The impact of task service time variability on gang scheduling performance in a two-cluster system

Gang scheduling is a common task scheduling policy for parallel and distributed systems which combines elements of space-sharing and time-sharing. In this paper we present a migration strategy which reduces the fragmentation in the schedule caused by gang scheduled jobs. We consider the existence of high priority jobs in the workload. These jobs need to be started immediately and they may interrupt a parallel job’s execution. A distributed system consisting of two homogeneous clusters is simulated to evaluate the performance for various workloads. We study the impact on performance of the variability in service time of the parallel tasks. Our simulation results indicate that the proposed strategy can result in a significant performance gain and that the performance improvement depends on the variability of gang tasks’ service time.     

DAG Scheduling in Heterogeneous Computing and Grid Environments Using Variable Neighborhood Search Algorithm

DAG scheduling is a process that plans and supervises the execution of interdependent tasks on heterogeneous computing resources. Efficient task scheduling is one of the important factors to improve the performance of heterogeneous computing systems. In this paper, an investigation on implementing Variable Neighborhood Search (VNS) algorithm for scheduling dependent jobs on heterogeneous computing and grid environments is carried out. Hybrid Two PHase VNS (HTPHVNS) DAG scheduling algorithm has been proposed. The performance of the VNS and HTPHVNS algorithm has been evaluated with Genetic Algorithm and Heterogeneous Earliest Finish Time algorithm. Simulation results show that VNS and HTPHVNS algorithm generally perform better than other meta-heuristics methods.     

Communication cost effective scheduling policies of nonclairvoyant jobs with load balancing in a grid

Effective load distribution is of great importance at grids, which are complex heterogeneous distributed systems. In this paper we study site allocation scheduling of nonclairvoyant jobs in a 2-level heterogeneous grid architecture. Three scheduling policies at grid level which utilize site load information are examined. The aim is the reduction of site load information traffic, while at the same time mean response time of jobs and fairness in utilization between the heterogeneous sites are of great interest. A simulation model is used to evaluate performance under various conditions. Simulation results show that considerable decrement in site load information traffic and utilization fairness can be achieved at the expense of a slight increase in response time.     

Performance analysis of priority scheduling mechanisms under heterogeneous network traffic

Priority scheduling principle plays a crucial role in the Differentiated Services (DiffServ) architecture for the provisioning of Quality-of-Service (QoS) of network-based applications. Analytical modelling and performance evaluation of priority queuing systems have received significant attention and research efforts. However, most existing work has primarily focused on the analysis of priority queuing under either Short Range Dependent (SRD) or Long Range Dependent (LRD) traffic only. Recent studies have shown that realistic traffic reveals heterogeneous nature within modern multi-service networks. With the aim of investigating the impact of heterogeneous traffic on the design and performance of network-based systems, this paper proposes a novel analytical model for priority queuing systems subject to heterogeneous LRD self-similar and SRD Poisson traffic. The key contribution of the paper is to extend the application of the generalized Schilder's theorem (originally a large deviation principle for handling Gaussian processes only) to deal with heterogeneous traffic and further develop the analytical upper and lower bounds of the queue length distributions for individual traffic flows. The validity and accuracy of the model demonstrated through extensive comparisons between analytical bounds and simulation results make it a practical and cost-effective evaluation tool for investigating the performance behaviour of priority queuing systems under heterogeneous traffic with various parameter settings.     

