Task Processing Delay Research Articles

Multi-Classification and Distributed Reinforcement Learning-Based Inspection Swarm Offloading Strategy

In meteorological and electric power Internet of Things scenarios, in order to extend the service life of relevant facilities and reduce the cost of emergency repair, the intelligent inspection swarm is introduced to cooperate with monitoring tasks, which collect and process the current scene data through a variety of sensors and cameras, and complete tasks such as emergency handling and fault inspection. Due to the limitation of computing resources and battery life of patrol inspection equipment, it will cause problems such as slow response in emergency and long time for fault location. Mobile Edge Computing is a promising technology, which can improve the quality of service of the swarm by offloading the computing task of the inspection equipment to the edge server nearby the network. In this paper, we study the problem of computing offloading of multi-devices multi-tasks and multi-servers in the intelligent patrol inspection swarm under the condition of a dynamic network environment and limited resources of servers and inspection equipment. An effective adaptive learning offloading strategy based on distributed reinforcement learning and multi-classification is proposed to reduce the task processing delay and energy consumption of the intelligent inspection swarm and improve the service quality. Numerical experimental results demonstrate that the proposed strategy is superior to other offloading strategies in terms of time delay, energy consumption and quality of service.

Energy-Efficiency Computation Offloading Strategy in UAV Aided V2X Network With Integrated Sensing and Communication

Integrated Sensing and Communications (ISAC) technology can jointly design radio sensing and communication functionalities, which enable 6G Ere to have the ability to “see” the physical world rather than communication-only. Benefitting from ISAC, vehicular-to-everything (V2X) networks may efficiently complete high-precision traffic environment perception. Furthermore, with the assistance of flexibly deployed Unmanned Aerial Vehicles (UAVs), the V2X networks overcome the limited sensing range of sensors equipped on a vehicle and guarantee safe driving. This paper proposes an energy-efficient computation offloading strategy for multiple sensor data fusion in UAV Aided V2X Network supported by Integrated Sensing and Communication. Firstly, a vehicle-UAV cooperative perception architecture is proposed to perceive a wide range of traffic environments. Secondly, we introduce a computation offloading strategy jointly considering offloading decisions and dynamic computing resource allocation. Finally, a successive convex approximation (SCA) algorithm transforms a non-convex formulation problem into a tractable convex approximation problem. The simulation results show that the strategy proposed in this paper reduces the UAV energy consumption and data fusion task processing delay.

Deep reinforcement learning-based computation offloading for 5G vehicle-aware multi-access edge computing network

Multi-access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional Internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based vehicle-aware Multi-access Edge Computing network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.

Research on High Confidence Resource Allocation Technology in Edge Computing

This paper studies resource allocation and security performance. Aiming at the problem of privacy disclosure caused by eavesdropping during task unloading in single cell and multi user scenarios, the physical layer security technology is used to formulate corresponding confidentiality measures. Facing the dynamic change of the wireless channel state of the system and the computing power required by users, taking reducing the average processing delay of tasks as the optimization goal, the multi-user task partial migration problem is modeled as a joint optimization problem of computing resources and power resources under the constraints of security set-tings and energy constraints, and the system is established as a Markov decision process model, and proposes a resource allocation algorithm based on physical layer security and depth deterministic policy gradient. Simulation results show that the algorithm can approach the optimal performance, has adaptability and lower computational complexity, can make better unloading strategy and resource allocation scheme in trusted environment, effectively reduce the average task processing delay and improve the robustness of the system.

STMTO: A smart and trust multi-UAV task offloading system

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory. Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks.

KFTO: Kuhn-Munkres based fair task offloading in fog networks

In the fog network with multiple terminals and fog nodes, how to make a tradeoff between energy consumption fairness among fog nodes (FNs) and task processing delay of terminal nodes (TNs) is still a challenging problem. To solve this problem, this paper proposes a Kuhn-Munkres (KM) based Fair Task Offloading (KFTO) scheme, which includes two optimization models: FN selection model and task offloading decision model. FN selection model employs the KM algorithm to obtain the optimal matching scheme between TNs and FNs that maximizes the total network potential. The second model is used to obtain the task size offloaded to FN that minimizes the task processing delay of TN while satisfying the energy consumption constraint. Numerical simulation results reveal that the proposed KFTO scheme achieves a satisfactory tradeoff between the energy fairness among FNs and the task processing delay of TNs.

Computation offloading and service allocation in mobile edge computing

The intensive mobile data traffic poses a great challenge for energy-constrained mobile devices. In the mobile edge environment, effective computing offloading and resource allocation can improve the service performance of edge computing systems. Therefore, a dynamic computation offloading model based on genetic algorithm is proposed in this paper. In this strategy, a task weight cost model based on processing delay and energy consumption is built, which can optimize processing delay and energy consumption simultaneously. Moreover, in view of the limited computing resources of edge servers, a resource allocation model based on utility maximization is proposed. In this strategy, the bidding strategies of users and edge nodes are studied and the resource is allocated to the high-unit bidding users based on the greedy strategy during the double auction process. A large number of experimental results show that the proposed computation offloading algorithm can significantly reduce task processing delay and energy consumption. For instance, the proposed offloading algorithm can save energy up to 14.81% and reduce processing delay up to 7.71% compared with the COPSO algorithm. Besides, the proposed resource allocation algorithm can promote the number of successful auction users and maximize the utility of the users and the edge nodes.

Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.

Online and energy-efficient task-processing for distributed edge networks

User equipment produces a series of tasks that are processed locally or remotely, falling into three categories: (i) local computing only, (ii) a fraction of the task is computed locally and the remaining task unprocessed is offloaded for remote computation, and (iii) the entire task is offloaded. Each case has attracted substantial attention in recent studies, where a delay-constrained non-linear optimization problem is often formulated. The solutions employed are either based on Lagrange duality, heuristic search, or dynamic programming. To our knowledge, there is no unifying task-processing orchestrator that is an online tailored solver for learning the model-free problems, encapsulating the three cases above. We fill this gap and present the first attempt on an innovative actor-critic reinforcement learning approach in consideration of the energy-efficiency, to compute the asymptotically optimal solutions via decomposing the comprehensive optimization into sub-problems. Rigorous theoretical analyses and experience-driven simulations demonstrate significant advantages over the benchmark approaches, in terms of task-processing delay, power efficiency, and convergence time.

Computing tasks assignment optimization among edge computing servers via SDN

As an extension of cloud computing, the edge computing has become an important pattern to deal with novel service scenarios of Internet of Everything (IoE) under 5G, especially for the delay sensitive computing tasks generated from edge equipment. The edge computing provides the key support to meet the characteristics of delay sensitivity by deploying servers near network edges. However, a great many uneven distributed computing tasks in different network edges usually lead to task processing delay bottleneck for single Edge Computing Server (ECS). Tasks assignment is mainly based on the local ECS status without the global network view considered, which also easily leads to unbalanced task loads among multiple ECSs. In this paper, the novel networking idea of Software Defined Network (SDN) is introduced into the edge computing pattern. The logically highly centralized control plane consists of multiple physically distributed ECSs, so as to collaboratively assign computing tasks in a global view. In order to optimize the task assignment and minimize the task processing delay, three schemes are proposed in this paper. The scheme of assessing the ECS’s task computing features is firstly proposed, then the scheme of predicting the ECS’s future unit task processing time is presented. Thus, different types of computing tasks can be assigned to appropriate ECSs that are better at dealing with them with processing delay minimized. Furthermore, the scheme of optimizing the delay of task processing time estimation is devised, so as to further improve task assignment efficiency. Experimental results show that the proposed mechanism is able to optimize the task assignment and minimize the task processing delay more efficiently than the state of the art. Specifically, our mechanism is capable of improving the average unit task processing delay and the ECS load balancing degree by about 14% and 23% respectively, compared with corresponding work.

Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in SAGIN

In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions, in which the tasks can be processed at the UAV or offloaded to the nearby base station or the remote satellite. Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint. To this end, we first formulate the online scheduling problem as an energy-constrained Markov decision process (MDP). Then, considering the task arrival dynamics, we develop a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the algorithm evaluates the risk, which measures the energy consumption that exceeds the constraint, for each state and searches the optimal parameter weighing the minimization of delay and risk while learning the optimal policy. Extensive simulation results demonstrate that the proposed algorithm can reduce the task processing delay by up to 30% compared to probabilistic configuration methods while satisfying the UAV energy capacity constraint.

Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users’ daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this article, we consider the vehicular computation offloading problem in mobile-edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multiagent deep reinforcement learning (DRL)-based computation offloading scheme, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. First, we formalize a formula for the computation offloading problem. The goal of this article is to determine the optimal offloading decision to the MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multiagent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.

Optimal auction for delay and energy constrained task offloading in mobile edge computing

Mobile edge computing has emerged as a promising paradigm to complement the computing and energy resources of mobile devices. In this computing paradigm, mobile devices offload their computing tasks to nearby edge servers, which can potentially reduce their energy consumption and task completion delay. In exchange for processing the computing tasks, edge servers expect to receive a payment that covers their operating costs and allows them to make a profit. Unfortunately, existing works either ignore the payments to the edge servers, or ignore the task processing delay and energy consumption of the mobile devices. To bridge this gap, we propose an auction to allocate edge servers to mobile devices that is executed by a pair of deep neural networks. Our proposed auction maximizes the profit of the edge servers, and satisfies the task processing delay and energy consumption constraints of the mobile devices. The proposed deep neural networks also guarantee that the mobile devices are unable to unfairly affect the results of the auctions. Our extensive simulations show that our proposed auction mechanism increases the profit of the edge servers by at least 50% compared to randomized auctions, and satisfies the task processing delay and energy consumption constraints of mobile devices.

