A multi-dimensional scheduling scheme in a Grid computing environment

In this paper, we propose a novel distributed resource-scheduling algorithm capable of handling multiple resource requirements for jobs that arrive in a Grid computing environment. In our proposed algorithm, referred to as multiple resource scheduling (MRS) algorithm, we take into account both the site capabilities and the resource requirements of jobs. The main objective of the algorithm is to obtain a minimal execution schedule through efficient management of available Grid resources. We first propose a model in which the job and site resource characteristics can be captured together and used in the scheduling algorithm. To do so, we introduce the concept of a n-dimensional virtual map and resource potential. Based on the proposed model, we conduct rigorous simulation experiments with real-life workload traces reported in the literature to quantify the performance. We compare our strategy with most of the commonly used algorithms in place on performance metrics such as job wait times, queue completion times, and average resource utilization. Our combined consideration of job and resource characteristics is shown to render high-performance with respect to above-mentioned metrics in the environment. Our study also reveals the fact that MRS scheme has a capability to adapt to both serial and parallel job requirements, especially when job fragmentation occurs. Our experimental results clearly show that MRS outperforms other strategies and we highlight the impact and importance of our strategy.     

Grid resource management policies for load-balancing and energy-saving by vacation queuing theory

The resource management is the central component of grid system. The analysis of the workload log file of LCG including the job arrival and the resource utilization daily cycle shows that the idle sites in the Grid are the source of load imbalance and energy waste. Here we focus on these two issues: balancing the workload by transferring jobs to idle sites at prime time to minimize the response time and maximize the resource utilization; power management by switch the idle sites to sleeping mode at non-prime time to minimize the energy consume. We form the M/G/1 queue model with server vacations, startup and closedown to analysis the performance metrics to instruct the design of load-balancing and energy-saving policies. We provide our Adaptive Receiver Initiated (ARI) load-balancing strategy and power-management policy for energy-saving. The simulation experiments prove the accuracy of our analysis and the comparisons results indicate our policies are largely suitable for large-scale heterogeneous grid environment.     

Workload vs scheduling policies in a dual-resource constrained job shop

The purpose of this paper is to report on research conducted to examine the effectiveness of different scheduling policies in a dual-constrained job shop under various workload conditions. The standard assumption in most job shop scheduling research has been that a 90% utilization of the shop is achieved. However, since shop utilization levels vary widely, it was hypothesized that scheduling policies that are optimum under one load condition might not be as effective under other load conditions. The model for this simulation experiment represented a job shop constrained by both labor and machines. The shop contains four machine centers with random routing of jobs through the shop. Shop workload was defined at three levels: 70, 85 and 99% utilization. Four machine scheduling rules and three labor assignment rules were tested for each of the shop workload levels, with mean job flow time as. the performance criterion. The results of the 3 × 4 × 3 factorial experiment showed that the advantage of the SPT (shortest processing time) machine scheduling rule over other rules is diminished dramatically when shop utilization is reduced from 99 to 85% or below. This same observation holds for other rules considered. The LNQ (longest queue length) labor assignment rule outperformed other rules at the 99% utilization level, but yielded no significant difference in performance at the 85% and below workload levels.     

Performance Prediction and Analysis of BOINC Projects: An Empirical Study with EmBOINC

Middleware systems for volunteer computing convert a set of computers that is large and diverse (in terms of hardware, software, availability, reliability, and trustworthiness) into a unified computing resource. This involves a number of scheduling policies and parameters, which have a large impact on the throughput and other performance metrics. How can we study and refine these policies? Experimentation in the context of a working project is problematic, and it is difficult to accurately model complex middleware in a conventional simulator. Instead, we use an approach in which the policies being studied are “emulated”, using parts of the actual middleware. In this paper we describe EmBOINC, an emulator based on the BOINC middleware system. EmBOINC simulates a population of volunteered clients (including heterogeneity, churn, availability, and reliability) and emulates the BOINC server components. After describing the design of EmBOINC and its validation, we present three case studies in which the impact of different scheduling policies are quantified in terms of throughput, latency, and starvation metrics.     

