ACS-based resource assignment and task scheduling in grid

To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.

Task scheduling and virtual machine allocation policy in cloud computing environment

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.

Dynamic access task scheduling of LEO constellation based on space-based distributed computing

A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.

Flexible Task Scheduling Based on Edge Computing and Cloud Collaboration

With the rapid development and popularization of 5G and the Internetof Things, a number of new applications have emerged, such as driverless cars.Most of these applications are time-delay sensitive, and some deficiencies werefound during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved atpresent. In 5 g environments, edge computing can better meet the needs of lowdelay and wide connection applications, and support the fast request of terminalusers. However, edge computing only has the edge layer computing advantage,and it is difficult to achieve global resource scheduling and configuration, whichmay lead to the problems of low resource utilization rate, long task processingdelay and unbalanced system load, so as to lead to affect the service quality ofusers. To solve this problem, this paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes agenetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve taskscheduling and resource allocation, and designs a series of experiments to verifythe effectiveness of the GSA-EDGE algorithm. The experimental results showthat the proposed method can reduce the time delay of task processing comparedwith the local task processing method and the task average allocation method.

A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing

With the continuous evolution of smart grid and global energy interconnection technology,amount of intelligent terminals have been connected to power grid,which can be used for providing resource services as edge nodes.Traditional cloud computing can be used to provide storage services and task computing services in the power grid,but it faces challenges such as resource bottlenecks,time delays,and limited network bandwidth resources.Edge computing is an effective supplement for cloud computing,because it can provide users with local computing services with lower latency.However,because the resources in a single edge node are limited,resource-intensive tasks need to be divided into many subtasks and then assigned to different edge nodes by resource cooperation.Making task scheduling more efficient is an important issue.In this paper,a two-layer resource management scheme is proposed based on the concept of edge computing.In addition,a new task scheduling algorithm named GA-EC(Genetic Algorithm for Edge Computing)is put forth,based on a genetic algorithm,that can dynamically schedule tasks according to different scheduling goals.The simulation shows that the proposed algorithm has a beneficial effect on energy consumption and load balancing,and reduces time delay.

Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.

Overall plan and design of the task management system of ternary optical computer

t In this paper an overall scheme of the task management system of ternary optical computer （TOC） is proposed, and the software architecture chart is given. The function and accomplishment of each module in the system are described in general. In addition, according to the aforementioned scheme a prototype of TOC task management system is implemented, and the feasibility, rationality and completeness of the scheme are verified via running and testing the prototype.

Run-Time Dynamic Resource Adjustment for Mitigating Skew in MapReduce

MapReduce is a widely used programming model for large-scale data processing.However,it still suffers from the skew problem,which refers to the case in which load is imbalanced among tasks.This problem can cause a small number of tasks to consume much more time than other tasks,thereby prolonging the total job completion time.Existing solutions to this problem commonly predict the loads of tasks and then rebalance the load among them.However,solutions of this kind often incur high performance overhead due to the load prediction and rebalancing.Moreover,existing solutions target the partitioning skew for reduce tasks,but cannot mitigate the computational skew for map tasks.Accordingly,in this paper,we present DynamicAdjust,a run-time dynamic resource adjustment technique for mitigating skew.Rather than rebalancing the load among tasks,DynamicAdjust monitors the run-time execution of tasks and dynamically increases resources for those tasks that require more computation.In so doing,DynamicAdjust can not only eliminate the overhead incurred by load prediction and rebalancing,but also culls both the partitioning skew and the computational skew.Experiments are conducted based on a 21-node real cluster using real-world datasets.The results show that DynamicAdjust can mitigate the negative impact of the skew and shorten the job completion time by up to 40.85%.

