树型网格计算环境下的独立任务调度

任务调度是实现高性能网格计算的一个基本问题,然而,设计和实现高效的调度算法是非常具有挑战性的.讨论了在网格资源计算能力和网络通信速度异构的树型计算网格环境下,独立任务的调度问题.与实现最小化任务总的执行时间不同(该问题已被证明是NP难题),为该任务调度问题建立了整数线性规划模型,并从该线性规划模型中得到最优任务分配方案??各计算节点最优任务分配数.然后,基于最优任务分配方案,构造了两种动态的需求驱动的任务分配启发式算法:OPCHATA(optimization-based priority-computation heuristic algorithm for task allocation)和OPBHATA(optimization-basedpriority-bandwidth heuristic algorithm for task allocation).实验结果表明:在异构的树型计算网格环境下实现大量独立任务调度时,该算法的性能明显优于其他算法.     

一种基于QoS的自适应网格失效检测器

失效检测器是构建可靠的网格计算环境所必需的基础组件之一.由于网格中存在大量对失效检测有着不同QoS需求的分布式应用,对于一个网格失效检测器来说,为保持其有效性和可扩展性,应该既能够准确提供应用程序所需的失效检测QoS,又能够避免为满足不同QoS而设计多套失效检测器所产生的多余负载.基于QoS基本评价指标,采用PULL模式主动检测策略实现了一种新的失效检测器--GA-FD(adaptive failure detector for grid),可以同时支持多个应用程序定量描述的QoS需求,不需要关于消息行为和时钟同步的任何假设.同时,证明了GA-FD在部分同步模型下可实现一个◇P类的失效检测器,并给出了相应的实验及数据.     

基于聚类和改进共生演算法的云任务调度策略

针对云计算环境中一些基于服务质量（QoS）调度算法存在寻优速度慢、调度成本与用户满意度不均衡的问题,提出了一种基于聚类和改进共生演算法的云任务调度策略。首先将任务和资源进行模糊聚类并对资源进行重排序放置,依据属性相似度对任务进行指导分配,减小对资源的选择范围;然后依据交叉和旋转学习机制改进共生演算法,提升算法的搜索能力;最后通过加权求和方式构造驱动模型,均衡调度代价与系统性能间关系。通过不同任务量的云任务调度仿真实验,表明该算法相比改进遗传算法、混合粒子群遗传算法和离散共生演算法,有效减少了进化代数,降低了调度成本并提升了用户满意度,是一种可行有效的任务调度算法。     

移动边缘计算中基于改进拍卖模型的计算卸载策略

随着移动互联网业务的快速发展,增强现实、虚拟现实、超清视频等手机应用逐渐普及、IoT应用不断涌现,计算能力和续航能力的不足成为限制智能终端设备成功支撑这些应用的主要瓶颈。针对这一现状,采用计算卸载的方式解决该问题,在多用户多移动边缘服务器的场景下,综合考虑智能设备性能和服务器资源提出了一种基于改进拍卖算法的计算卸载策略。该策略主要包括两个阶段,在卸载决策阶段,通过综合考虑计算任务自身大小、计算需求和服务器计算能力、网络带宽等因素提出了卸载决策的依据;在任务调度阶段,通过综合考虑计算任务的时间需求和MEC服务器计算性能提出了基于改进拍卖算法的任务调度模型。实验证明,提出的计算卸载策略能够有效地降低服务时延,减少智能设备能耗,改善用户体验。     

A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow

In this paper, a rotary chaotic particle swarm optimization (RCPSO) algorithm is presented to solve trustworthy scheduling of a grid workflow. In general, the grid workflow scheduling is a complex optimization problem which requires considering various scheduling criteria so as to meet a wide range of QoS requirements from users. Traditional researches into grid workflow scheduling mainly focus on the optimization constrained by time and cost. The key requirements for reliability, availability and security are not considered adequately. The main contribution of this study is to propose a new approach for trustworthy workflow scheduling in a large-scale grid with rich service resources, and present the RCPSO algorithm to optimize the scheduling performance in a multi-dimensional complex space. Experiments were done in two grid applications with at most 120 candidate services supplied to each task of various workflows. The results show better performance of the RCPSO in solving trustworthy scheduling of grid workflow problems as compared to GA, ACO and other recent variants of PSO.     

