Optimal Task Allocation Research Articles

Task Scheduling Strategy for Industrial Control Network based on Cloud-Edge-End Fusion Framework

Industrial tasks with traditional cloud architecture have many disadvantages, which are mainly longer delay of data transmission and difficulty in software deployment. In view of the above disadvantages, this paper used containers to solve the problems for industrial software deployment and upgrade. This paper uses a cloud-edge-end framework to deploy tasks with high delay on the edge of the network or even on the device side, so as to reduce the delay of data transmission and computing pressure. At the same time, the task scheduling strategy based on the particle swarm optimization algorithm is used to search for the node with the minimum task delay among multiple nodes to realize the task allocation optimization. Finally, through the comparison experiment, it is concluded that the task scheduling strategy for industrial control networks based on a cloud-edge-end fusion framework can better reduce the task execution delay and reduce the difficulty of software deployment.

Reinforcement learning empowered multi-AGV offloading scheduling in edge-cloud IIoT

The edge-cloud computing architecture has been introduced to industrial circles to ensure the time constraints for industrial computing tasks. Besides the central cloud, various numbers of edge servers (ESes) are deployed in a distributed manner. In the meantime, most large factories currently use auto guided vehicles (AGVs). They usually travel along a given route and can help offload tasks to ESes. An ES maybe accessed by multiple AGVs, thus incurring offloading and processing delays due to resource competition. In this paper, we investigate the offloading scheduling issue for cyclical tasks and put forth the Multi-AGV Cyclical Offloading Optimization (MCOO) algorithm to reduce conflicts. The solution divides the offloading optimization problem into two parts. Firstly, the load balancing algorithm and greedy algorithm are utilized to find the optimal allocation of tasks for a single AGV under limited conditions. Then, multiple AGVs are asynchronously trained by applying the Reinforcement Learning-based A3C algorithm to optimize the offloading scheme. The simulation results show that the MCOO algorithm improves the global offloading performance both in task volume and adaptability compared with the baseline algorithms.

Multi-task dispatch of shared autonomous electric vehicles for Mobility-on-Demand services – combination of deep reinforcement learning and combinatorial optimization method

The Autonomous Mobility-on-Demand system is an emerging green and sustainable transportation system providing on-demand mobility services for urban residents. To achieve the best recharging, delivering, and repositioning task assignment decision-making process for shared autonomous electric vehicles, this paper formulates the fleet dynamic operating process into a multi-agent multi-task dynamic dispatching problem based on Markov Decision Process. Specifically, the decision-making process at each time step is divided into 3 sub-processes, among which recharging and delivery task assignment processes are transformed into a maximum weight matching problem of bipartite graph respectively, and the repositioning task assignment process is quantified as a maximum flow problem. Kuhn-Munkres Algorithm and Edmond-Karp Algorithm are adopted to solve the above two mathematical problems to achieve the optimal task allocation policy. To further improve the dispatching performance, a new instant reward function balancing order income with trip satisfaction is designed, and a state-value function estimated by Back Propagation-Deep Neural Network is defined as a matching degree between each shared autonomous electric vehicle and each delivery task. The numerical results show that: (i) a reward function focusing on income and satisfaction can increase total revenue by 33.2%, (ii) the introduction of task allocation repositioning increases total revenue by 50.0%, (iii) a re-estimated state value function leads to a 2.8% increase in total revenue, (iv) the combination of charging and task repositioning can reduce user waiting time and significantly improve user satisfaction with the trip.

Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System.

Unmanned aerial vehicles (UAVs) are widely used in wireless communication systems due to their flexible mobility and high maneuverability. The combination of UAVs and mobile edge computing (MEC) is regarded as a promising technology to provide high-quality computing services for latency-sensitive applications. In this paper, a novel UAV-assisted MEC uplink maritime communication system is proposed, where an MEC server is equipped on UAV to provide flexible assistance to maritime user. In particular, the task of user can be divided into two parts: one portion is offloaded to UAV and the remaining portion is offloaded to onshore base station for computing. We formulate an optimization problem to minimize the total system latency by designing the optimal flying altitude of UAV and the optimal task allocation ratio. We derive a semi closed-form expression of the optimal flying altitude of UAV and a closed-form expression of the optimal task allocation ratio. Simulation results demonstrate the precision of the theoretical analyses and show some interesting insights.