EMPEROR: An OGSA Grid Meta-Scheduler Based on Dynamic Resource Predictions

Scheduling constitutes an integral feature of Grid computing infrastructures, being also a key to realizing several of the Grid promises. In particular, scheduling can maximize the resources available to end users, accelerate the execution of jobs, while also supporting scalable and autonomic management of the resources comprising a Grid. Grid scheduling functionality hinges on middleware components called meta-schedulers, which undertake to automatically distribute jobs across the dispersed heterogeneous resources of a Grid. In this paper we present the design and implementation of a Grid meta-scheduler, which we call EMPEROR. EMPEROR provides a framework for implementing scheduling algorithms based on performance criteria. In implementing a particular instantiation of this framework, we have devised models for predicting host load and memory resources, and accordingly for estimating the running time of a task. These models hinge on time series analysis techniques and take into account results of the cluster computing literature. Apart from incorporating these models, EMPEROR provides fully fledged Grid scheduling functionality, which complies with OGSA standards as the later are reflected in the Globus toolkit. Specifically, EMPEROR interfaces to Globus middleware services (i.e., GSI, MDS, GRAM) towards discovering resources, implementing the scheduling algorithm and ultimately submitting jobs to local scheduling systems. By and large, EMPEROR is one of the few standards based meta-schedulers making use of dynamic scheduling information.     

Adaptive hierarchical scheduling policy for enterprise grid computing systems

In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job scheduling systems for enterprise grid computing provide batch queuing support and focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present a hierarchical scheduling policy paying special attention to I/O and service-demands of parallel jobs in homogeneous and heterogeneous systems with background workload. The performance of the proposed scheduling policy is studied under various system and workload parameters through simulation. We also compare performance of the proposed policy with a static space–time sharing policy. The results show that the proposed policy performs substantially better than the static space–time sharing policy.     

List scheduling with duplication for heterogeneous computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous computing systems (HCS). However, finding an effective task schedule in HCS, requires the consideration of the heterogeneity of computation and communication. To solve this problem, we present a list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD). As task priority is a key attribute for list scheduling algorithm, this paper presents a new approach for computing their priority which considers the performance difference in target HCS using variance. Another novel idea proposed in this paper is to try to duplicate all parent tasks and get an optimal scheduling solution. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm HEFD significantly surpasses other three well-known algorithms.     

Scheduling multiple task graphs in heterogeneous distributed real-time systems by exploiting schedule holes with bin packing techniques

The most crucial aspect of distributed real-time systems is the scheduling algorithm, which must guarantee that every job in the system will meet its deadline. In this paper, we evaluate by simulation the performance of strategies for the dynamic scheduling of composite jobs in a heterogeneous distributed real-time system. Each job that arrives in the system is a directed acyclic graph of component tasks and has an end-to-end deadline. For each scheduling policy, we provide alternative versions which allow the insertion of tasks into idle time slots, using various bin packing techniques. The comparison study, based on different workloads and system heterogeneity levels, shows that the alternative versions of the algorithms outperform their respective counterparts.     

An adaptive job scheduling scheme for mesh-connected multicomputers

Allocating submeshes to jobs in mesh-connected multicomputers in an FCFS fashion leads to poor system performance because a large job at the head of the waiting queue can prevent the allocation of free submeshes to other smaller waiting jobs. However, serving jobs aggressively out-of-order can lead to excessive waiting delays for large jobs located at the head of the waiting queue. In this paper, we show that the ability of the job scheduling algorithm to bypass the head of the waiting queue should increase with the load, and we propose a scheduling scheme that can bypass the waiting queue head in a load-dependent adaptive fashion. Also, giving priority to large jobs because they are more difficult to accommodate is investigated. The performance of the proposed scheme has been compared to that of FCFS, aggressive out-of-order scheduling, and other previous job scheduling schemes. Extensive simulation results based on synthetic workloads and real workload traces indicate that our scheduling strategy is a good strategy when both average and maximum job waiting delays are considered. In particular, it is substantially superior to FCFS in terms of mean turnaround times, and to aggressive out-of-order scheduling in terms of maximum waiting delays.     

A group priority earliest deadline first scheduling algorithm

In most priority scheduling algorithms, the number of priority levels is assumed to be unlimited. However, if a task set requires more priority levels than the system can support, several jobs must in practice be assigned the same priority level. To solve this problem, a novel group priority earliest deadline first (GPEDF) scheduling algorithm is presented. In this algorithm, a schedulability test is given to form a job group, in which the jobs can arbitrarily change their order without reducing the schedulability. We consider jobs in the group having the same priority level and use shortest job first (SJF) to schedule the jobs in the group to improve the performance of the system. Compared with earliest deadline first (EDF), best effort (BE), and group-EDF (gEDF), simulation results show that the new algorithm exhibits the least switching, the shortest average response time, and the fewest required priority levels. It also has a higher success ratio than both EDF and gEDF.     

