Load‐balanced and locality‐aware scheduling for data‐intensive workloads at extreme scales

Data‐driven programming models such as many‐task computing (MTC) have been prevalent for running data‐intensive scientific applications. MTC applies over‐decomposition to enable distributed scheduling. To achieve extreme scalability, MTC proposes a fully distributed task scheduling architecture that employs as many schedulers as the compute nodes to make scheduling decisions. Achieving distributed load balancing and best exploiting data locality are two important goals for the best performance of distributed scheduling of data‐intensive applications. Our previous research proposed a data‐aware work‐stealing technique to optimize both load balancing and data locality by using both dedicated and shared task ready queues in each scheduler. Tasks were organized in queues based on the input data size and location. Distributed key‐value store was applied to manage task metadata. We implemented the technique in MATRIX, a distributed MTC task execution framework. In this work, we devise an analytical suboptimal upper bound of the proposed technique, compare MATRIX with other scheduling systems, and explore the scalability of the technique at extreme scales. Results show that the technique is not only scalable but can achieve performance within 15% of the suboptimal solution. Copyright © 2015 John Wiley & Sons, Ltd.     

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.     

基于图匹配的多处理机调度算法

本文对具有高通讯延迟的多处理机系统（机群系统）上的任务调度算法进行了研究，与以往算法主要考虑任务图的关键路径不同，本文给出了任务图的调度与其偶图匹配的对应关系，并由此提出了一种新的启发式算法，通过模拟试验显示本算法具有较好的调度效果。     

基于数据交换与同步的作业调度方案

多机作业调度问题是一个经典的NP难问题,在应用中由于实际需要,会出现各种约束和变形,调度问题的研究成果决定着系统的性能.DataTurbo是作者参与的一个用于解决分布式数据迁移、集成和融合的平台,该平台承担着大数据量的分布式传输任务.在DataTurbo平台基础上,提出一种适用于数据交换与同步的分布式作业调度方案,并构建一个灵活的分布式调度算法框架,解决相关的调度问题.该调度方案是一种在线的、可并发的、作业可分解的多机调度方案.仿真实验结果显示,该调度方案在任务负载大、调度点稀疏情况下优势明显,能适用于数据交换同步作业,可作为数据交换与同步作业的动态调度方案,并为相关启发式算法建立基础模型.     

基于蚁群算法的分布多处理机系统任务调度研究

本文主要基于现代蚁群算法讨论分布式系统调度。蚁群算法是一种构造型启发算法,在离散优化问题中得到广泛应用。分布式系统调度属于NP-hard,为了提高算法性能,把问题任务图的优先级作为启发信息。最后,采用随机产生的任务图将调度结果和模拟退火算法、遗传算法等进行了比较。     

A scheduling algorithm for applications in a cloud computing system with communication changes

This paper proposes a scheduling algorithm to solve the problem of task scheduling in a cloud computing system with time‐varying communication conditions. This algorithm converts the scheduling problem with communication changes into a directed acyclic graph (DAG) scheduling problem for existing fuzzy communication task nodes, that is, the scheduling problem for a communication‐change DAG (CC‐DAG). The CC‐DAG contains both computation task nodes and communication task nodes. First, this paper proposes a weighted time‐series network bandwidth model to solve the indefinite processing time (cost) problem for a fuzzy communication task node. This model can accurately predict the processing time of a fuzzy communication task node. Second, to address the scheduling order problem for the computation task nodes, a dynamic pre‐scheduling search strategy (DPSS) is proposed. This strategy computes the essential paths for the pre‐scheduling of the computation task nodes based on the actual computation costs (times) of the computation task nodes and the predicted processing costs (times) of the fuzzy communication task nodes during the scheduling process. The computation task node with the longest essential path is scheduled first because its completion time directly influences the completion time of the task graph. Finally, we demonstrate the proposed algorithm via simulation experiments. The experimental results show that the proposed DPSS produced remarkable performance improvement rate on the total execution time that ranges between 11.5% and 21.2%. In view of the experimental results, the proposed algorithm provides better quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than HEFT, PEFT, and CEFT algorithms.     

Computation and communication schedule optimization for data-sharing tasks on uniprocessor

Almost every computation task requires input data in order to find a solution. This is not a problem for a centralized system because data is usually available locally. However, in a parallel and distributed system, e.g., computation grids, the data may be in remote sites and must be transferred to the local site before the computation can proceed. As a result, the interleaved sequence of data transfer and job execution has a significant impact on the overall computational efficiency. In this paper, we analyze the computational complexity of the shared-data job scheduling problem on uniprocessor, with and without consideration of the storage capacity constraint on the local site.We show that if there is an upper bound on the server capacity, the problem is NP-complete, even when each job depends on at most two data items. For the case where there is no upper bound on the server capacity, we show that there exists an efficient algorithm that can provide an optimal job schedule when each job depends on at most two data items. We also propose an efficient heuristic algorithm that can determine good schedules for cases where there is no limit on the amount of data a job may access. The reported experiment results demonstrate that this heuristic algorithm performs very well, and derives near optimal solutions.     

