虚拟化移动核心网任务调度算法研究

针对虚拟化移动核心网的任务调度问题进行研究。Min-Min调度算法是一个简单、快速、有效的算法,但是它很难满足移动用户对服务质量的要求。提出一种基于虚拟机响应时间的改进算法。在虚拟机与元任务映射过程中,首先按照Min-Min算法进行匹配,然后计算每个与元任务匹配的虚拟机的响应时间,将任务划分为满足响应时限和不满足两部分,对满足响应时限的任务进行调度,为不满足调度响应时限的任务重新分配一个空闲的虚拟机中执行时间最短的虚拟机,尽最大可能避免任务执行时间超出响应时限,导致任务调度失败,进而影响用户满意度。在虚拟机和元任务一定的情况下,分别仿真这两种算法的调度过程,得到任务完成时间、任务成功率和负载情况图,对仿真实验结果进行对比分析。     

基于VR虚拟漫游系统的任务树交互设计应用研究

为了提高VR虚拟漫游系统的运行效率,从而改善用户体验,提出一种基于任务树的人机交互任务调度算法。首先对VR漫游系统的工作流程进行介绍,分析了实时人机交互的重要性。然后将人机交互任务分解为具有优先级的树状层次结构,并在确保按照优先级调度的条件下,通过对比执行时间来消除CPU调度的盲目性,从而提高运行速度。仿真实验结果表明,相比先来先服务(FCFS)调度算法和短作业优先(SJF)调度算法,提出的人机交互任务调度算法在平均响应时间和平均等待时间方面表现出更好的性能。     

基于移动边缘计算的任务调度算法设计

移动边缘计算(Mobile Edge Computing, MEC)作为一种新兴的计算模式可以将智能设备上的任务调度到MEC服务器中执行以解决智能设备资源受限问题。多用户场景下以时延和任务依赖性为约束的任务调度问题是移动边缘计算中的研究热点之一。针对该问题建立了任务调度模型,然后依据场景特性将任务调度问题转换为最小化能量消耗问题。针对任务调度问题的实时性和持续性进一步将优化问题缩放至较小规模的优化问题,并依据优化问题的解设计了一个实时调度算法。最后使用遗传算法作为对比算法进行仿真实验。实验结果表明实时调度算法比遗传算法更有效地降低了智能设备整体能量消耗,并在高并发、高时延要求等情况下仍保持良好的性能。     

异构网络化汽车电子系统中多DAG离线任务调度

公平性和通信开销已成为影响调度性能的主要瓶颈,首先在任务优先级排序阶段,提出基于通信开销权值的轮转调度的公平排序标准;在处理器选择阶段,提出在插入法的基础上将任务分配到具有最小选择值的选择标准;综合上述2个阶段,提出以降低调度长度和减少通信开销为目标的多DAG离线公平任务调度(MDOFTS, multiple DAGs off-line and fairness task scheduling)算法。异构网络化汽车电子系统是一个典型的混合关键级嵌入式系统,既要确保实时性又要降低调度长度,提出了以满足安全关键DAG的多DAG离线优先级任务调度(MDOPTS, multiple DAGs off-line and Priority task scheduling)算法。综合MDOFTS和MDOPTS,提出多DAG离线自适应任务调度(MDOATS, multiple DAGs off-line and adaptive task scheduling)算法,在满足实时性的基础上提高调度性能。实例分析和实验结果表明,提出的算法在调度长度、通信开销、不公平性、最差响应时间和实时性上都优于其他算法。     

一种基于实时中间件的可扩展的服务调度框架

随着中间件技术的发展,中间件需要处理的任务越来越多,实时性的要求也越来越高,如何高效率的对这些任务进行合理的调度成为实时中间件需要解决的问题.本文针对实时中间件的调度问题提出了一种集成了多种调度算法的调度框架.既能满足中间件对于非实时性任务调度的需要,又能满足其对实时性任务调度的需求.对于调度算法的集成采用统一的接口,因此如果有新的调度算法需求,可以进行扩展.     