Dynamic Computation Offloading in IoT Fog Systems With Imperfect Channel-State Information: A POMDP Approach

Driven by the growing popularity of mobile applications, such as the Internet of Things (IoT), fog computing has been envisioned as a promising approach to enhance the computation capability of mobile devices and reduce the energy consumption. In this article, we aim to investigate the dynamic computation offloading problem in the IoT fog system under the fast time-varying wireless channel conditions. Our work differs from the existing work, which is based on the assumption that the channel-state information can be perfectly obtained by the offloading agent (e.g., the IoT device). In reality, due to hardware limitation, short sensing time, and network connectivity issues in IoT fog systems, it is difficult for the IoT device to have the perfect knowledge of a dynamic channel environment. Therefore, in this article, we propose a partially observable offloading scheme to enable the IoT device to make the optimal offloading decision with imperfect channel-state information. The optimization problem is formulated as a partially observable Markov decision process (POMDP) formulation, with the objective of minimizing the IoT device's energy consumption while meeting its requirement on task processing delay. To find the optimal offloading solution, an offline algorithm based on the deep recurrent $Q$ -network (DRQN) is developed. Finally, extensive simulation experiments are performed to evaluate the effectiveness of the proposed offloading scheme.

Computation Offloading With Instantaneous Load Billing for Mobile Edge Computing

Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this article, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

Research on the optimization of IIoT data processing latency

As for data processing, data should be sent to cloud, stored and computed. Usually, the amount of data is huge enough for higher latency of processing. In addition, the mobility of cloud service would be much slower. In this paper, an Industrial Internet of Things cloud–fog hybrid network (ITCFN) framework is proposed to solve high latency upon processing industrial data on cloud. In the production equipment area, edge devices such as routers and switches are utilized by framework to construct a fog computing layer between cloud server and production equipment. Since computing power of the edge devices in fog computing is much poor, a distributed computing method for multi-devices is proposed. The aim of minimum task processing delay is achieved by using the constrained particle swarm optimization load balancing algorithm based on simulated annealing method (SAPSO-LB). The experimental results show that the ITCFN based on SAPSO-LB algorithm can effectively reduce the industrial data processing delay. When ten fog computing devices are used and the collected data is between 4GB and 12GB, compared with cloud computing, the latency is improved by 84.1%-29.9%.

Cloud Network and Mathematical Model Calculation Scheme for Dynamic Big Data

Cloud network oriented to dynamics big data is a popular way of big data operation at present. It is a big data that can be applied to human behavior under dynamics. Time-based features include communication, network access and migration activities combined with unique physiological characteristics to complete big data. This paper aims to study a new cloud hybrid network architecture based on edge computing. This article uses this architecture to tectonic fog calculation layer between cloud servers and devices using edge devices such as communications and networks. After analysis, the obtained results are actively cached on the fog computing device and compared with the data of the telecommunication network detection terminal on the fog device. To consider the final result. The experimental data show that the distributed computing scheme can achieve the goal of minimizing the task processing delay. The experimental results show that the cloud network of the dynamic big data can meet the requirements of the contemporary big data completely, and the utilization ratio of the big data can reach 90%, and the calculation scheme of the mathematical model can run completely.

DOTS: Delay-Optimal Task Scheduling Among Voluntary Nodes in Fog Networks

Through offloading the computing tasks of the task nodes (TNs) to the fog nodes (FNs) located at the network edge, the fog network is expected to address the unacceptable processing delay and heavy link burden existed in current cloud-based networks. Unlike most existing researches based on the command-mode offloading and full capability report, this paper develops a general analytical model of the task scheduling among voluntary nodes (VNs) in fog networks, wherein the VNs voluntarily contribute their capabilities for serving their neighboring TNs. A novel delay-optimal task scheduling (DOTS) algorithm is proposed to obtain the delay-optimal offloading solution according to the reported capabilities of the VNs. Extensive simulations are carried out in a fog network, and the numerical results indicate that the proposed DOTS algorithm can effectively provide the optimal set of the helper nodes, subtask sizes, and the TN transmission power to minimize the overall task processing delay. Moreover, compared with the command-mode offloading, the voluntary-mode achieves more balanced offloading and a higher fairness level among the FNs.

Centralized and Distributed Architectures for Energy and Delay Efficient Fog Network-Based Edge Computing Services

Edge computing techniques allow to exploit the devices at the network borders for computing efforts in order to reduce centralized cloud requests. A fog network is a feasible solution for implementing edge computing services. Within this scenario, the deployed fog nodes (FNs) are able to offload different portions of a single task to the available nodes to be processed at the network edge. However, to partially offload, FNs consume an extra amount of energy for transmission and reception of the tasks while saving energy by not processing the whole task on their own. Moreover, offloading requires an extra transmission and reception time to the task processing time. In this paper, the focus is on a partial offloading approach where the tradeoff between FN energy consumption and task processing delay is considered when estimating the portion to be offloaded to the available devices at the edge of the network by comparing a centralized and a distributed architecture. Simulation results demonstrate the effectiveness of the proposed estimation solutions in terms of FN energy consumption, average task delay, and network lifetime.