A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide.Modern data centres’operating costs mostly come from back-end cloud infrastructure and energy consumption.In cloud computing,extensive communication resources are required.Moreover,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements.It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers.This paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic load.The primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data centres.We first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource allocation.EC-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed environment.We tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler metrics.The EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

伴随维修资源配置与任务调度的多目标联合优化

现代战争作战节奏快、横跨地域广,对伴随维修保障模式提出了更高的要求。如何在复杂战场中实现资源配置与任务调度的流程整合,充分发挥伴随维修系统效能,已经成为当今装备维修保障的迫切需求与发展方向。综合考虑多中心、开放式、多修复状态、时间窗、非遍历、容量限制等因素,首次提出随时补货的思路,将地区危险系数与维修组当前成本相结合,以维修组创造维修效益最大化、承担风险成本最小化为目标,建立更完善的数学模型。针对上述问题进行相应的编码,改进多目标人工蜂群(Multi Objective Artificial Bee Colony,MOABC)算法,提出求解质量、收敛速度良好的多目标人工蜂群-多记忆蜜源(Multi Objective Artificial Bee Colony for Memorizing Multiple Honey Sources per Bee,MOABC-MMHS)算法;针对小部分极端值影响均值的问题改进覆盖率(Coverage,C)指标的使用方式,以频率的方式对其进行展示;通过仿真实验与多指标评价验证模型和算法的科学性,实现了伴随维修资源配置与任务调度的联合优化。研究结果表明,上述模型与算法能够为伴随维修保障提供相应的辅助决策。

多接入边缘计算赋能的AI质检系统任务实时调度策略

AI质检是智能制造的重要环节,其设备在进行产品质量检测时会产生大量计算密集型和时延敏感型任务。由于设备计算能力不足,执行检测任务时延较大,极大影响生产效率。多接入边缘计算(MEC)通过将任务卸载至边缘服务器为设备提供就近算力,提升任务执行效率。然而,系统中存在信道变化和任务随机到达等动态因素,极大影响卸载效率,给任务调度带来了挑战。该文面向多接入边缘计算赋能的AI质检任务调度系统,研究了联合任务调度与资源分配的长期时延最小化问题。由于该问题状态空间大、动作空间包含连续变量,该文提出运用深度确定性策略梯度(DDPG)进行实时任务调度算法设计。所设计算法可基于系统实时状态信息给出最优决策。仿真结果表明,与基准算法相比,该文所提算法具有更好的性能表现和更小的任务执行时延。

云边端协同下的任务调度与资源分配方法

为了优化多用户终端、多边缘节点以及云服务器之间的计算资源分配,找到最优的任务调度和资源分配方案。本文在算网融合的基础上提出了一种3层计算资源分配方案,考虑到通信网络带宽的有限性,设立了任务计算优先级和任务上传策略,并在多边缘节点的环境下,制定了多节点并行计算规则,以最大化计算资源的利用率,通过深度强化学习算法进行训练,以学习最优的任务调度和资源分配策略。实验结果表明,本文所提方法在缩短计算任务完成时间方面表现出色,并且在任务数据量增长的情况下,依然表现出良好的鲁棒性。

计算机网络服务质量优化方法研究综述

优化方法为设计更好的计算机网络服务质量保证机制提供了有力的理论支持.相较于传统启发式的网络设计方法,优化方法可以从理论上找到问题的最优解,从而从根本上克服了启发式方法不能证明方案优劣程度的缺陷.因此,基于优化方法的机制设计与性能评价成为了当前网络服务质量领域中的一个前沿研究领域.大量的研究着眼于从优化理论的角度重新建立网络模型,按照优化理论给出的求解机制和实施方案设计网络协议.计算机网络的优化可以划分为资源分配、任务调度、网络资源部署和系统参数配置等4方面问题.对计算机网络服务质量的优化建模、求解、实施和评价成为当今研究的热点.根据最新网络服务质量优化的研究进展,文中对计算机网络服务质量研究中所涉及到的优化技术进行了研究与综述,主要包括4个方面:系统地描述了计算机网络模型优化算法的通用表达形式,并将其按照不同的方式进行分类;探讨了不同结构的优化模型对应的求解方案;对比分析了不同优化算法的实施方案,给出了方案之间的联系与区别;归纳了计算机网络中优化方案的性能和代价评价方法.最后,对全文进行了总结,并展望了进一步的研究方向.

