Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Satellite-Air-Terrestrial Cloud Edge Collaborative Networks:Architecture,Multi-Node Task Processing and Computation

Integrated satellite-terrestrial network(ISTN)has been considered a novel network architecture to achieve global three-dimensional coverage and ultra-wide area broadband access anytime and anywhere.Being a promising paradigm,cloud computing and mobile edge computing(MEC)have been identified as key technology enablers for ISTN to further improve quality of service and business continuity.However,most of the existing ISTN studies based on cloud computing and MEC regard satellite networks as relay networks,ignoring the feasibility of directly deploying cloud computing nodes and edge computing nodes on satellites.In addition,most computing tasks are transferred to cloud servers or offloaded to nearby edge servers,the layered design of integrated satellite-air-terrestrial architecture and the cloud-edge-device cooperative processing problems have not been fully considered.Therefore,different from previous works,this paper proposed a novel satellite-air-terrestrial layered architecture for cloud-edge-device collaboration,named SATCECN.Then this paper analyzes the appropriate deployment locations of cloud servers and edge servers in ISTN,and describes the processing flow of typical satellite computing tasks.For computing resource allocation problems,this paper proposed a device-edge-cloud Multi-node Cross-layer Collaboration Computing(MCCC)method to find the optimal task allo-cation strategy that minimizes the task completion delay and the weighted system energy consumption.Furthermore,the approximate optimal solutions of the optimization model are obtained by using successive convex approxi-mation algorithm,and the outstanding advantages of the proposed method in reducing system energy consumption and task execution delay are verified through experiments.Finally,some potential issues and directions for future research are highlighted.

Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells

This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.

On the Task-based Collaborative Learning

Task-based language teaching(TBLT) has been a prevalent teaching practice in the TEFL field in the recent years and its momentum for striving to be the legitimate one has never ceased. The present study tries to provide a theoretical foundation for its application in the communicative learning approach of English as the second language(ESL),namely the collaborative learning mode.

A Two-stage Service-oriented Task Offloading Framework with Edge-cloud Collaboration:A Game Theory Approach

With the fast development of Mobile Internet,data traffic generated by end devices is anticipated to witness substantial growth in the future years.However,processing tasks locally will cause latency due to the limited resources of the end devices.Edge-cloud collaboration,an effective solution for latency-sensitive applications,is attracting greater attention from both industry and academia.It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities.In this paper,we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center.As for homogeneous task offloading,in the first stage,the competitive end devices offload tasks to the edge gateways.We formulate the selfish task offloading problem among end devices as a potential game.In the second stage,the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources.Then,we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy.Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices.We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence.

Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and lowlatency communications(URLLC)requirements.A user vehicle(UV)dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers(VFSs).However,the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing,incomplete information,and dimensionality curse.In this paper,we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs.Then,a Deep Reinforcement lEarning-based URLLCAware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a besteffort way.Compared with existing task offloading algorithms,DREAM achieves superior performance in throughput,queuing delay,and URLLC.

A Task-oriented Modular and Agent-based Collaborative Design Mechanism for Distributed Product Development

The rapid expansion of enterprises makes product collaborative design （PCD） a critical issue under the distributed heterogeneous environment, but as the collaborative task of large-scale network becomes more complicated, neither unified task decomposition and allocation methodology nor Agent-based network management platform can satisfy the increasing demands. In this paper, to meet requirements of PCD for distributed product development, a collaborative design mechanism based on the thought of modularity and the Agent technology is presented. First, the top-down 4-tier process model based on task-oriented modular and Agent is constructed for PCD after analyzing the mapping relationships between requirements and functions in the collaborative design. Second, on basis of sub-task decomposition for PCD based on a mixed method, the mathematic model of task-oriented modular based on multi-objective optimization is established to maximize the module cohesion degree and minimize the module coupling degree, while considering the module executable degree as a restriction. The mathematic model is optimized and simulated by the modified PSO, and the decomposed modules are obtained. Finally, the Agent structure model for collaborative design is put forward, and the optimism matching Agents are selected by using similarity algorithm to implement different task-modules by the integrated reasoning and decision-making mechanism with the behavioral model of collaborative design Agents. With the results of experimental studies for automobile collaborative design, the feasibility and efficiency of this methodology of task-oriented modular and Agent-based collaborative design in the distributed heterogeneous environment are verified. On this basis, an integrative automobile collaborative R＆D platform is developed. This research provides an effective platform for automobile manufacturing enterprises to achieve PCD, and helps to promote product numeralization collaborative R＆D and management development.