网格计算中任务调度算法的研究和改进

任务调度一直是网格计算中的热点问题,任务调度的目的是最优地分配任务,实现最佳的调度策略,以高效地完成计算任务。在网格环境中,资源的合理有效利用是实现任务调度的关键问题之一。本文首先论述静态任务调度算法和动态任务算法的原理和优缺点等,然后结合Min-min、Max-min算法的优点设计一种新的调度算法SA-MM,根据资源的使用情况自适应调度相应算法进行任务到资源的映射。最后,用GridSim模拟工具对网格计算中Min-min、Max-min和SA-MM任务调度算法进行仿真实验,分析和比较它们的调度长度(MakeSpan)和资源负载情况等影响任务调度效率的指标。     

基于延迟敏感应用的边云协同方案

智能设备存在着存储能力以及计算能力不足的问题,导致无法满足计算密集型和延迟敏感型应用的服务质量要求。边缘计算和云计算被认为是解决智能设备局限性的有效方法。为了有效利用边云资源,并在延迟和服务失败概率方面提供良好的服务质量,首先提出了一种三层计算系统框架,然后考虑到边缘服务器的异构性和任务的延迟敏感性,在边缘层提出了一种高效的资源调度策略。三层计算系统框架可以根据应用程序的延迟敏感性提供计算资源和传输时延,保证了边缘资源的有效利用以及任务的实时性。仿真结果验证了所提资源调度策略的有效性,并表明该调度算法优于现有传统方法。     

基于动态能量感知的云计算任务调度模型

针对传统云计算任务调度模型出现的计算量大、能耗高、效率低、调配精度差等问题,基于动态能量感知设计了一种新的云计算任务调度模型;以动态能量感知为基础,选取资源分配服务器的中央处理器的使用率、存储器的占用率、控制器的负载率等3个参数,构建三维云计算任务节点投影空间,将上述参数向量投影到空间中;引入动态能量感知建立云计算任务调度模型,采用虚拟技术将多个服务器合并成一台服务器,对调度任务进行需求分析和分类,采用能量感知算法将待调度任务分配给满足调度需求的虚拟资源,将任务调度到服务器资源上,实现任务调度;实验结果表明,基于动态能量感知的云计算任务调度模型在从小任务集和大任务集两个角度都能给有效缩短调度时间,降低调度能耗。     

一种基于综合匹配度的边缘计算系统任务调度方法

边缘计算模式满足数据的实时和低功耗处理需求,是缓解当前网络数据洪流实时处理问题的有效方法之一.但边缘设备资源的异构与多样性给任务的调度与迁移带来极大的困难与挑战.目前,边缘计算任务调度研究主要集中在调度算法的设计与仿真,这些算法和模型通常忽略了边缘设备的异构性和边缘任务的多样性,不能使多样化的边缘任务与异构的资源能力深...     

Scheduling online mixed-parallel workflows of rigid tasks in heterogeneous multi-cluster environments

Workflow scheduling on parallel systems has long been known to be a NP-complete problem. As modern grid and cloud computing platforms emerge, it becomes indispensable to schedule mixed-parallel workflows in an online manner in a speed-heterogeneous multi-cluster environment. However, most existing scheduling algorithms were not developed for online mixed-parallel workflows of rigid data-parallel tasks and multi-cluster environments, therefore they cannot handle the problem efficiently. In this paper, we propose a scheduling framework, named Mixed-Parallel Online Workflow Scheduling (MOWS), which divides the entire scheduling process into four phases: task prioritizing, waiting queue scheduling, task rearrangement, and task allocation. Based on this framework, we developed four new methods: shortest-workflow-first, priority-based backfilling, preemptive task execution and All-EFT task allocation, for scheduling online mixed-parallel workflows of rigid tasks in speed-heterogeneous multi-cluster environments. To evaluate the proposed scheduling methods, we conducted a series of simulation studies and made comparisons with previously proposed approaches in the literature. The experimental results indicate that each of the four proposed methods outperforms existing approaches significantly and all these approaches in MOWS together can achieve more than 20% performance improvement in terms of average turnaround time.     