基于ECT的优先权约束作业调度策略研究

为了协调网格计算中异构资源在多用户之间的合理共享,满足不同用户需求,该文提出一种基于ECT的优先权约束作业调度策略。该策略充分考虑不同作业的期望完成时间,并通过为不同级别用户设置优先级,使得高优先权用户的作业优先执行,保证绝大多数作业在期望完成时间之内完成,同时平衡了各种资源的利用率。该策略解决了网格环境下不同类别用户无冲突共享资源问题,提高了用户满意程度,实现了作业与异构资源之间的合理匹配。     

A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters

In this paper, a heuristic dynamic scheduling scheme for parallel real-time jobs executing on a heterogeneous cluster is presented. In our system model, parallel real-time jobs, which are modeled by directed acyclic graphs, arrive at a heterogeneous cluster following a Poisson process. A job is said to be feasible if all its tasks meet their respective deadlines. The scheduling algorithm proposed in this paper takes reliability measures into account, thereby enhancing the reliability of heterogeneous clusters without any additional hardware cost. To make scheduling results more realistic and precise, we incorporate scheduling and dispatching times into the proposed scheduling approach. An admission control mechanism is in place so that parallel real-time jobs whose deadlines cannot be guaranteed are rejected by the system. For experimental performance study, we have considered a real world application as well as synthetic workloads. Simulation results show that compared with existing scheduling algorithms in the literature, our scheduling algorithm reduces reliability cost by up to 71.4% (with an average of 63.7%) while improving schedulability over a spectrum of workload and system parameters. Furthermore, results suggest that shortening scheduling times leads to a higher guarantee ratio. Hence, if parallel scheduling algorithms are applied to shorten scheduling times, the performance of heterogeneous clusters will be further enhanced.     

异构计算中的负载共享

在基于消息传递的异构并行计算系统中 ,各处理器或计算机具有自制和独立地调度、执行作业的能力 .当一个可划分的作业初始位于一个处理器上时 ,为了提高计算性能 ,该处理器可以请求其他异构处理器负载共享 ,参与协同计算 ,减少作业的完成时间 .该文提出了异构计算负载共享的一种方案 .首先 ,调用负载共享协议 ,收集当前各处理器参与负载共享的许可数据 ,包括共享时间段、计算能力等 .然后 ,构造一个作业量与作业完成时间之间的关系函数 .该函数是选择一组合适的处理器群、优化作业划分、作业完成时间最小的理论基础 .最     

Coordinating Parallel Processes on Networks of Workstations

The network of workstations (NOW) we consider for scheduling is heterogeneous and nondedicated, where computing power varies among the workstations and local and parallel jobs may interact with each other in execution. An effective NOW scheduling scheme needs sufficient information about system heterogeneity and job interactions. We use the measured power weight of each workstation to quantify the differences of computing capability in the system. Without a processing power usage agreement between parallel jobs and local user jobs in a workstation, job interactions are unpredictable, and performance of either type of jobs may not be guaranteed. Using the quantified and deterministic system information, we design a scheduling scheme calledself-coordinated local schedulingon a heterogeneous NOW. Based on a power usage agreement between local and parallel jobs, this scheme coordinates parallel processes independently in each workstation based on the coscheduling principle. We discuss its implementation on Unix System V Release 4 (SVR4). Our simulation results on a heterogeneous NOW show the effectiveness of the self-coordinated local scheduling scheme.     