Optimization and Design of Cloud-Edge-End Collaboration Computing for Autonomous Robot Control Using 5G and Beyond

Robots have important applications in industrial production, transportation, environmental monitoring and other fields, and multi-robot collaboration is a research hotspot in recent years. Multi-robot autonomous collaborative tasks are limited by communication, and there are problems such as poor resource allocation balance, slow response of the system to dynamic changes in the environment, and limited collaborative operation capabilities. The combination of 5G and beyond communication and edge computing can effectively reduce the transmission delay of task offloading and improve task processing efficiency. First, this paper designs a robot autonomous collaborative computing architecture based on 5G and beyond and mobile edge computing(MEC).Then, the robot cooperative computing optimization problem is studied according to the task characteristics of the robot swarm. Then, a reinforcement learning task offloading scheme based on Qlearning is further proposed, so that the overall energy consumption and delay of the robot cluster can be minimized. Finally, simulation experiments demonstrate that the method has significant performance advantages.

Cost-Effective Task Scheduling for Collaborative Cross-Edge Analytics

Collaborative cross-edge analytics is a new computing paradigm in which Internetof Things (IoT) data analytics is performed across multiple geographically dispersededge clouds. Existing work on collaborative cross-edge analytics mostly focuses on reducingeither analytics response time or wide-area network (WAN) traffic volume. In thiswork, we empirically demonstrate that reducing either analytics response time or networktraffic volume does not necessarily minimize the WAN traffic cost, due to the price heterogeneityof WAN links. To explicitly leverage the price heterogeneity for WAN cost minimization,we propose to schedule analytic tasks based on both price and bandwidth heterogeneities.Unfortunately, the problem of WAN cost minimization underperformance constraintis shown non-deterministic polynomial (NP)-hard and thus computationally intractablefor large inputs. To address this challenge, we propose price- and performanceawaregeo-distributed analytics (PPGA) , an efficient task scheduling heuristic that improvesthe cost-efficiency of IoT data analytic jobs across edge datacenters. We implementPPGA based on Apache Spark and conduct extensive experiments on Amazon EC2to verify the efficacy of PPGA.

Collaboration strategy for software dynamic evolution of multi-agent system

As the ability of a single agent is limited while information and resources in multi-agent systems are distributed, cooperation is necessary for agents to accomplish a complex task. In the open and changeable environment on the Internet, it is of great significance to research a system flexible and capable in dynamic evolution that can find a collaboration method for agents which can be used in dynamic evolution process. With such a method, agents accomplish tasks for an overall target and at the same time, the collaborative relationship of agents can be adjusted with the change of environment. A method of task decomposition and collaboration of agents by improved contract net protocol is introduced. Finally, analysis on the result of the experiments is performed to verify the improved contract net protocol can greatly increase the efficiency of communication and collaboration in multi-agent system.

Multi-Task Timing Assignment Algorithm for Intelligent Production of Vegetables in Open Field

Vegetable production in the open field involves many tasks,such as soil preparation,ridging,and transplanting/sowing.Different tasks require agricultural machinery equipped with different agricultural tools to meet the needs of the operation.Aiming at the coupling multi-task in the intelligent production of vegetables in the open field,the task assignment method for multiple unmanned tractors based on consistency alliance is studied.Firstly,unmanned vegetable production in the open field is abstracted as a multi-task assignment model with constraints of task demand,task sequence,and the distance traveled by an unmanned tractor.The tight time constraints between associated tasks are transformed into time windows.Based on the driving distance of the unmanned tractor and the replacement cost of the tools,an expanded task cost function is innovatively established.The task assignment model of multiple unmanned tractors is optimized by the consensus based bundle algorithm(CBBA)with time windows.Experiments show that the method can effectively solve task conflict in unmanned production and optimize task allocation.A basic model is provided for the cooperative task of multiple unmanned tractors for vegetable production in the open field.