Optimization of Intelligent Network Information Management System under Big Data and Cloud Computing

With the economy and society development, big data storage in management has become a problem which can’t be ignored. Task allocation management and optimization is a significant factor in the enterprise sustainable development. The enterprise information intelligent management has become management mode and presents information to the business managers in a more effective and lower cost. In order to solve the management problem under big data and cloud computing, a research on the optimization of intelligent network information management system is proposed. Firstly, build the overall model of big data diversion system under cloud computing platform, and design the architecture of Tiny OS operating system for big data diversion; Secondly, S3C2440 is used as the system control core to design the hardware structure of the shunting system; Finally, the software of the system is developed by using the dynamic loading method of functional subroutines in Linux operating system. The power supply voltage is DC 3.3 V and 1.25 v. DC 5 V is used as the overall power supply of the circuit board, and 0.1 is added at both ends of lm1117 chip F and 100 F capacitance for FIR filtering of power supply. Simulation experiments are carried out to verify the performance, which shows the superior performance of the big data shunting system designed in this paper.

Distributed Competition of Multi-Robot Coordination Under Variable and Switching Topologies

This paper investigates a distributed competition behavior in multi-robot coordination under variable communication topology and switching one. In terms of multi-robot competition-based coordination, a winner-take-all (WTA) strategy is leveraged to address this issue with inevitable environmental barriers incorporated. Moreover, an innovative control theory stimulated gradient neural network (CTSGNN) algorithm is proposed to realize the WTA with prominent robustness and convergence over the traditional ones. Besides, to adapt to diversified local communication modes among multi-robot systems, fast variable and low switching topologies are constructed to establish two dynamic consensus estimators, accompanied by the proposed distributed control schemes. Traditional algorithms are introduced and served as a contrast. Afterward, the global convergence of the proposed algorithm in dealing with multi-robot competitive coordination, the universality of the application scenario, as well as the weaknesses of traditional methods are substantiated theoretically. The effectiveness and superiority of the proposed CTSGNN algorithm and the resultant distributed control schemes by integrating consensus estimators are further sustained via simulations. Note to Practitioners—The motivation of this paper is the coordination operation of multi-robot systems, but it is also applicable to other fields adopting multi-agent systems. Most of the existing researches on multi-robot coordination only exploit their collaborative behavior, which usually leads to the system redundancy and overflow of control costs. To this end, an innovative control algorithm is proposed for this competitive coordination and remains to be perfect in terms of stability and accuracy. Then, this paper establishes new distributed control schemes for multi-robot systems to compete for optimal dynamic task allocation. In this sense, under the premise of ensuring a successful task execution, only a few individuals with strong abilities and advantages are assigned. It is promising to maximize resource utilization, increase efficiency, and be extended to multi-objective scenarios. Note that the design of this scheme takes into account the environmental constraints and physical constraints of the robot itself. Theoretical analysis and preliminary simulation experiments prove the high efficiency of the control scheme. In ongoing research, the competitive coordination tasks of the mobile robot systems and communication delays or fault in control scheme design are explored to expand the operation scope and system extensibility.

Task Allocation and Traffic Route Optimization in Hybrid Fire-fighting Unmanned Aerial Vehicle Network

With the increase of extreme weather conditions in the world, the probability of forest fires is increasing. How the forest fire management decision-making system can monitor and control the fire quickly and effectively is the key of forest fire fighting work. This paper uses SSA drones carrying high-definition and thermal imaging cameras and telemetry sensors in conjunction, as well as Repeater drones used to greatly expand the frontline low-power radio range, to support fire management decision-making systems. At the same time, explore a drone cooperation plan to deal with different fire terrains and different scales of fire conditions. The aim of this paper is to improve the existing fire management decision system in order to quickly respond to the emergency fire. Research object for the Australian state of Victoria on October 1, 2019 to January 7, 2020 wildfires, explore SSA drones and Repeater drones in the application of the forest fire, ensure that fire management decision-making system to provide the optimal number deployment scheme of fire task quickly and efficiently, and achieve the maximum efficiency and economic optimal compatibility.

