Hierarchical scheduling strategies for parallel tasks and advance reservations in grids

Recently, the advance reservation functionality gained high importance in grids due to increasing popularity of modern applications that require interactive tasks, co-allocation of multiple resources, and performance guarantees. However, simultaneous scheduling, both advance reservations and batch tasks affects the performance. Advance reservations significantly deteriorate flow time of batch tasks and the overall resource utilization, especially in hierarchical scheduling structures. This is a consequence of unknown batch task processing times and the lack of possibility of altering allocations of advance reservations. To address these issues we present a common model for scheduling both computational batch tasks and tasks with advance reservation requests. We propose simple on-line scheduling policies and generic advices that reduce negative impact of advance reservations on a schedule quality. We also propose novel data structures and algorithms for efficient scheduling of advance reservations. A comprehensive experimental analysis is presented to show the influence of advance reservations on resource utilization, mean flow time, and mean tardiness—the criteria significant for administrators, users submitting batch tasks, and users requesting advance reservations, respectively. All experiments were performed with a well-known real workload using the GSSIM simulator.     

Scheduling in and out forests in the presence of communicationdelays

We consider the problem of scheduling tasks on multiprocessor architectures in the presence of communication delays. Given a set of dependent tasks, the scheduling problem is to allocate the tasks to processors such that the pre-specified precedence constraints among the tasks are obeyed and certain cost-measures (such as the computation time) are minimized. Several cases of the scheduling problem have been proven to be NP-complete. Nevertheless, there are polynomial time algorithms for interesting special cases of the general scheduling problem. Most of these results, however, do not take into consideration the delays due to message passing among processors. In this paper we study the increase in time complexity of scheduling problems due to the introduction of communication delays. In particular, we address the open problem of scheduling Out-forests (In-forests) in a multiprocessor system of m identical processors when communication delays are considered. The corresponding problem of scheduling Out-forests (In-forests) without communication delays admits an elegant polynomial time solution as presented first by Hu in 1961; however, the problem in the presence of communication delays has remained unsolved. We present here first known polynomial time algorithms for the computation of the optimal schedule when the number of available processors is given and bounded and both computation and communication delays are assumed to take one unit of time. Furthermore, we present a linear-time algorithm for computing a near-optimal schedule for unit-delay out-forests. The schedule's length exceeds the optimum by no more than (m-2) time units, where m is the number of processors. Hence for two processors the computed schedule is strictly optimum     

An Energy-Efficient Resource Allocation Scheme for Mobile Ad Hoc Computational Grids

Due to recent advancements in mobile computing and communication technologies, mobile ad hoc computational Grids are emerging as a new computing paradigm, enabling innovative applications through sharing of computing resources among mobile devices without any pre-existing network infrastructure. Energy-efficient resource allocation is one of the key issues in mobile ad hoc computational Grids due to limited battery life of mobile nodes. To reduce energy consumption, we propose a hybrid power-based resource allocation scheme for allocation of interdependent tasks to nodes within mobile ad hoc computational Grid. The basic idea is to exploit dependencies and task type, and allocate interdependent tasks to nodes accessible at minimum transmission power. We also propose a power-based algorithm to search a group of closest nodes to allocate a set of interdependent tasks. Compared to traditional algorithms, complexity of proposed algorithm depends on number of transmission power levels rather than number of nodes within a Grid. The scheme is validated in a simulation environment using various workloads and parameters.     

Routing and scheduling connections in networks that support advance reservations

A key problem in networks that support advance reservations is the routing and time scheduling of connections with flexible starting time and known data transfer size. In this paper we present a multicost routing and scheduling algorithm for selecting the path to be followed by such a connection and the time the data should start and end transmission at each link so as to minimize the reception time at the destination, or optimize some other performance criterion. The utilization profiles of the network links, the link propagation delays, and the parameters of the connection to be scheduled form the inputs to the algorithm. We initially present a scheme of non-polynomial complexity to compute a set of so-called non-dominated candidate paths, from which the optimal path can be found. We then propose two mechanisms to appropriately prune the set of candidate paths in order to find multicost routing and scheduling algorithms of polynomial complexity. We examine the performance of the algorithms in the special case of an Optical Burst Switched network. Our results indicate that the proposed polynomial-time algorithms have performance that is very close to that of the optimal algorithm. We also study the effects network propagation delays and link-state update policies have on performance.     

