Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

Computation Rate Maximization in Multi-User Cooperation-Assisted Wireless-Powered Mobile Edge Computing with OFDMA

In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.

Performance Analysis of Cooperative NOMA Based Intelligent Mobile Edge Computing System

In this manuscript, a cooperative non-orthogonal multiple access based intelligent mobile edge computing(NOMA-MEC) communication system is constructed in detail. The nearby user is viewed as a decoding and forwarding relay, which can assist a distant user in offloading tasks to the intelligent MEC server. Then, the closed-form expressions of offloading outage probability for a pair of users are derived in detail to evaluate the performance of the cooperative NOMA-MEC system. Furthermore, the approximate expressions of offloading outage probability are provided in the high signal-to-noise ratio region. Based on the asymptotic analyses, the diversity order of distant user and nearby user is n+m+1 and n+1, respectively. The system throughput and energy efficiency of cooperative NOMA-MEC are analyzed in delay-limited transmission mode. Numerical results show that 1) Cooperative NOMA-MEC is better than orthogonal multiple access(OMA) in terms of offload performance;2) The offload performance of cooperative NOMA-MEC system improves as the number of transmission task decreases;and 3) Cooperative NOMA-MEC performs better than OMA in energy efficiency.

Call for Papers The 6^(th) International Conference on Grid and Cooperative Computing

Recent years have seen rapid advances in various grid-related technologies, middleware, and applications. The GCC conference has become one of the largest scientific events worldwide in grid and cooperative computing. The 6th international conference on grid and cooperative computing (GCC2007) Sponsored by China Computer Federation (CCF),Institute of Computing Technology, Chinese Academy of Sciences (ICT) and Xinjiang University ,and in Cooperation with IEEE Computer Soceity ,is to be held from August 16 to 18, 2007 in Urumchi, Xinjiang, China.

OPTIMIZATION DESIGN OF HYDRAULIC MANIFOLD BLOCKS BASED ON HUMAN-COMPUTER COOPERATIVE GENETIC ALGORITHM

Optimization design of hydraulic manifold blocks (HMB) is studied as acomplex solid spatial layout problem. Based on comprehensive research into structure features anddesign rules of HMB, an optimal mathematical model for this problem is presented. Usinghuman-computer cooperative genetic algorithm (GA) and its hybrid optitation strategies, integratedlayout and connection design schemes of HMB can be automatically optimized. An example is given totestify it.

Cooperation Oriented Computing:A Computing Model Based on Emergent Dynamics of Group Cooperation

Traditional way of problem solving tries to deliver data to program.But when the problem’s complexity exponentially increases as the data scale increases,to obtain the solution is difficult.Group cooperation computing model works in an inverse way by delivering program to data.It first models each single data as individual and data unit as group of individuals.Then,different cooperation rules are designed for individuals to cooperate with each other.Finally,the solution of the problem emerges through individuals’cooperation process.This study applies group cooperation computing model to solve Hamilton Path problem which has NP-complete time complexity.Experiment results show that the cooperation model works much better than genetic algorithm.More importantly,the following properties of group cooperation computing are found which may be different from the traditional computing theory.(1)By using different cooperation rules,the same problem with the same scale may exhibit different complexities,such as liner or exponent.(2)By using the same cooperation rule,when the problem scale is less than a specific threshold,the problem’s time complexity is liner.Otherwise,the problem complexity may be exponent.

Multi-UAV Network Assisted Intelligent Edge Computing:Challenges and Opportunities

Introducing multi-UAV network with flexible deployment into mobile edge computing(MEC)can effectively improve the quality of service of Internet-of-Things services,reduce the coverage cost and resource waste rate of edge nodes,and also bring some challenges.This paper first introduces the current situation and pain points of mobile edge computing,then analyzes the significance and value of using multi-UAV network to assist mobile edge computing,and summarizes its key technologies and typical applications.In the end,some open research problems and technology prospects of multi-UAV network assisted intelligent edge computing are put forward,which provide new ideas for the future development of this field.

An Open Environment for Cooperative Equational Solving

We describe a system called CFLP which aims at the integration of the best features of functional logic programming (FLP), cooperative constraint solving (CCS), and distributed computing. FLP provides support for defining one's own abstractions over a constraint domain in an easy and comfortable way, whereas CCS is employed to solve systems of mixed constraints by iterating specialized constraint solving methods in accordance with a well defined strategy. The system is a distributed implementation of a cooperative constraint functional logic programming scheme that combines higher order lazy narrowing with cooperative constraint solving. The model takes advantage of the existence of several constraint solving resources located in a distributed environment (e.g., a network of computers), which communicate asynchronously via message passing. To increase the openness of the system, we are redesigning CFLP based on CORBA. We discuss some design and implementation issues of the system.