基于混合思维进化计算的网格资源分配算法

分布式、异构的网格环境中独立计算任务的有效调度是一个关键问题。由于在这样的环境中找到一个最优的调度是一个NP难问题,通常运用各种启发式算法来找到近似最优解。本文将思维进化计算和禁忌搜索算法结合起来,充分发挥各自的优势,并用实验证明了运用混合思维进化计算进行网格资源分配的有效性。     

一种基于任务全局迁移的静态调度算法

任务调度是分布实时系统中的一个关键问题.TDS等典型算法在优化条件下可得到该问题调度长度上的最优解.但是TDS等算法在节点分配时存在节点选择范围和节点执行时间范围的局限,无法最小化算法所需处理器数目.任务全局迁移调度算法GTT（global task-transferring）在保证调度长度最优的前提下,从全局范围内选择并调度任务节点,有效利用了处理器,可最小化调度所需处理器数目.优化条件下对各种算法的调度实验表明,GTT算法在加速比和效率上比TDS等同类算法有显著提高.GTT算法的时间复杂度是O（d ｜ V｜^ 2）.这里｜V｜是DAG图中的节点数,d是图中各节点入度或出度的最大值.     

基于动态多处理节点的分布式系统任务调度

针对固定处理节点分布式系统动态调控能力弱的问题,给出一种分布式系统任务调度模型,讨论单处理节点任务调度问题,提出平均处理强度指标,用于更准确地刻画处理节点的承载能力。推导出动态多处理节点的任务分配方法,优化分布式系统中任务处理的时间响应特性。模拟实验证明,该算法有较好的动态调控能力,能根据需要降低处理器负载、改善任务处理延时并更合理地利用系统资源。     

An Adaptive Partitioning Scheme for Sleep Scheduling and Topology Control in Wireless Sensor Networks

This paper presents an adaptive partitioning scheme of sensor networks for node scheduling and topology control with the aim of reducing energy consumption. Our scheme partitions sensors into groups such that a connected backbone network can be maintained by keeping only one arbitrary node from each group in active status while putting others to sleep. Unlike previous approaches that partition nodes geographically, our scheme is based on the measured connectivity between pairwise nodes and does not depend on nodes' locations. In this paper, we formulate node scheduling with topology control as a constrained optimal graph partition problem, which is NP-hard, and propose a Connectivity-based Partition Approach (CPA), which is a distributed heuristic algorithm, to approximate a good solution. We also propose a probability-based CPA algorithm to further save energy. CPA can ensure K-vertex connectivity of the backbone network, which achieves the trade-off between saving energy and preserving network quality. Moreover, simulation results show that CPA outperforms other approaches in complex environments where the ideal radio propagation model does not hold.     

基于EFT遗传算法的分布式图像绘制

在分布式图像绘制中，为提高图像绘制速度，达到图像的实时绘制，缩短任务调度长度，提出了基于最早完成时问(EFT)启发的遗传算法，染色体编码采用问题属性作为基因。实验表明它对于解决异构网络平台下分布式任务调度具有很好的收敛速度，最佳的调度长度和调度方案。应用于分布式图像绘制可以取得良好的实时性和较佳的图像质量。     

Optimizing the Throughput of Data-Driven Peer-to-Peer Streaming

During recent years, the Internet has witnessed a rapid growth in deployment of data-driven (or swarming based) peer-to-peer (P2P) media streaming. In these applications, each node independently selects some other nodes as its neighbors (i.e. gossip-style overlay construction), and exchanges streaming data with the neighbors (i.e. data scheduling). To improve the performance of such protocol, many existing works focus on the gossip-style overlay construction issue. However, few of them concentrate on optimizing the streaming data scheduling to maximize the throughput of a constructed overlay. In this paper, we analytically study the scheduling problem in data-driven streaming system and model it as a classical min-cost network flow problem. We then propose both the global optimal scheduling scheme and distributed heuristic algorithm to optimize the system throughput. Furthermore, we introduce layered video coding into data-driven protocol and extend our algorithm to deal with the end-host heterogeneity. The results of simulation with the real world traces indicate that our distributed algorithm significantly outperforms conventional ad hoc scheduling strategies especially in stringent buffer and bandwidth constraints.     