Task-resource scheduling problem

Cloud computing is a new and rapidly emerging computing paradigm where applications,data and IT services are provided over the Internet.The task-resource management is the key role in cloud computing systems.Task-resource scheduling problems are premier which relate to the efficiency of the whole cloud computing facilities.Task-resource scheduling problem is NPcomplete.In this paper,we consider an approach to solve this problem optimally.This approach is based on constructing a logical model for the problem.Using this model,we can apply algorithms for the satisfiability problem(SAT) to solve the task-resource scheduling problem.Also,this model allows us to create a testbed for particle swarm optimization algorithms for scheduling workflows.     

Caching-based task scheduling for edge computing in intelligent manufacturing

Tasks have high requirements for response delay and security in intelligent manufacturing. Industrial data have the characteristics of high privacy. However, cloud services are difficult to implement for low latency-sensitive applications and privacy data tasks. Therefore, the offloading technology in edge computing can offload the computing tasks of terminal devices to the edge of the network, which can effectively reduce the delay and match the needs of intelligent manufacturing. Unreasonable task scheduling cannot meet the needs of real-time scheduling between edge servers and cloud servers. In this paper, we establish a joint low-delay optimization model of task scheduling and dynamic replacement-release caching (DRRC) mechanism, which couples a privacy selection strategy for tasks to protect privacy. Tasks are scheduled to different location by the privacy of sensitive data, which can improve the security of data and meet the calculation request of different tasks. DRRC mechanism caches tasks according to the size of the task and replaces it with the weight of the task data, and adds automatic release mechanism. To solve the task scheduling strategy, we design the improved genetic-differential evolution algorithm. Extensive simulations reveal that the proposed algorithm has a better performance in minimizing latency compared with other scheduling algorithms. At the same time, the caching mechanism has a better hit rate.

     

基于分批优化的实时多处理器系统的集成动态调度算法

提出了一种基于分批优化的实时多处理器系统的集成动态调度算法，该算法采用在每次扩充当前局部调度时，通过对所选取的一批任务进行优化分配的策略以及软实时任务的服务质量QoS（quality of service）降级策略，以统一方式实现了对实时多处理器糸统中硬、软实时任务的集成动态调度．进行了大量的模拟研究，结果表明．在多种任务参数取值下，新算法的调度成功率均高于近视算法（Myopic Algorithm）．     

实时异构系统的动态分批优化调度算法

提出了一种实时异构系统的动态分批优化调度算法,该算法采用的是在每次扩充当前局部调度时,按一定规则在待调度的任务集中选取一批任务,对该批任务中的每项任务在每个处理器上的运行综合各种因素构造目标函数,将问题转化为非平衡分配问题,一次性为这些任务都分配一个处理器或为每个处理器分配一项任务,使得这种分配具有最好的“合适性”,以增大未被调度任务的可行性.这种方法有效地提高了算法调度成功率.同时,为了评估该算法的性能,对其进行了大量的模拟,分析了一些任务参数的变化对算法调度成功率的影响,并与老算法的调度成功率进行了比较.模拟结果显示,新算法优于老算法.     

Processing capacity-based decision mechanism edge computing model for IoT applications

The handling of complex tasks in IoT applications becomes difficult due to the limited availability of resources in most IoT devices. There arises a need to offload the IoT tasks with huge processing and storage to resource enriched edge and cloud. In edge computing, factors such as arrival rate, nature and size of task, network conditions, platform differences and energy consumption of IoT end devices impacts in deciding an optimal offloading mechanism. A model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity. This dynamic decision is proposed as processing capacity-based decision mechanism (PCDM) which takes the offloading decisions on new tasks by scheduling all the available devices based on processing capacity. The target devices are then selected for task execution with respect to energy consumption, task size and network time. PCDM is developed in the EDGECloudSim simulator for four different applications from various categories such as time sensitiveness, smaller in size and less energy consumption. The PCDM offloading methodology is experimented through simulations to compare with multi-criteria decision support mechanism for IoT offloading (MEDICI). Strategies based on task weightage termed as PCDM-AI, PCDM-SI, PCDM-AN, and PCDM-SN are developed and compared against the five baseline existing strategies namely IoT-P, Edge-P, Cloud-P, Random-P, and Probabilistic-P. These nine strategies are again developed using MEDICI with the same parameters of PCDM. Finally, all the approaches using PCDM and MEDICI are compared against each other for four different applications. From the simulation results, it is inferred that every application has unique approach performing better in terms of response time, total task execution, energy consumption of device, and total energy consumption of applications.     

