections of networked vehicles. This strategy took into account all relevant factors, including the matching of users and service nodes, offloading ratio, bandwidth and computing power resource allocation, and system energy consumption. It is mainly divided into 2 tasks: (1) Service node selection: A fuzzy logic-based service node selection algorithm (SNFLC) is proposed. The linear equation for node performance value is determined through fuzzy reasoning by specifying three performance indexes as input. Gradient descent method is used to find the optimal value of the objective function, and the Lyapunov criterion coefficient is introduced to improve the stability of the algorithm. (2) Offload ratio and resource allocation are solved: The coupling between offload ratio and bandwidth resource allocation is confirmed by relaxing integer variables because the optimization goal problem is a NP problem, and the issue is divided into two sub-problems. At the same time, a low-complexity alternate iteration resource allocation algorithm (LC-IRA) is proposed to solve the bandwidth resource and computational power resource allocation. According to simulation results, the performance of genetic ant colony algorithm (G_ACA), non orthogonal multiple access technology (NOMA) and LC-IRA are improved by 26.5%, 31.37%, and 45.52%, respectively, compared with the random unloading allocation (RUA) and average distribution (AD).

Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAV Networks: A Metaheuristic Approach

Nowadays, unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) systems have been exploited as a promising solution for providing computation services to mobile users outside of terrestrial networks. However, it remains challenging for standalone UAVs to meet the computation requirement of numerous users due to their limited computation capacity and battery lives. Therefore, we propose a collaborative scheme among UAVs to share the workload between them. Furthermore, this work is the first to consider the task topology of offloading in MEC-enabled UAVs networks while restricting their power consumption. We study the task topology, in which a task consists of a set of sub-tasks, and each sub-task has dependencies upon other sub-tasks. In the real world, sub-tasks with dependencies must wait for their preceding sub-tasks to complete before being executed, and this affects the offloading strategy. Next, we formulate an optimization problem to minimize the average latency of users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem is NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two sub-problems: offloading decision problem and communication resource allocation problem. Then, a meta-heuristic method is proposed to find the sub-optimal solution to the former problem, while the latter problem is solved by using convex optimization. Finally, we conduct simulation experiments to prove that our proposed offloading technique outperforms several benchmark schemes in minimizing the average latency of users for dependency tasks and achieving higher uplink transmission rates.

Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm

In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption.

Task offloading optimization mechanism based on deep neural network in edge-cloud environment

With the rise of edge computing technology and the development of intelligent mobile devices, task offloading in the edge-cloud environment has become a research hotspot. Task offloading is also a key research issue in Mobile CrowdSourcing (MCS), where crowd workers collect sensed data through smart devices they carry and offload to edge-cloud servers or perform computing tasks locally. Current researches mainly focus on reducing resource consumption in edge-cloud servers, but fails to consider the conflict between resource consumption and service quality. Therefore, this paper considers the learning generation offloading strategy among multiple Deep Neural Network(DNN), proposed a Deep Neural Network-based Task Offloading Optimization (DTOO) algorithm to obtain an approximate optimal task offloading strategy in the edge-cloud servers to solve the conflict between resource consumption and service quality. In addition, a stack-based offloading strategy is researched. The resource sorting method allocates computing resources reasonably, thereby reducing the probability of task failure. Compared with the existing algorithms, the DTOO algorithm could balance the conflict between resource consumption and service quality in traditional edge-cloud applications on the premise of ensuring a higher task completion rate.

Divergent Selection Task Offloading Strategy for Connected Vehicles Based on Incentive Mechanism

With the improvements in the intelligent level of connected vehicles (CVs), travelers can enjoy services such as self-driving, self-parking and audiovisual entertainment inside the vehicle, which place extremely high demands on the computing power of onboard systems (OBSs). However, the arithmetic power of a single CV often cannot meet the diverse service demands of the in-vehicle system. As a new computing paradigm, task offloading based on vehicular edge computing has significant advantages in remedying the shortcomings of single-CV computing power and balancing the allocation of computing resources. This paper studied the computational task offloading of high-speed connected vehicles without the help of roadside edge servers in certain geographic areas. User vehicles (UVs) with insufficient computing power offload some of their computational tasks to nearby CVs with abundant resources. We explored the high-speed driving model and task classification model of CVs to refine the task offloading process. Additionally, inspired by game theory, we designed a divergent selection task offloading strategy based on an incentive mechanism (DSIM), in which we balanced the interests of both the user vehicle and service vehicles. CVs that contribute resources are rewarded to motivate more CVs to join. A DSIM algorithm based on a divergent greedy algorithm was introduced to maximize the total rewards of all volunteer vehicles while respecting the will of both the user vehicle and service vehicles. The experimental simulation results showed that, compared with several existing studies, our approach can always obtain the highest reward for service vehicles and lowest latency for user vehicles.

Task offloading strategy and scheduling optimization for internet of vehicles based on deep reinforcement learning

Driven by the construction of smart cities, networks and communication technologies are gradually infiltrating into the Internet of Things (IoT) applications in urban infrastructure, such as automatic driving. In the Internet of Vehicles (IoV) environment, intelligent vehicles will generate a lot of data. However, the limited computing power of in-vehicle terminals cannot meet the demand. To solve this problem, we first simulate the task offloading model of vehicle terminal in Mobile Edge Computing (MEC) environment. Secondly, according to the model, we design and implement a MEC server collaboration scheme considering both delay and energy consumption. Thirdly, based on the optimization theory, the system optimization solution is formulated with the goal of minimizing system cost. Because the problem to be resolved is a mixed binary nonlinear programming problem, we model the problem as a Markov Decision Process (MDP). The original resource allocation decision is turned into a Reinforcement Learning (RL) problem. In order to achieve the optimal solution, the Deep Reinforcement Learning (DRL) method is used. Finally, we propose a Deep Deterministic Policy Gradient (DDPG) algorithm to deal with task offloading and scheduling optimization in high-dimensional continuous action space, and the experience replay mechanism is used to accelerate the convergence and enhance the stability of the network. The simulation results show that our scheme has good performance optimization in terms of convergence, system delay, average task energy consumption and system cost. For example, compared with the comparison algorithm, the system cost performance has improved by 9.12% under different task sizes, which indicates that our scheme is more suitable for highly dynamic Internet of Vehicles environment.

Intelligent computing for WPT–MEC-aided multi-source data stream

Due to its low latency and energy consumption, edge computing technology is essential in processing multi-source data streams from intelligent devices. This article investigates a mobile edge computing network aided by wireless power transfer (WPT) for multi-source data streams, where the wireless channel parameters and the characteristic of the data stream are varied. Moreover, we consider a practical communication scenario, where the devices with limited battery capacity cannot support the executing and transmitting of computational data streams under a given latency. Thus, WPT technology is adopted for this considered network to enable the devices to harvest energy from the power beacon. In further, by considering the device’s energy consumption and latency constraints, we propose an optimization problem under energy constraints. To solve this problem, we design a customized particle swarm optimization-based algorithm, which aims at minimizing the latency of the device processing computational data stream by jointly optimizing the charging and offloading strategies. Furthermore, simulation results illustrate that the proposed method outperforms other benchmark schemes in minimizing latency, which shows the proposed method’s superiority in processing the multi-source data stream.