一种基于预测的资源匹配算法

网格任务调度过程中的资源匹配是根据任务要求从网格资源信息服务（GRIS）中查找出合适资源的过程。GRIS中记录的往往是资源的静态信息，由于本地负载的动态变化使得基于资源静态信息来确定的候选资源集中一些资源并不能满足任务的QoS需求。基于相关资源动态信息预测资源未来状态，给出了网格任务平均完成时间及完成时间的分布函数，并根据任务QoS需求，兼顾考虑资源当前及未来状态，提出了一种资源匹配模型与匹配算法。通过实验表明，该算法能有效减少候选资源数目，从而降低调度时间复杂度。     

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.     

基于网格资源超图模型的可信任务调度

网格任务调度是当前重要的研究领域。网格环境具有动态性、异构性等特点,网格资源的处理性能和稳定性都是影响到任务调度顺利完成的重要因素。为了获得更小的任务完成时间,该文根据网格环境的特点,建立了网格资源超图模型,在该模型基础上对资源按性能进行聚类,并提出一种可信任务调度算法GRHTS。模拟实验结果表明,该基于网格资源超图模型的可信任务调度算法优于同类算法,是一种有效的网格任务调度算法。     

网格资源超图划分聚类任务调度

网格计算是当前一个活跃的研究领域,其中任务调度是实现网格计算目标的一个重要部分.为获得良好的网格任务调度性能,提出了一种基于资源超图划分聚类的网格任务调度算法RHPC.该算法根据网格环境下资源数量庞大、异构、多样的特点,在构建的网格资源超图模型基础上,预先对资源进行性能划分聚类,将任务与聚类资源相匹配并实施调度.模拟实验结果证明算法缩短了任务资源相匹配的时间,提高了任务调度的性能,是一种有效的网格任务调度算法.     

基于遗传算法的网格任务调度方法分析

网格任务调度为多项式复杂程度的非确定性问题,其中所有非确定性多项式时间可解的判定问题,共同构成了NP类问题。如何快速地找到全局最优解是网格任务调度的难点所在。而遗传算法在验证猜测的正确性方面,具有自动获取和快速搜索的特性,是解决非线性问题的最优方案。本文主要对基于遗传算法的网格任务调度方法进行分析,通过网格任务调度模型构建、资源分配等操作,来完成遗传算法的仿真实验研究。     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

基于网格的任务调度与资源分配有效机制的研究

为实现QoS路由技术,提高网格的服务质量,本文定义了网格服务中任务调度的通信开销,给出了QoS路由树的生成原则,提出网格堆排序算法和QoS路由选择算法,利用算法实现了网格的任务调度与分配机制的设计.实验证明本设计能提高网格资源管理的效率.     

一种网格环境下的资源协同调度算法*

基于传统的DAGs调度算法,提出了一种适于网格环境下使用的协同调度算法,目标是使得一组应用程序的整体执行时间最小,并且算法中提供了对资源的提前预约的支持。模拟结果表明,该算法与传统的调度算法相比,性能有了较大的提高 。     

An efficient dynamic scheduling algorithm for multiprocessorreal-time systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. In this paper, we propose an efficient algorithm for nonpreemptive scheduling of dynamically arriving real-time tasks (aperiodic tasks) in multiprocessor systems. A real-time task is characterized by its deadline, resource requirements, and worst case computation time on p processors, where p is the degree of parallelization of the task. We use this parallelism in tasks to meet their deadlines and, thus, obtain better schedulability compared to nonparallelizable task scheduling algorithms. To study the effectiveness of the proposed scheduling algorithm, we have conducted extensive simulation studies and compared its performance with the myopic scheduling algorithm. The simulation studies show that the schedulability of the proposed algorithm is always higher than that of the myopic algorithm for a wide variety of task parameters     

网格资源调度研究

在介绍网格资源管理的基础上,针对网格中的资源调度问题,分析和总结常见的三类资源调度策略;并结合应用任务的类型分析了面向应用的资源调度策略,探讨网格对不同类型任务的调度支持,着重分析协作型任务的调度问题。将协作型任务通过BPEL4WS规范描述后,分解为多个可以并发或串行执行的子任务,然后进行调度。基于此,提出了一个新的协作型任务调度方法。     