基于深度强化学习的多用户边缘计算任务卸载调度与资源分配算法

移动边缘计算(Mobile Edge Computing,MEC)把计算和存储等资源部署在网络边缘以满足某些对延迟要求苛刻的应用.用户设备可以通过无线网络将计算任务整体或者部分卸载到边缘服务器执行从而降低延迟和本地耗能,进而获得良好的用户体验.现有传统优化算法在MEC卸载决策和资源分配方面是可行的,但传统优化算法并不很适合高实时性的MEC系统.深度强化学习可以通过与传统优化算法不同的方式,建立尝试-奖励反馈机制,通过积累经验进行学习,从而完成优化目标.本文考虑移动边缘计算网络中多用户多任务卸载的情况下,研究任务卸载中的卸载决策和任务调度以及服务器资源分配的问题,以最小化系统延迟和传输耗能、本地耗能为目标,基于深度强化学习提出了一种多用户多任务下的任务卸载调度与资源分配算法,在上层服务器分配资源确定的情况下,提出基于贪心策略的流水车间调度算法解决了任务卸载决策和卸载调度问题,下层采用强化学习方法优化服务器资源分配问题.仿真结果表明,所提出的方法在降低延迟和本地耗能方面有比较优越的性能.

网格系统中的层次化资源分配与任务调度

讨论具有大量任务数的一类应用在网格系统中的资源管理和控制问题.提出了具有层次化结构的资源分配与任务调度模型,它由任务分发器和次级调度器组成.上层的任务分发器根据任务的性质和需求,并参考下层次级调度器的执行情况,将任务分发到相应的次级调度器上;而下层次级调度器负责将分发来的任务进行实际的资源分配及调度工作.模拟分析表明随着次级调度器个数的增加,任务调度的并行性增加,但系统的优化趋势逐步减缓.在实际应用中,合理选择次级调度器个数,可在满足调度性能的同时减少设备投入.

支持资源协同分配的网格任务调度研究

提出通用的网格和任务执行模型,并以此为基础,给出一种支持资源协同分配的任务调度算法。算法通过定义临界资源的概念,改进了传统的列表调度算法。模拟实验结果表明该调度策略更符合网格计算的复杂环境,能得到较短的任务执行时间,并更好的支持不同类型资源的协同分配。

基于关联规则的网格资源分域管理

为有效优化网格资源管理和任务调度方案,提出了一种基于关联规则模型进行子任务分组的网格资源分域管理机制。该方法对业已切分的用户任务根据切分后的子任务间的关联性,对所有子任务予以分组,以达到增强子任务组内的关联性及组间的独立性。分组完成后,管理机制将以子任务组作为运行单元,从而使得子任务组在运行过程中减少相互间的频繁通信,达到提高系统运行效率的目的。然后,在事先分好类的网格资源中根据需求临时为子任务组的运行划分“资源域”并以“域”为单位进行管理。最后,通过仿真实验分析,在调度性能上将本机制与传统多队列Backfilling、FCFS等调度方案的性能差异进行比较,证明了该机制的优越性与实用性。

基于免疫遗传算法的网格任务调度

研究了网格环境下任务调度问题,提出了一个任务调度机制:基于任务图将每一个可能的任务调度方案表示成一个任务-资源分配图,将网格任务调度问题转化为任务-资源分配图优化选取问题.提出了一种基于免疫遗传算法的、实现任务-资源分配图优化选取的任务调度算法.该算法将任务-资源分配图的最长路径作为抗原,每一个任务-资源分配图对应一个抗体.实验结果表明这个算法在全局优化能力及收敛速度上均有显著提高.

基于蚂蚁算法的网格计算任务调度方法设计

网格环境中的资源情况和任务情况异常复杂,难以用实验测试各种资源管理和任务调度方法的有效性.文中提出一种网格仿真系统结构,并设计和实现了基于蚂蚁算法的任务调度策略,将任务调度和资源管理相结合,兼顾系统的负载平衡和QOS,取得了较理想的实验结果.