Performance of gang scheduling strategies in a parallel system

In this paper we study job scheduling performance in a partitionable parallel system. Jobs consist of parallel tasks scheduled to execute concurrently on processor partitions, where each task starts at the same time and computes at the same pace. The performance of different scheduling schemes is compared over various workloads. The impact of the variability of tasks service time is also studied. Various performance metrics are examined. The objective is to achieve good overall performance and also small scheduling overhead. Simulated results reveal that periodic job scheduling and also scheduling which depends on the number of job insertions in the queue can succeed these goals.     

利用作业可塑性改进结合回填FCFS策略的性能

结合回填的FCFS策略是超级计算机上使用最为普遍的调度策略,针对该策略在响应时间和系统利用率等方面的不足,提出了改进其性能的DGA方法。该方法利用并行作业的可塑性,通过调度时对作业平均响应时间的预测来选择适合的作业请求规模,并利用遗传算法来解决最优作业资源请求的搜索问题。模拟器上实际作业流的模拟结果表明：该方法可以显著地改进结合回填的FCFS策略的调度效果,也优于已有的可塑性作业调度策略。     

The impact of workload variability on the energy efficiency of large-scale heterogeneous distributed systems

Previous studies have shown that the workload variability has a serious impact on the performance of large-scale distributed architectures, since it may cause significant fluctuations in service demands. Energy efficiency is one of the aspects of such platforms that are of paramount importance and therefore it is imperative to investigate how it may also be affected by this factor. Towards this direction, in this paper we investigate via simulation the impact of workload variability, in terms of computational volume and interarrival times, on the energy consumption of a large-scale heterogeneous distributed system. The workload consists of real-time bag-of-tasks jobs that arrive dynamically at the system. The execution rate and power consumption characteristics of the processors are modeled after real-world processors, according to the Standard Performance Evaluation Corporation (SPEC) Power benchmark. Four heuristics are employed for the scheduling of the workload, two commonly used baseline policies and two proposed energy-aware heuristics. The simulation results reveal that the workload variability has a significant impact on the energy consumption of the system and that the severity of the impact depends on the employed scheduling technique.     

Simulation study of multitasking in distributed server systems with variable workload

The multitasking performance of a distributed server system is considered where the workload is variable. Time varying distributions are proposed for job arrival, job parallelism, and task service demand. A simulation model is used to address performance issues associated with task scheduling. The objective is to identify conditions that produce good overall system performance, while maintaining fairness of individual job execution times. Simulated results show that all task scheduling methods have merit. In all cases, the best policy depends on performance goals. Furthermore, although the paper studies distributed multiprocessor system performance, it also addresses the performance issues of other systems where multitasking and scheduling is used, since no restrictions are imposed on the server/job/task characteristics.     

EMM: Extended matching market based scheduling for big data platform hadoop

Hadoop has emerged as a popular choice for processing Big data. Its cluster is used to process large scale jobs. The performance of a cluster is largely dependent upon the different kind of scheduling policies employed for job processing. However, a single type of scheduling policy may not be suitable for different kind of jobs. Inefficient performance of a cluster is an apparent outcome of inappropriate scheduling policies. These policies are either too complex or they are too elementary to understand the diverse jobs and their needs. Most of them follow a fixed pattern, which cannot be considered as a common solution for different jobs. The effect of such a non-fitting mechanism is lower resource utilization and poor cluster performance. In this paper, a pluggable scheduling mechanism is proposed for efficient and adaptive processing of the jobs. It utilizes the Matching Market concept for the allocation and further adaptively accommodates the diverse needs of the multiple jobs by understanding the varying requirements of the tasks. The experimental results reveal an enhanced resource utilization and improved cluster performance with an overall reduction in makespan. In certain instances, we have seen resource utilization improved up to 80% and performance improvement up to 60% with the proposed technique. Cluster efficiency is increased up of 31%. The evaluation and comparisons were conducted on various scheduling policies using different benchmarks of Hadoop with the same data and identical configurations. The proposed system has shown significant improvement in cluster efficiency.