数据和计算密集混合元任务的网格调度算法

网格计算技术是继Internet计算之后出现的新兴研究领域。网格系统由异构的资源组成，一个好的任务调度方法可以充分利用网格系统的处理能力，减少任务的完成时间。根据目前网格系统的使用模式，提出了符合实际的用户任务形式，即任务由数据传输和计算两部分组成，计算在获得所有输入之后开始执行。多个这样的独立任务组成元任务，作为调度程序的最小执行单位。在实际应用中，元任务应该由数据密集型和计算密集型任务混合组成。考虑到数据传输和计算的比例关系对元任务完成的影响，提出一种新的调度算法TCR，通过提高计算资源的利用率以及任务间的并行度，减少元任务的完成时间。详细介绍了该算法，并通过模拟结果的对比验证了该算法的良好性能。     

基于任务复制的网格任务调度算法

网格中资源之间存在着通信延迟，通过任务复制的冗余，可以减少任务之间的通信开销，缩短整个计算程序的计算时间。目前网格中的任务调度算法基本上是没有考虑任务复制的；而基于任务复制调度算法往往会产生过多的复制任务，增大系统开销，甚至有可能延迟计算时间。由于基于任务复制的任务调度是一个NP问题，因此本文提出了一种基于任务复制的网格资源调度算法，以减少调度长度为主要目标、减少任务复制量和资源占用量为次要目标。该算法在调度长度和任务复制数量以及占用资源数量方面都等于或优于其它算法。     

New Algorithms for Resource Reclaiming from Precedence Constrained Tasks in Multiprocessor Real-Time Systems

The scheduling of tasks in multiprocessor real-time systems has attracted many researchers in the recent past. Tasks in these systems have deadlines to be met, and most of the real-time scheduling algorithms use worst case computation times to schedule these tasks. Many resources will be left unused if the tasks are dispatched purely based on the schedule produced by these scheduling algorithms, since most of the tasks will take less time to execute than their respective worst case computation times. Resource reclaiming refers to the problem of reclaiming the resources left unused by a real-time task when it takes less time to execute than its worst case computation time. In this paper, we propose two algorithms to reclaim these resources from real-time tasks that are constrained by precedence relations and resource requirements, in shared memory multiprocessor systems. We introduce a notion called a restriction vector for each task which captures its resource and precedence constraints with other tasks. This will help not only in the efficient implementation of the algorithms, but also in obtaining an improvement in performance over the reclaiming algorithms proposed in earlier work [[2]]. We compare our resource reclaiming algorithms with the earlier algorithms and, by experimental studies, show that they reclaim more resources, thereby increasing the guarantee ratio (the ratio of the number of tasks guaranteed to meet their deadlines to the number of tasks that have arrived), which is the basic requirement of any resource reclaiming algorithm. From our simulation studies, we demonstrate that complex reclaiming algorithms with high reclaiming overheads do not lead to an improvement in the guarantee ratio.     

Scheduling real-time divisible loads with advance reservations

Providing QoS and performance guarantee to arbitrarily divisible loads has become a significant problem for many cluster-based research computing facilities. While progress is being made in scheduling arbitrarily divisible loads, previous approaches have no support for advance reservations. However, with the emergence of Grid applications that require simultaneous access to multi-site resources, supporting advance reservations in a cluster has become increasingly important. In this paper we propose a new real-time divisible load scheduling algorithm that supports advance reservations in a cluster. The impact of advance reservations on system performance is systematically studied. Simulation results show that, with the proposed algorithm and appropriate advance reservations, the system performance could be maintained at the same level as the no reservation case. Thus, Our approach enforces the real-time agreement vis-a-vis addresses the under-utilization concerns.     

Improving scheduling of tasks in a heterogeneous environment

Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. We introduce a task duplication-based scheduling algorithm for network of heterogeneous systems (TANH), with complexity O(V/sup 2/), which provides optimal results for applications represented by directed acyclic graphs (DAGs), provided a simple set of conditions on task computation and network communication time could be satisfied. The performance of the algorithm is illustrated by comparing the scheduling time with an existing "best imaginary level scheduling (BIL)" scheme for heterogeneous systems. The scalability for a higher or lower number of processors, as per their availability is also discussed. We have shown to provide substantial improvement over existing work on the task duplication-based scheduling algorithm (TDS).