An Improved Model for Computing-Intensive Tasks on Heterogeneous Workstations

An improved algorithm, which solves cooperative concurrent computing tasks using the idle cycles of a number of high performance heterogeneous workstations interconnected through a high-speed network, was proposed. In order to get better parallel computation performance, this paper gave a model and an algorithm of task scheduling among heterogeneous workstations, in which the costs of loading data, computing, communication and collecting results are considered. Using this efficient algorithm, an optimal subset of heterogeneous workstations with the shortest parallel executing time of tasks can be selected.

Man-Computer Cooperated Intelligent Science and Intelligent Technology

During 1990’s, there were two important events in the science and technology field which highly related to research on artificial intelligence (AI): One was the fifth generation computer project of Japan, and the other was the intelligent computer project (863-306) of China. After these projects were performed, the realization of “intelligent automation" was transferred and improved. The academician Qian Xuesen has made a speech to summarize the importance of man-computer cooperative information processing, and the aim was not intelligent computer. The man-computer cooperative intelligent systems should be studied and developed. The establishment of a new discipline on intelligent science and intelligent technology is proposed by the author of this article.

面向车辆边缘计算的任务合作卸载

随着物联网技术和人工智能技术的飞速发展,车辆边缘计算越来越引起学者的关注。车辆如何有效地利用其周边的各种通信、计算和缓存资源,结合边缘计算系统模型将计算任务迁移到离车辆更近的路边单元,已经成为目前车联网研究的热点。由于车辆应用设备计算资源的有限性,车辆用户的任务计算需求无法满足,需要提升车辆周边计算资源的利用率来完成计算任务。本文研究了车辆边缘计算中任务的合作卸载机制,以最小化车辆任务的计算时延。首先,考虑周边停泊车辆以及路边单元的计算资源,设计了由云服务器层、停泊车辆合作集群层和路边单元合作集群层组成的任务合作卸载3层系统架构,通过路边单元合作集群和停泊车辆合作集群的合作卸载,充分利用系统的空闲计算资源,进一步提高了系统的资源利用率。然后,基于k-聚类算法的思想提出了路边单元合作集群划分算法对路边单元进行合作集群的划分,并采用块连续上界最小化的分布式迭代优化方法设计了任务合作卸载算法,对终端车辆用户的任务进行卸载计算。最后,通过将本文算法和其他算法方案进行实验仿真对比,仿真结果表明,本文算法在系统时延和系统吞吐量方面具有更好的性能表现,可以降低23%的系统时延,并且能提升28%的系统吞吐量。

基于无线供能和节点间合作的二进制计算卸载方案

针对性研究了无线供能下的移动边缘计算,其中计算节点能够自主执行计算任务或将其卸载给空闲节点或边缘服务器,以最大化节点的总计算速率(Sum Computing Rate,SCR)。首先,将SCR最大化问题建模为一个非凸问题,考虑到能量因果和任务因果等约束。接着,提出了一个基于深度强化学习的解决方案,采用深度神经网络输出近似最优的二进制卸载决策。最后,设计了高效算法来解决在给定卸载决策下的子问题。该方案具备在线学习能力,具有快速收敛和低计算复杂度的特点,实现了近似最大SCR。

抗理性敌手共谋的安全K-prototype聚类

针对云环境下数据隐私泄露及聚类过程中云服务器间共谋的问题,提出一种抗理性敌手共谋的安全协作K-prototype聚类方案,目的在不泄露各方隐私数据情况下根据距离度量将相似的数据进行聚类。首先,考虑到同态加密不直接支持非线性计算,基于同态加密和加性秘密共享技术设计安全比较协议和安全大于协议,并确保输入数据、中间结果及模型参数均为加性秘密份额形式,以防止单个服务器能够获得完整数据,且能实现非线性函数的精确计算,在已设计的安全计算协议基础上,实现安全距离计算、安全聚类标签更新、安全聚类中心更新、聚类标签及聚类中心重构;其次,根据博弈均衡理论设计多种高效激励机制,构造互制合约及举报合约以约束云服务器诚实非共谋地执行聚类任务;最后,对所提的安全计算协议及合约进行理论分析,并对该方案的性能进行实验验证。实验结果表明,与明文环境下的模型精度相比,该方案的模型精度损失控制在0.22%内,进而验证了所提方案的有效性。