FlexEdge: Dynamic Task Scheduling for a UAV-Based On-Demand Mobile Edge Server

With the large number of cameras deployed in smart industrial parks and smart campuses, edge devices and location-fixed edge servers are deployed near to these cameras and help transmit video streams to data center for video analytics; however, location-fixed edge servers are difficult to adapt to computation-intensive and delay-sensitive video analytics tasks in hot spot, and it is also challenging to execute tasks in natural disasters in which the infrastructure is damaged. Moreover, task migration methods are used to balance the load of edge servers caused by irregular movement of detected objects, but it results in extra data transmission overhead. Therefore, unmanned aerial vehicles (UAVs) with computing and communication resources are widely used to optimize mobile edge video analysis; however, existing solutions formulate the UAV-based lowest latency and energy consumption by jointly optimizing the task allocation strategy and UAV location to be a multiobjective optimization problem, based on which the Pareto optimum solution set, including task allocation strategies and UAV locations, can find multiple solutions but not a unique solution. It makes the solution difficult to be applied in video analytics with the UAV hover location decision-making scheme and task allocation strategy. In this article, we propose a flexible cloud-edge collaborative scheduling strategy based on a UAV named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FlexEdge</i> . We first normalize values of execution time and energy consumption, and then convert the multiobjective optimization problem into a single-objective optimization problem by using the weighted sum of the two metrics as the optimization objective. We also proved the task allocation strategy based on execution time, energy consumption, and the UAV hover location decision-making scheme as an NP-hard problem. We propose a flexible and lightweight genetic algorithm (FGA) based on a polysomy-strengthening elitist genetic algorithm in FlexEdge to address the NP-hard problem. FlexEdge not only achieves optimal task allocation and UAV location to minimize the weighted sum of execution time and energy consumption but also provides computing resources and reliable network connection to reduce task offloading overload, which is validated by comprehensive performance evaluation.

Multi objective task scheduling based on hybrid metaheuristic algorithm for cloud environment

Cloud computing is gaining a huge popularity for on-demand services on a pay-per-use basis. However, single data centre is restricted in offering the services, as it does not have unlimited resource capacity mostly in the peak demand time. Generally, the count of Virtual Machines (VM) is more in public cloud; still, the security is not ensured. In contrast, the VMs are limited in private cloud with high security. So, the consideration of security levels in task scheduling is remains to be more critical for secured processing. This works intends to afford the optimization strategies for optimal task scheduling with multi-objective constraints in cloud environment. Accordingly, the proposed optimal task allocation framework considers the objectives such as execution time, risk probability, and task priority. For this, a new hybrid optimization algorithm known as Clan Updated Seagull Optimization (CUSO) algorithm is introduced in this work, which is the conceptual blending of Elephant Herding Optimization (EHO) and Seagull Optimization Algorithm (SOA). Finally, the performance of proposed work is evaluated over other conventional models with respect to certain performance measures.

DDDQN‐TS: A task scheduling and load balancing method based on optimized deep reinforcement learning in heterogeneous computing environment

Task scheduling and load balancing problem of heterogeneous computing environment (HCE) is getting more and more attention these days and has become a research hotspot in this field. The task scheduling and load balancing problem of heterogeneous environment, which refers to assigning a set of tasks to a specific set of machines with different hardware and different computing performance with the goal of minimizing task processing time and keeping load balance among machines, has been proved to be an NP-complete problem. The development of artificial intelligence provides new ideas to solve this problem. In this paper, we propose a novel task scheduling and load balancing method based on optimized deep reinforcement learning in HCE. First, we formulate task scheduling problem as a Markov decision process and then adopt a dueling double deep Q-learning network to search the optimal task allocation solution. Then we use two well-known large-scale cluster data sets Google Cloud Jobs data set and Alibaba Cluster Trace data set to validate our approach. The experimental results show that compared with other existing solutions, our proposed method can achieve much shorter task response time and better load balancing effect.

Optimized Task Allocation for IoT Application in Mobile-Edge Computing

With the rapid upgrading and explosive growth of Internet of Things (IoT) devices in mobile-edge computing, more and more IoT applications with high resource requirements are developed and utilized. Meanwhile, there are large quantities of edge nodes (e.g., switches and edge servers) with limited resources, higher operating costs, and certain failure probabilities in the mobile-edge computing environment. Therefore, when an IoT application is split into multiple collaborative tasks and offloaded into multiple edge clouds, there is an urgent need to increase the availability level of the task allocation scheme and the resource utilization of edge servers under the condition of certain communication delay. In this article, we first present a joint optimization objective to evaluate the unavailability level, communication delay, and resource wastage while allocating the same batch of IoT applications to multiple edge clouds. We then propose an approach to minimize the joint optimization objective under the condition of certain communication delay. Finally, we performed a comprehensive simulation experiment analysis to demonstrate that our proposed approach is superior to other related approaches.