面向绿色计算的车辆协同任务卸载方法

车辆边缘计算(VEC)为处理计算密集、延迟敏感型任务提供了新的范式,然而边缘服务器在整合可再生能源方面的能力较差。因此,为了提高边缘服务器的能效,该文设计了一种面向绿色计算的车辆协同任务卸载框架。在该框架中,车辆配备能源收集(EH)设备,通过彼此间共享绿色能源和计算资源协作执行任务。为有效促进车辆的参与积极性,该文通过动态定价激励车辆,并综合考虑了车辆的移动性、任务优先级等。为了使卸载决策适应动态环境的变化,该文提出了一种基于双延迟深度确定性策略梯度(TD3)的任务卸载方法,以在最大化所有车辆平均任务完成效用的同时减少边缘端电网电力的使用。最后,仿真结果验证了该方法的有效性,相比基于深度确定性策略梯度(DDPG)和基于贪心原则(GPE)的方法在性能上分别提升了7.34%和37.47%。

任务协作表示增强的要素及关系联合抽取模型

对文本中诸如实体与关系、事件及其论元等要素及其特定关系的联合抽取是自然语言处理的一项关键任务.现有研究大多采用统一编码或参数共享的方式隐性处理任务间的交互,缺乏对任务之间特定关系的显式建模,从而限制模型充分利用任务间的关联信息并影响任务间的有效协同.为此,提出了一种基于任务协作表示增强的要素及关系联合抽取模型(Task-Collaboration Representation Enhanced model for joint extraction of elements and relationships,TCRE).该模型旨在从多个阶段处理任务间的特定关系,帮助子任务进行更细致的调节和优化,促进整体性能的提升.在三个关系抽取和一个事件抽取数据集上进行实验,TCRE在实体识别和关系提取任务上平均性能分别提高0.57%和0.77%,在触发词识别和论元角色分类任务上分别提高0.7%和1.4%.此外,TCRE还显示出在缓解“跷跷板现象”方面的作用.

一种任务驱动的车联网边缘卸载策略

边缘计算为解决未来车联网中移动流量的爆炸式增长提供了可行范式,然而位置的动态变化以及计算任务的多样性和差异性,使得资源有限的边缘服务器很难在规定时间内完成区域内多车辆任务的并行处理需求。基于此,以最小化时延为目标,提出一种结合深度确定性策略梯度算法的任务驱动卸载策略。首先,结合差异性任务类型和紧迫程度进行预处理,构建了一种基于最大延迟容忍度的任务动态优先级调整模型;然后,利用道路区域内的车辆拓扑和通信半径,提出了基于网络密度和负载均衡的动态协作簇划分方法,解决了多样性任务的动态协作卸载优化问题。实验结果表明,所提算法在收敛性、卸载时延及卸载命中率等方面具有性能优势。

基于异步深度强化学习的车联网协作卸载策略

随着车联网(IoV)的快速发展,智能网联汽车产生了大量延迟敏感型和计算密集型任务,有限的车辆计算资源以及传统的云服务模式无法满足车载用户的需求,移动边缘计算(MEC)为解决海量数据的任务卸载提供了一种有效范式。但在考虑多任务、多用户场景时,由于车辆位置、任务种类以及车辆密度的实时性和动态变化,IoV中任务卸载场景复杂度较高,卸载过程中容易出现边缘资源分配不均衡、通信成本开销过大、算法收敛慢等问题。为解决以上问题,重点研究了IoV中多任务、多用户移动场景中的多边缘服务器协同任务卸载策略。首先,提出一种多边缘协同处理的三层异构网络模型,针对IoV中不断变化的环境,引入动态协作簇,将卸载问题转化为时延和能耗的联合优化问题;其次,将问题分为卸载决策和资源分配两个子问题,其中资源分配问题又拆分为面向边缘服务器和传输带宽的资源分配,并基于凸优化理论求解。为了寻求最优卸载决策集,提出一种能在协作簇中处理连续问题的多边缘协作深度确定性策略梯度(MC-DDPG)算法,并在此基础上设计了一种异步多边缘协作深度确定性策略梯度(AMCDDPG)算法,通过将协作簇中的训练参数异步上传至云端进行全局更新,再将更新结果返回每个协作簇中提高收敛速度。仿真结果显示,AMC-DDPG算法较DDPG算法至少提高了30%的收敛速度,且在奖励和总成本等方面也取得了较好的效果。

多机协同自主任务规划系统研究综述

针对多机协同自主任务规划显著提升无人机任务感知效能、提高任务执行能力的现状,对其关键技术进行阐述,主要包括知识表示、通感一体化、任务调度及航迹规划等技术。针对知识表示技术,阐述基于任务环境上下文感知的知识库构建方法,进而对多域融合知识图谱构建、动态知识图谱更新与共享方法进行分析总结;针对通感一体化技术,分析了环境适变、灵活可扩的多机协同通感一体化架构,进而揭示多机协同通感一体化理论能限,阐述面向任务差异性需求的资源共享方法;针对任务调度及航迹规划技术,阐述资源适配的多机协同自主任务调度方案,并对基于动力学模型的航迹控制策略及不完美环境下的鲁棒控制机制进行分析总结。