云环境下任务调度算法对比研究

云计算是完全基于互联网的新兴技术。云计算环境中的任务调度问题一直都是该领域的研究热点。合理高效的任务调度算法在云环境中能有效的缩短任务完成时间,提高系统负载均衡,更好的满足用户与云提供商的需求。本文研究了云平台的任务调度机制,探究了任务调度过程中的关键性指标。通过云仿真平台CloudSim实现并分析了顺序调度算法、Min-Min算法和Max-Min算法,对比其在随机生成用户任务负载与虚拟机计算资源的情况下的任务完成时间,实验证明Min-Min算法与Max-Min算法均优于顺序调度算法。以此为未来研究提供实验支撑和方向。     

HRRF任务调度在汽车仪表中的改进与实现

汽车仪表中的嵌入式系统通常采用前后台循环设计模式,该模式下各任务执行频率强制相同,执行顺序无法改变,任务每次等待时间波动较大,难以保证实时性。为解决上述问题,利用高响应比优先(HRRF)任务调度算法的优点,对其进行改进,并将其应用在汽车仪表的软件设计中。把周期性任务按优先特性分类,实时更新各任务的等待时间和服务时间,考虑任务截止期错失,每次选取同类别中响应比最高的任务执行。实际应用表明,该方法调度开销小,实时性高,且方便维护和移植。     

基于5G+MEC的电网边缘计算平台任务安全性调度方法

为保证电网边缘计算平台任务调度的安全性以及任务调度所需的数据质量,提出基于5G+MEC的电网边缘计算平台任务安全性调度方法。结合机密性服务和完整性服务,构建任务调度安全等级模型,约束调度任务队列调度传输过程中的风险,实现5G核心网的安全传输;确认优先级队列类型,选择最小化队列与最大队列,进行数据资源最大化支持、MEC 设备端的任务调度,构建分布式任务调度模型,并利用 Lyapunov候选函数提升任务调度的稳定性,通过交替方向乘子法求解模型,获取任务安全性调度最优解。测试结果表明,应用该方法后,风险概率结果均在0.15～0.35的范围波动,MEC设备提供的相关数据与核心服务器调度任务的拟合程度均高于0.92;任务调度数据的质量分值也高于0.94。     

基于人工蜂群算法的中继卫星任务调度研究

研究中继卫星任务调度问题可以为跟踪与数据中继卫星系统(TDRSS)的任务计划编排提供科学合理的决策方法,任务调度模型的建立与调度算法的设计是中继卫星任务调度的两个关键问题。该文针对中继卫星任务调度问题特点,综合考虑中继卫星与用户航天器之间具有可见时间窗、用户提交的任务属性、中继卫星前向资源受限等约束条件,建立了中继卫星任务调度约束规划模型并提出基于人工蜂群(ABC)算法的中继卫星任务调度算法。最后,通过仿真数据分析,表明基于人工蜂群算法的中继卫星任务调度算法是一种有效的、合理的调度方法。     

基于改进时间指针的相控阵火控雷达任务调度算法

针对相控阵火控雷达负载饱和情况下的时间资源分配主观性强、雷达任务调度及时性差的问题,结合任务综合优先级,提出一种基于改进时间指针的相控阵火控雷达任务调度算法。该算法在传统时间指针算法的基础上增加调度前比较环节,即提取当前时刻综合优先级最高的两个任务,选择时间偏移量较小的任务进行调度。仿真结果表明：相比于传统时间指针调度算法,改进后的算法提升了任务的调度成功率、时间利用率和射击价值率,降低了平均时间偏移率。     

基于实时业务挤占的OFDMA系统的无线资源分配方案

为了在实时业务和非实时业务共享无线资源的场景中增加系统吞吐量,该文提出了一种基于实时业务挤占的无线资源分配方法,该方案首先实施统一调度,然后进行实时业务挤占过程。在保证对实时业务服务的情况下,实时业务挤占的无线资源分配方案提高了多用户分集效果,增加了系统的吞吐量。理论和仿真分析表明,与已有的传统的实时业务和非实时业务共享无线资源的调度方案相比,该方案能够提供更高的系统吞吐量和频谱效率。在实际应用中,该方案具有一定的可行性和可操作性。     

Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

An efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.     

EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.     

Multidimensional QoS cloud computing resource scheduling method based on stakeholder perspective

A multidimensional cloud computing architecture is designed and a multidimensional cloud resource scheduling model is constructed based on the stakeholder perspective of cloud users and cloud service providers to meet the high QoS requirements of cloud users (such as task execution time and task completion time) with low computing costs (such as energy consumption,economic costs and system availability).For the second-level cloud resource scheduling,an MQoS cloud resource scheduling algorithm based on multiple Greedy algorithm is proposed.The experimental results show that under the four cloud computing application scenarios with no aftereffects,the MQoS cloud resource scheduling algorithm has an overall increase of 206.42%~228.99% and 34.26%~56.93 in terms of multidimensional QoS degree compared with FIFO and M2EC algorithms.It has an average overall reduction of 0.48~0.49 and 0.20~0.27 in terms of cloud data center load balance difference.     

一种基于风险感知策略的多节点任务调度方法

为解决无人机(UAV)集群任务调度时面临各节点动态、不稳定的情况,该文提出一种面向多计算节点的可尽量避免任务中断且具有容错性的任务调度方法。该方法首先为基于多计算节点构建了一个以最小化任务平均完成时间为优化目标的任务分配策略;然后基于任务的完成时间和边缘计算节点的存留时间两者的概率分布,将任务计算节点上的执行风险量化成额外开销时间;最后以任务的完成时间与额外开销时间之和替换原本的完成时间,设计了风险感知的任务分配策略。在仿真环境下将该文提出的任务调度方法与3种基准调度方法进行了对比实验,实验结果表明该方法能够有效地降低任务平均响应时间、任务平均执行次数以及任务截止时间错失率。证明该文提出的方法降低了任务重调度和重新执行带来的额外开销,可实现分布式协同计算任务的调度工作,为复杂场景下的无人机集群网络提供新的技术支持。     

边缘计算多约束可信协同任务迁移策略

为保障边缘计算的服务质量,提出一种在多约束条件下边缘计算可信协同任务迁移策略。该策略基于任务需求,由边缘计算协同服务盟主节点组织调度协同服务盟员,基于用户任务迁移的K维权重指标,确定协同盟员调度优先级,以盟员负载均衡性为适应函数,通过贪心算法执行盟员任务分配与调度,基于路由捎带选择备用节点,通过迁移优先级评估,实现协同服务异常时的调度和迁移,由此提高边缘计算任务迁移的服务质量,保障任务迁移的可靠性。仿真实验表明,该机制能有效完成协同任务分发与迁移调度,提高边缘计算协同效率,保障网络服务质量。     

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.     

Deadline based scheduling for data-intensive applications in clouds

Cloud computing emerges as a new computing pattern that can provide elastic services for any users around the world. It provides good chances to solve large scale scientific problems with fewer efforts. Application deployment remains an important issue in clouds. Appropriate scheduling mechanisms can shorten the total completion time of an application and therefore improve the quality of service (QoS) for cloud users. Unlike current scheduling algorithms which mostly focus on single task allocation, we propose a deadline based scheduling approach for data-intensive applications in clouds. It does not simply consider the total completion time of an application as the sum of all its subtasks’ completion time. Not only the computation capacity of virtual machine (VM) is considered, but also the communication delay and data access latencies are taken into account. Simulations show that our proposed approach has a decided advantage over the two other algorithms.     

边缘算力网络中智能算力感知路由分配策略研究

增强现实、自动驾驶、智慧城市、工业互联网等新型业务应用对网络算力的需求逐渐增强,然而,边缘算力网络系统面临着网络共存的问题——负载不均衡,导致一部分边缘服务器无法满足业务应用的处理需求,另一部分边缘服务器的算力资源处于空闲状态.为了高效协同地感知利用泛在、异构的算力资源,提升6G通信网络的内生感知和算力自适应能力,急需...