Cooperative Hybrid Nonorthogonal Multiple Access-Based Mobile-Edge Computing in Cognitive Radio Networks

In order to efficiently compute the primary data and support the secondary quality-of-service (QoS) requirement, we propose a cooperative hybrid non-orthogonal multiple access (NOMA) scheme for mobile edge computing (MEC) assisted cognitive radio networks. In the proposed scheme, the primary computation task is securely offloaded to the secondary base station, and the hybrid NOMA technique is adopted to provide secondary spectrum access and secure the primary offloading simultaneously. The weighted energy consumption minimization problem for both the primary and secondary systems is first studied under the constraints of the primary system’s secure outage probability and the secondary system’s QoS requirements, and a two-stage algorithm is proposed to derive the optimal power, time slot and computation task allocation. To motivate the secondary system’s cooperation, we optimally allocate the transmit power, time slot and computation task, such that the average secondary system’s rate is maximized under the primary system’s security requirement, and we derive closed-form expressions for the optimal resource allocations. Numerical results demonstrate the performance superiority of the proposed scheme compared with the full-offloading scheme in terms of the energy consumption and the average secondary rate.

Hierarchical Coded Matrix Multiplication in Heterogeneous Multihop Networks

The performance of distributed computing is restricted by the slowest worker nodes, known as stragglers, in the system. Coded computation has emerged as an efficient technique to mitigate the straggler effects in distributed computing. Most existing works only considered the computation straggler for single-hop networks. However, in multi-hop networks, the straggler effects will occur not only on worker nodes but also on relay nodes. In this paper, we consider a heterogeneous multi-hop network. The nodes in the network are heterogeneous, i.e., their computation capacities and transmission capacities are different. We propose a hierarchical coding scheme for such a network. Firstly, we reorganize it into a hierarchical network containing multiple layers. Each layer in the network consists of several groups. Then, a new hierarchical coding scheme is proposed, where coding is applied to each group to mitigate the stragglers. By taking both the computation time and transmission time into consideration, the overall task completion time is derived. To improve the performance of the network, heterogeneous hierarchical coded computation (HHCC) algorithm is proposed to provide an asymptotically optimal task allocation strategy. Compared with existing uniform uncoded, load balanced uncoded, and heterogeneous coded matrix multiplication schemes, HHCC has significant improvement.

Multi-objective secure task scheduling based on SLA in multi-cloud environment

The appliances that are received at a cloud data centre are a compilation of jobs (task) that might be independent or dependent on one another. These tasks are then allocated to diverse virtual machine (VM) in a scheduled way. For this task allocation, various scheduling policies are deployed with the intention of reducing energy utilization and makespan, and increasing cloud resource exploitation as well. A variety of research and studies were done to attain an optimal solution in a single cloud setting, however the similar schemes might not operate on multi-cloud environments. Here, this paper aims to introduce a secured task scheduling model in multi-cloud environment. The developed approach mainly concerns on optimal allocation of tasks via a hybrid optimization theory. Consequently, the developed optimal task allotment considers the objectives like makespan, execution time, security parameters (risk evaluation), utilization cost, maximal service level agreement (SLA) adherence and power usage effectiveness (PUE). For resolving this issue, a novel hybrid algorithm termed as rock hyraxes updated shark smell with logistic mapping (RHU-SLM) is introduced in this work. At last, the superiority of developed approach is proved on varied measures.

Quantum invasive weed optimization-based energy aware task scheduling for cyber–physical system environment

Cyber–physical systems (CPSs) can be treated as an emerging technology that has the ability to handle the physical process and computational view of interlinked systems. At the same time, the high-performing processing capability provides assurance of CPS applications in real time. Besides, task scheduling is considered as the Nondeterministic Polynomial (NP)-hard problem and optimal allocation of tasks is important for the CPS environment. The primary concept of the optimum energy-based scheduling approach searches for the physical host allocation vector to the allotted virtual machine with an aim of reducing energy utilization. The multiple processor packet scheduling technique defined that every task in the system is already divided into processors by the task allocating scheme and every process can execute on the distinct or identical single processor scheduling technique. With this motivation, this paper presents a new quantum invasive weed optimization-based energy-aware scheduling (QIWO-EATS) technique for the CPS environment. The goal of the QIWO-EATS technique is to assign [Formula: see text] autonomous tasks to [Formula: see text] dissimilar resources, and thereby the whole task completion duration gets reduced and resources are completely used. The proposed model has been simulated using the MATLAB tool. The experimental results highlighted the better outcomes of the QIWO-EATS technique over the recent approaches in terms of several evaluation metrics.