异构Hadoop环境下的实时作业调度算法

为提升Hadoop集群在异构环境下处理硬实时作业的性能,提出一种基于历史进度自动调整作业优先级的调度算法（HAPS）。该算法实时监控作业进度信息,对作业进度率进行指数平滑预测,计算作业剩余执行时间,动态估算作业空闲时间。并据此实时更新作业队列中作业的优先级顺序,优先调度空闲时间小的作业。实验结果表明,HAPS有效地提高了异构环境下硬实时作业的执行成功率。     

A heterogeneous computing system for data mining workflows in multi-agent environments

Abstract: The computing-intensive data mining (DM) process calls for the support of a heterogeneous computing system, which consists of multiple computers with different configurations connected by a high-speed large-area network for increased computational power and resources. The DM process can be described as a multi-phase pipeline process, and in each phase there could be many optional methods. This makes the workflow for DM very complex and it can be modeled only by a directed acyclic graph (DAG). A heterogeneous computing system needs an effective and efficient scheduling framework, which orchestrates all the computing hardware to perform multiple competitive DM workflows. Motivated by the need for a practical solution of the scheduling problem for the DM workflow, this paper proposes a dynamic DAG scheduling algorithm according to the characteristics of an execution time estimation model for DM jobs. Based on an approximate estimation of job execution time, this algorithm first maps DM jobs to machines in a decentralized and diligent (defined in this paper) manner. Then the performance of this initial mapping can be improved through job migrations when necessary. The scheduling heuristic used considers the factors of both the minimal completion time criterion and the critical path in a DAG. We implement this system in an established multi-agent system environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. The system evaluation and its usage in oil well logging analysis are also discussed.     

Dynamic scheduling of a batch of parallel task jobs on heterogeneous clusters

This paper addresses the problem of minimizing the scheduling length (make-span) of a batch of jobs with different arrival times. A job is described by a direct acyclic graph (DAG) of parallel tasks. The paper proposes a dynamic scheduling method that adapts the schedule when new jobs are submitted and that may change the processors assigned to a job during its execution. The scheduling method is divided into a scheduling strategy and a scheduling algorithm. We also propose an adaptation of the Heterogeneous Earliest-Finish-Time (HEFT) algorithm, called here P-HEFT, to handle parallel tasks in heterogeneous clusters with good efficiency without compromising the makespan. The results of a comparison of this algorithm with another DAG scheduler using a simulation of several machine configurations and job types shows that P-HEFT gives a shorter makespan for a single DAG but scores worse for multiple DAGs. Finally, the results of the dynamic scheduling of a batch of jobs using the proposed scheduler method showed significant improvements for more heavily loaded machines when compared to the alternative resource reservation approach.     

一种面向BSP系统的多等待队列作业调度算法

在以往的BSP（Bulk Synchronous Parallel）系统中,作业调度都是采用基于单队列的优先级调度策略.它的优点是实现简单,但作业队列维护开销大,低优先级作业存在无限等待的问题.论文提出了面向BSP系统基于多等待队列的按优先级作业调度算法,以高响应比优先级队列为作业组织方式,并加入了作业优先级的动态调整策略,避免了低优先级作业因长期得不到执行而废弃的情况.目前,论文所提算法已成功运行于BC-BSP系统中.文中通过实验进一步证明,融合了作业优先级调整策略的基于多等待队列的作业调度算法较传统的单队列优先级调度算法在队列维护方面,能降低30％～50％的维护代价.另外,在兼顾作业的初始优先级的同时,能够减少低优先级作业的等待时间,避免低优先级作业的无限等待问题.     

Evolving priority scheduling heuristics with genetic programming

This paper investigates the use of genetic programming in automated synthesis of scheduling heuristics for an arbitrary performance measure. Genetic programming is used to evolve the priority function, which determines the priority values of certain system elements (jobs, machines). The priority function is used within an appropriate meta-algorithm for a given environment, which forms the priority scheduling heuristic. The evolved solutions are compared with existing scheduling heuristics and found to perform similarly to or better than existing algorithms. We intend to show that this approach is particularly useful for combinations of scheduling environments and performance measures for which no adequate scheduling algorithms exist.     

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.