     

大规模并行计算机作业调度评价

大规模并行计算机的作业调度直接关系到其计算能力的发挥,因而相应的研究具有十分重要的意义。论文通过对国外现有作业调度评价体系的研究,建立了更能反映并行作业特点的作业调度策略评价体系,在此基础上设计并实现了一个作业模拟调度环境。模拟调度环境采用事件驱动的工作模式,支持FCFS、大作业优先、小作业优先、长作业优先、短作业优先、GANG等调度策略。模拟测试结果表明,GANG调度策略优于所有测试的空间共享调度策略;同时在空间共享调度策略中,短作业优先策略和大作业优先策略具有较好的性能。     

A novel index system describing program runtime characteristics for workload consolidation

Workload consolidation is a common method to improve the resource utilization in clusters or data centers. In order to achieve efficient workload consolidation, the runtime characteristics of a program should be taken into consideration in scheduling. In this paper, we propose a novel index system for efficiently describing the program runtime characteristics. With the help of this index system, programs can be classified by the following runtime characteristics: 1) dependence to multi-dimensional resources including CPU, disk I/O, memory and network I/O; and 2) impact and vulnerability to resource sharing embodied by resource usage and resource sensitivity. In order to verify the effectiveness of this novel index system in workload consolidation, a scheduling strategy, Sche-index, using the new index system for workload consolidation is proposed. Experiment results show that compared with traditional least-loaded scheduling strategy, Sche-index can improve both program performance and system resource utilization significantly.     

Processor Allocation in Multiprogrammed Distributed-Memory Parallel Computer Systems

In this paper, we examine three general classes of space-sharing scheduling policies under a workload representative of large-scale scientific computing. These policies differ in the way processors are partitioned among the jobs as well as in the way jobs are prioritized for execution on the partitions. We consider new static, adaptive and dynamic policies that differ from previously proposed policies by exploiting user-supplied information about the resource requirements of submitted jobs. We examine the performance characteristics of these policies from both the system and user perspectives. Our results demonstrate that existing static schemes do not perform well under varying workloads, and that the system scheduling policy for such workloads must distinguish between jobs with large differences in execution times. We show that obtaining good performance under adaptive policies requires somea prioriknowledge of the job mix in these systems. We further show that a judiciously parameterized dynamic space-sharing policy can outperform adaptive policies from both the system and user perspectives.     

Impact of workload and system parameters on next generation clusterscheduling mechanisms

Scheduling of processes onto processors of a parallel machine has always been an important and challenging area of research. The issue becomes even more crucial and difficult as we gradually progress to the use of off-the-shelf workstations, operating systems, and high bandwidth networks to build cost-effective clusters for demanding applications. Clusters are gaining acceptance not just in scientific applications that need supercomputing power, but also in domains such as databases, web service, and multimedia which place diverse Quality-of-Service (QoS) demands on the underlying system. Further, these applications have diverse characteristics in terms of their computation, communication, and I/O requirements, making conventional parallel scheduling solutions, such as space sharing or gang scheduling, unattractive. At the same time, leaving it to the native operating system of each node to make decisions independently can lead to ineffective use of system resources whenever there is communication. Instead, an emerging class of dynamic coscheduling mechanisms that attempt to take remedial actions to guide the system toward coscheduled execution without requiring explicit synchronization offers a lot of promise for cluster scheduling. Using a detailed simulator, this paper evaluates the pros and cons of different dynamic coscheduling alternatives while comparing their advantages over traditional gang scheduling (and not performing any coordinated scheduling at all). The impact of dynamic job arrivals, job characteristics, and different system parameters on these alternatives is evaluated in terms of several performance criteria. In addition, heuristics to enhance one of the alternatives even further are identified, classified, and evaluated. It is shown that these heuristics can significantly outperform the other alternatives over a spectrum of workload and system parameters and is thus a much better option for clusters than conventional gang scheduling     