POTAM: A Parallel Optimal Task Allocation Mechanism for Large-Scale Delay Sensitive Mobile Edge Computing

Design an optimization model for task management among Mobile Terminal (MT), Macro cell Base Station (MBS), and multiple Small cell Base Stations (SBS) for the large-scale Mobile Edge Computing (MEC) system, is a challenging issue due to the large number of tasks and SBSs. Inspired by this, we propose a Parallel Optimal Task Allocation Mechanism (POTAM) framework for MEC, which includes Device to Device (D2D)-enabled computing, MBS computing and Edge Computation Resource Distribution (ECRD) computing. In POTAM, we exploit a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of delay and energy consumptions, which formulates the task allocation under these requirements as a nonlinear 0–1 integer programming problem. To solve this problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing and Cyclic Block coordinate Gradient Projection (CBGP) methods, which can guarantee convergence and reduce computational complexity with a good scalability. In order to allocate task cooperatively, an optimal approach is proposed, ECRD-A, which is used to find the shortest path among each node. Numerical results demonstrate the effectiveness of the POTAM and it can effectively reduce delay and energy consumption for a large-scale MEC system.

Optimizing Task Location Privacy in Mobile Crowdsensing Systems

The location information for tasks may expose sensitive information, which impedes the practical use of mobile crowdsensing in the industrial Internet. In this article, to our knowledge, we are the first to discuss the privacy protection of task locations and propose a codebook-based task allocation mechanism to protect it. Considering the cost of system utility caused by privacy protection technology, the tradeoff between local privacy and system utility is formalized a multiobjective optimization problem. The optimal solution is theoretically derived, and the optimal task allocation scheme is obtained. In addition, the selected allocation codebook (SAC) method is introduced to solve the problem of high computational resource consumption in the task allocation process and protect the task location privacy to some extent. The experimental results show that the SAC method sacrifices system utility but improves the privacy protection for task locations by 60% on average.

Joint flight scheduling and task allocation for secure data collection in UAV-aided IoTs

With the flexible mobility and deployment, Unmanned Aerial Vehicles (UAVs) have created a new dimension to provide data collection services for a large number of distributed wireless sensory devices. Unfortunately, the broadcast nature of wireless channels make it hard to guarantee the security of data transmission in Internet of Things (IoTs). In this paper, focusing on the scenario where potential eavesdroppers intend to intercept the data uploading of legitimate nodes, we take the advantages of the UAV technology and the physical-layer security approach to study the Secure Data Collection Problem (SDCP). We first explore the flight scheduling and task allocation, that is, determining which areas to be visited and who to be served by UAVs, to establish favorable communication channels. Then we set up the utility function with secrecy rates and non-negative budget constraints. Due to the coupling between the UAV scheduling and task allocation optimization variables in the SDCP, we construct a surrogate function to achieve decoupling and reformulate SDCP into the SDCP-m problem, which is analyzed and proved to be a nonnegative monotone submodular function subject to a general constraint. Finally, an efficient Cost–Benefit Greedy (CBG) algorithm, which is theoretically analyzed to seek the quantitative value of approximation ratio, is proposed. The simulation results show that the CBG algorithm not only outperforms benchmark algorithms in terms of the average secrecy rate and the iteration number, but also generates a nearly optimal trajectory.

Modeling adaptive security-aware task allocation in mobile cloud computing

Security is one of the most important criteria in the management of cloud resources. In Mobile Cloud Computing (MCC), secure allocation of tasks remains challenging due to the limited storage, battery life and computational power of mobile devices connected to the core cloud cluster infrastructure. Secure wireless communication channels and protocols for protecting the data and information sent to the cloud, and remote access to secure cloud services, are other important problems related to task scheduling and processing in dynamic MCC. In this paper, we developed a new security-aware task allocation model strategy in Mobile Cloud Computing. In this model, we define an allocation algorithm which generates an optimal and secure configuration of communication protocols in order to meet the specific data confidentiality requirements defined by end users. Resource utilization is predicted using Machine Learning methods, and the optimal secure service for task execution is selected. We developed a simulation environment (MocSecSim) for the evaluation of the algorithms proposed in several scenarios based on the users’ requirements. The results of simulations and experiments have demonstrated that the model proposed significantly improves the level of security of calculations in comparison with a configuration where processing time and energy consumption are the main criteria for optimizing task allocation.

An efficient multi-objective ant colony optimization for task allocation of heterogeneous unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become powerful tools in modern military combat. How to properly allocate the tasks of heterogeneous UAVs in a combat is a fundamental and challenging problem. In this paper, we formulate the cooperative task allocation of heterogeneous UAVs as a constrained multi-objective optimization problem. To efficiently resolve the formulated problem, we further propose a multi-objective ant colony optimization (MOACO) algorithm with a new pheromone updating mechanism and four newly defined heuristic information. Simulation results on test cases with different scales and characteristics have shown that the proposed methods can perform better than several recently published algorithms, in terms of convergence speed, solution quality and solution diversity.