负载敏感的云任务三支聚类评分调度研究

在云计算商业化的服务模式中,追求服务质量、负载均衡与经济原则的多目标优化调度。针对集群资源使用率偏低的现象,提出了三支聚类评分（three-way clustering weight,TWCW）算法,首先分析云任务的多样化需求与资源的动态特性,采用三支聚类算法对任务集合聚类划分,然后结合任务属性对类簇对象进行评分调度。基于Cloudsim实验模拟表明:相比于k-means与FCM聚类调度,三支聚类评分算法（TWCW）在任务平均响应时间与资源利用率等方面均有显著提升。     

Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm

Computational Grids and peer‐to‐peer (P2P) networks enable the sharing, selection, and aggregation of geographically distributed resources for solving large‐scale problems in science, engineering, and commerce. The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. We have proposed a computational economy framework for regulating the supply of and demand for resources and allocating them for applications based on the users' quality‐of‐service requirements. The framework requires economy‐driven deadline‐ and budget‐constrained (DBC) scheduling algorithms for allocating resources to application jobs in such a way that the users' requirements are met. In this paper, we propose a new scheduling algorithm, called the DBC cost–time optimization scheduling algorithm, that aims not only to optimize cost, but also time when possible. The performance of the cost–time optimization scheduling algorithm has been evaluated through extensive simulation and empirical studies for deploying parameter sweep applications on global Grids. Copyright © 2005 John Wiley & Sons, Ltd.     

Static scheduling of multiple workflows with soft deadlines in non-dedicated heterogeneous environments

Typical patterns of using scientific workflows include their periodical executions using a fixed set of computational resources. Using the statistics from multiple runs, one can accurately estimate task execution and communication times to apply static scheduling algorithms. Several workflows with known estimates could be combined into a set to improve the resulting schedule. In this paper, we consider the mapping of multiple workflows to partially available heterogeneous resources. The problem is how to fill free time windows with tasks from different workflows, taking into account users’ requirements of the urgency of the results of calculations. To estimate quality of schedules for several workflows with various soft deadlines, we introduce the unified metric incorporating levels of meeting constraints and fairness of resource distribution.The main goal of the work was to develop a set of algorithms implementing different scheduling strategies for multiple workflows with soft deadlines in a non-dedicated environment, and to perform a comparative analysis of these strategies. We study how time restrictions (given by resource providers and users) influence the quality of schedules, and which scheme of grouping and ordering the tasks is the most effective for the batched scheduling of non-urgent workflows. Experiments with several types of synthetic and domain-specific sets of multiple workflows show that: (i) the use of information about time windows and deadlines leads to the significant increase of the quality of static schedules, (ii) the clustering-based scheduling scheme outperforms task-based and workflow-based schemes. This was confirmed by an evaluation of studied algorithms on a basis of the CLAVIRE workflow management platform.     

基于动态遗传算法的网格计算任务调度

针对网格任务调度的动态特性,提出一种改进的遗传算法——动态遗传算法(DGA),设计了新的编码机制和适应度函数,以及相应的选择、交叉和变异算子。根据网格系统各服务节点的计算能力、负载及网络状态进行动态调度,不仅使总的完成时间最短,尽量使主机的空闲时间最短,同时满足每个任务的截止时间的要求。在OPNET环境中构建了一个局部网格仿真模型,对所提出的动态遗传算法进行了仿真实验,并与其他常见网格任务调度算法进行了对比,结果表明动态遗传算法具有很好的优化能力,提供了较好的服务质量。     

一种多QoS目标约束的云计算任务调度策略

针对云计算环境下大量用户任务请求各异的服务质量（Quality of Service, QoS）调度目标要求,通过综合考虑云用户任务的截止时间底线、调度预算等QoS目标约束条件以及各类可用资源的性能参数,对任务调度的多QoS目标约束条件进行形式化建模,基于构造的隶属度函数将多QoS目标约束的优化求解问题转化成一个单目标约束的优化问题,对转化后的单目标约束优化问题进行近似求解,最终提出一种多QoS目标约束的云计算任务调度策略。在CloudSim模拟器上的仿真结果表明,提出的多QoS目标约束的云计算任务调度策略总体上优于传统的Min-min算法以及改进的以QoS为导向的Min-min算法。