跨机构联邦学习的激励机制综述

联邦学习作为一种分布式机器学习,有效地解决了大数据时代的数据共享难题。其中,跨机构联邦学习是机构之间互相合作的一种联邦学习类型。如何在跨机构合作的过程中设计合理的激励机制十分重要。文中从跨机构合作的角度,对现有的跨机构联邦学习的激励机制研究进行了综述。首先介绍跨机构合作过程中的3个基本问题,即高隐私性、数据异质性、公平性,然后分析了以全局模型为中心和以参与者为中心这两种不同的跨机构合作模式下的激励机制设计方法,最后总结了影响跨机构合作稳定发展的几个影响因素,即参与者的数据演变、参与者合作关系变动和参与者的负面行为,并展望了跨机构联邦合作的未来方向。

基于深度学习的组合服务推荐

针对服务质量参数波动性与服务计算环境的不确定性问题,将深度神经网络的高维输入与强化学习相结合,解决复杂云计算环境下的动态优化和推荐问题,实现了一个基于三层多智能体架构的群智协同服务推荐模型。从更细粒度的活动级构造服务过程的活动状态迁移模型,分析业务活动的局部QoS评价和全局服务协作度量,解决了大规模服务过程建模中状态转移的时间依赖性问题。提出了一种基于深度学习的服务推荐算法(EDQL-BPR),并设计了基于粒子群算法的Q值更新策略,提高深度神经网络的学习智能体的寻优效率,有效提高了BPaaS服务的推荐质量,实现动态环境下效率和适应性的良好平衡。

基于百度飞桨平台的计算机视觉课程改革与实践

计算机视觉是当前计算机和人工智能领域的热门方向,但前沿课程内容在算法、算力和数据方面的需求,使得传统授课模式无法满足对当前学生实践能力培养的需求。在教育部产学合作协同育人的背景下,该文从课程核心内容、前沿技术扩展、先进实验设计和考核方式改革等多方面,开展课程的革新与探索,并在实际教学活动详解的基础上,验证教学质量和学生综合能力的提升,为计算机视觉相关课程提供参考。

智能时代赛博格体育教师身份协商与教育新景观

智能时代的到来使教育现场人与科技的关系日趋密切,呈现人机共生赛博格特征。运用文献资料、逻辑分析等方法探究赛博格体育教师身份协商与教育新景观,为教师“身体”与“工具”的融合提供路径指引,补充体育教师在运用科技工具教学过程中角色定位的研究缺憾,对提升教师专业素养和提高课程质量呈现新的审视。赛博格体育教师身份需要从:探索教育工具的身体属性;思考科技对于体育教师主体价值的影响;以及科技工具与教师身体的嵌合方式和数位原民学习者需求维度综合协商。同时对建构体育课新景观:分布式的体育知识传递环境;全流程的个性化动作学习体验;伴随式的精准化评价生态系统做出诠释。

共赢促进合作的认知计算机制:互惠中积极期望与社会奖赏的作用

在社会互动中,人们常表现出有条件的合作行为,即只有在预期他人也合作时人们才愿意合作。当前该过程的认知机制尚不明确。本文采用多回合版本的囚徒困境范式,两项实验均表明个体的合作行为随合作者合作行为的提高而提高。认知计算模型显示个体同时采用了一阶信念(只根据他人过去的行为)与二阶信念(既根据他人过去的行为,也考虑自己的行为对他人产生的影响)去更新他们对合作者的合作概率的期望。结果显示个体的有条件合作行为的提升由积极期望(即合作成功使得个体对合作者建立了积极的期望)与社会奖赏(由合作本身带来的额外奖励)共同驱动。这些结果揭示了有条件的合作行为的计算认知学习机制,阐明了积极期望和社会奖赏对合作的促进作用,能为社会中各领域合作的成功推动提供了重要的科学证据与参考价值。

协作处理任务的多无人机辅助移动边缘计算

随着通信技术的发展,移动边缘计算(mobile edge computing,MEC)被认为是处理计算密集型和延迟敏感任务的关键技术,然而在灾难响应、紧急救援等情景中,边缘服务器无法快速部署并提供任务处理服务。无人机(unmanned aerial vehicle,UAV)辅助MEC以其部署简便、机动性强的特点受到关注。但是UAV的计算资源和能量有限,如何进行资源分配成为难题。针对该问题,提出了一种高效利用资源的多UAV协作策略(LUAVs-Cor)。该策略通过多UAV协作的方式动态处理任务,为了充分利用UAV空闲的计算资源,通过搜索最优任务组合的方式确定任务传输策略。此外,通过估计UAV处理能力、任务数量及处理情况优化UAV派遣数量,实现了UAV的动态部署并减少了能量消耗。通过大量仿真实验得出,LUAVs-Cor策略的服务容量提升了约6.8%,UAV整体能耗降低了10.3%。提出的LUAVs-Cor策略中无人机的协作代价较小,实现在较低的能耗代价下为更多用户服务。