基于邻域采样的多任务图推荐算法

近年来,图神经网络(GNN)成为解决协同过滤的主流方法之一。它通过构建用户-物品图,模拟用户与物品的交互关系,并用GNN学习它们的特征表示。尽管现有在模型结构上的研究已取得了较大进展,但如何在图结构上更有效地进行负采样仍未有效解决。为此,提出一种基于邻域采样的多任务图推荐算法。该算法提出了一种基于GNN的邻域采样策略,该策略以每个用户为中心构建子图,将次高阶物品作为用户邻域采样的负样本,可以更有效地挖掘强负样本并提高采样质量。通过GNN对图结点进行信息聚合与特征提取,得到结点的最终嵌入表示。设计一种余弦边际损失来过滤部分冗余负样本,以有效减少采样过程中的噪声数据。同时,该算法引入了多任务策略对模型进行联合优化,以增强模型的泛化能力。在3个公开数据集上进行的大量实验表明,该算法在大多数情况下明显优于其他主流算法。

面向缓存的动态协作任务迁移技术研究

边缘网络中不断出现的计算密集和延迟敏感型业务推动了任务迁移技术的快速发展。然而,任务迁移过程中存在应用场景复杂多变、问题建模难度高等技术瓶颈。尤其是考虑用户移动时,如何保证用户服务的稳定性和连续性,设计合理的任务迁移策略仍是一个值得深入探讨的问题。因此,提出了一种移动感知的服务预缓存模型和任务预迁移策略,将任务迁移问题转化为最优分簇与边缘服务预缓存的组合优化问题。首先,基于用户的移动轨迹对当前执行任务状态进行预测,引入动态协作簇和迁移预测半径的概念,提出了一种面向移动和负载两种任务场景的预迁移模型,解决了何时何地迁移的问题。然后,针对需要迁移的任务,基于最大容忍时延约束分析协作簇半径和簇内目标服务器数量的极限值,提出了以用户为中心的分布式多服务器间动态协作分簇算法(Distributed Dynamic Multi-server Cooperative Clustering Algorithm,DDMC)以及面向服务缓存的深度强化学习算法(Cache Based Double Deep Q Network,C-DDQN),解决了最优分簇和服务缓存问题。最后,利用服务缓存的因果关系,设计了一种低复杂度的交替最小化服务缓存位置更新算法,求解出了最佳迁移目标服务器集合,实现了任务迁移中的服务器协作及网络负载均衡。实验结果表明,提出的迁移选择算法具有良好的鲁棒性和系统性能,相比其他迁移算法所消耗的总成本降低了至少12.06%,所消耗的总时延降低了至少31.92%。

一种动态锁定式多星协同任务规划的A2C算法

提出了一种基于强化学习算法的多星协同任务规划方法。该方法将多星协同任务规划视为多个双星协同规划,采用相邻双星锁定的方式来共享双星任务规划结果信息,基于A2C(优势动作评价)强化学习算法对双星任务规划的结果进行再调整。针对多星协同任务规划状态空间复杂且变化的问题,设计了两级状态空间和价值评价函数决策的方法,对强化学习所依赖的状态空间进行维度限制,确保智能体对任务进行调整的过程不影响状态空间维度。算法设计过程考虑了多种约束条件,设置了天气、成像质量和成像优先级等可调参数作为强化学习A2C算法的评价参数,这些可调参数有助于用户自定义决策评价体系。最后,通过仿真验证了算法的可行性。仿真结果表明,该算法多星协同任务规划的组合任务抛弃率小于10%。

战场环境下遗传黏菌算法的多机协同任务分配

针对已知战场环境下的多无人机协同任务分配问题,提出基于融合遗传黏菌算法的任务分配方法.综合单机约束、机群总体收益和损耗以及任务需求等条件,构建多机协同任务分配目标函数.针对遗传算法易陷入局部最优、黏菌算法收敛慢的问题,改进遗传迭代和黏菌探索行为.将离散黏菌算法引入遗传算法,增强融合算法的搜索能力.在种群迭代中加入干扰操作,提高求解精度.在已知环境下进行分配试验和路径演示,并与其他算法进行对比.结果表明,利用所提出的融合算法,能够获得目标函数值更高的任务分配方案.