异构系统中基于可用性的粒子群任务调度算法

任务调度技术是并行分布式系统中的关键技术之一,对系统的性能起着重要作用,但通常情况下大型系统的任务调度问题属于NP问题。而现代启发式生物进化算法是找出很多NP问题近似解的有效方法。本文将粒子群算法应用于基于可用性的网格系统调度中,提出了一种调度算法,对算法的性能进行了理论分析和模拟实验。结果表明:和最近文献中的基于可用性的调度算法SSAC相比,所提出的新算法在保证系统资源具有同样的可用性条件下,能够产生更好的调度长度。     

A heuristic algorithm for the distributed and flexible job-shop scheduling problem

The distributed manufacturing takes place in a multi-factory environment including several factories, which may be geographically distributed in different locations, or in a multi-cell environment including several independent manufacturing cells located in the same plant. Each factory/cell is capable of manufacturing a variety of product types. An important issue in dealing with the production in this decentralized manner is the scheduling of manufacturing operations of products (jobs) in the distributed manufacturing system. In this paper, we study the distributed and flexible job-shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. A fast heuristic algorithm based on a constructive procedure is developed to obtain good quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and promising for practical problems.     

基于通信和计算开销的网格任务调度算法的研究

在计算密集型的异构网格环境中,有效的任务调度是一个关键的问题,这是一个完全NP问题,针对这一问题提出了一种基于通信和计算开销的启发式网格任务调度算法,这一算法考虑了不同的节点计算能力、任务大小和网络带宽,最后给出了相应的实验及相关算法的比较结果,表明了该算法对于异构环境具有更优的性能。     

On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Load balanced transaction scheduling problem is an important issue in distributed computing environments including grid system. This problem is known to be NP-hard and can be solved by using heuristic as well as any meta-heuristic method. We ponder over the problem of the load balanced transaction scheduling in a grid processing system by using an Ant Colony Optimization for load balancing. The problem that we consider is to achieve good execution characteristics for a given set of transactions that has to be completed within their given deadline. We propose a transaction processing algorithm based on Ant Colony Optimization (ACO) for load balanced transaction scheduling. We modify two meta-heuristic along with ACO and three heuristic scheduling algorithms for the purpose of comparison with our proposed algorithm. The results of the comparison show that the proposed algorithm provides better results for the load balanced transaction scheduling in the grid processing system.     

一种优化MapReduce系统能耗的数据布局算法

在云计算技术和大数据技术的推动下,IT资源的规模不断扩大,其能耗问题日益显著.研究表明:节点资源利用率不高、资源空闲导致的能源浪费,是目前大规模分布式系统的主要问题之一.研究了MapReduce系统的能耗优化.传统的基于软件技术的能耗优化方法多采用负载集中和节点开关算法,但由于MapReduce任务的特点,集群节点不仅要完成运算,还需要存储数据,因此,传统方法难以应用到MapReduce集群.提出了良好的数据布局可以优化集群能耗.基于此,首先定义了数据布局的能耗优化目标,并提出相应的数据布局算法;接着,从理论上证明该算法能够实现数据布局的能耗优化目标;最后,在异构集群中部署3种数据布局不同的MapReduce系统,通过对比三者在执行CPU密集型、I/O密集型和交互型这3种典型运算时的集群能耗,验证了所提出的数据布局算法的能耗优化效果.理论和实验结果均表明,所提出的布局算法能够有效地降低MapReduce集群的能耗.上述工作都将促进高能耗计算和大数据分析的应用.     

Data dependent loop scheduling based on genetic algorithms for distributed and shared memory systems

Many approaches have been described for the parallel loop scheduling problem for shared-memory systems, but little work has been done on the data-dependent loop scheduling problem (nested loops with loop carried dependencies). In this paper, we propose a general model for the data-dependent loop scheduling problem on distributed as well as shared memory systems. In order to achieve load balancing and low runtime scheduling and communication overhead, our model is based on a loop task graph and the notion of critical path. In addition, we develop a heuristic algorithm based on our model and on genetic algorithms to test the reliability of the model. We test our approach on different scenarios and benchmarks. The results are very encouraging and suggest a future parallel compiler implementation based on our model.     

A new hybrid parallel genetic algorithm for the job‐shop scheduling problem

The job‐shop scheduling problem (JSSP) is considered one of the most difficult NP‐hard problems. Numerous studies in the past have shown that as exact methods for the problem solution are intractable, even for small problem sizes, efficient heuristic algorithms must achieve a good balance between the well‐known themes of exploitation and exploration of the vast search space. In this paper, we propose a new hybrid parallel genetic algorithm with specialized crossover and mutation operators utilizing path‐relinking concepts from combinatorial optimization approaches and tabu search in particular. The new scheme relies also on the recently introduced concepts of solution backbones for the JSSP in order to intensify the search in promising regions. We compare the resulting algorithm with a number of state‐of‐the‐art approaches for the JSSP on a number of well‐known test‐beds; the results indicate that our proposed genetic algorithm compares fairly well with some of the best‐performing genetic algorithms for the problem.