A simulation framework for priority scheduling on heterogeneous clusters

Executing heterogeneous workloads with different priorities, resource demands and performance objectives is one of the key operations for today’s data centers to increase resource as well as energy efficiency. In order to meet the performance objectives of diverse workloads, schedulers rely on evictions even resulting in waste of resources due to lost executions of evicted tasks. It is not straightforward to design priority schedulers which capture key aspects of workloads and systems and also to strike a balance between resource (in)efficiency and application performance tradeoff. To explore large space of designing such schedulers, we propose a trace-driven cluster management framework that models a comprehensive set of system configurations and general priority-based scheduling policies. In particular, we focus on the impact of task evictions on resource inefficiency and task response times of multiple priority classes driven by Google production cluster trace. Moreover, we propose a system design as a use case exploiting workload heterogeneity and introducing workload-awareness into the system configuration and task assignment.     

求解柔性作业车间调度的GATOC混合方法

柔性作业车间调度问题是经典作业车间调度问题的扩展,它允许工序在可选加工机器集中任意一台上加工,加工时间随加工机器不同而不同。针对柔性作业车间调度问题的特点,提出一种基于约束理论的局部搜索方法,对关键路径上的机器的负荷率进行比较,寻找瓶颈机器,以保证各机器之间的负荷平衡。为了克服传统遗传算法早熟和收敛慢的缺点,设计多种变异操作,增加种群多样性。为了更好保留每代中的优良解,设计了基于海明距离的精英解保留策略。运用提出的算法求解基准测试问题,验证了算法的可行性和有效性。     

Multihybrid job scheduling for fault-tolerant distributed computing in policy-constrained resource networks

Unpredictable fluctuations in resource availability often lead to rescheduling decisions that sacrifice a success rate of job completion in batch job scheduling. To overcome this limitation, we consider the problem of assigning a set of sequential batch jobs with demands to a set of resources with constraints such as heterogeneous rescheduling policies and capabilities. The ultimate goal is to find an optimal allocation such that performance benefits in terms of makespan and utilization are maximized according to the principle of Pareto optimality, while maintaining the job failure rate close to an acceptably low bound. To this end, we formulate a multihybrid policy decision problem (MPDP) on the primary-backup fault tolerance model and theoretically show its NP-completeness. The main contribution is to prove that our multihybrid job scheduling (MJS) scheme confidently guarantees the fault-tolerant performance by adaptively combining jobs and resources with different rescheduling policies in MPDP. Furthermore, we demonstrate that the proposed MJS scheme outperforms the five rescheduling heuristics in solution quality, searching adaptability and time efficiency by conducting a set of extensive simulations under various scheduling conditions.     

On the distribution of sequential jobs in random brokering for heterogeneous computational grids

Scheduling stochastic workloads is a difficult task. In order to design efficient scheduling algorithms for such workloads, it is required to have a good in-depth knowledge of basic random scheduling strategies. This paper analyzes the distribution of sequential jobs and the system behavior in heterogeneous computational grid environments where the brokering is done in such a way that each computing element has a probability to be chosen proportional to its number of CPUs and (new from the previous paper) its relative speed. We provide the asymptotic behavior for several metrics (queue-sizes, slowdowns, etc.) or, in some cases, an approximation of this behavior. We study these metrics for a variety of workload configurations (load, distribution, etc.). We compare our probabilistic analysis to simulations in order to validate our results. These results provide a good understanding of the system behavior for each metric proposed. This enables us to design advanced and efficient algorithms for more complex cases.     

Double auction-inspired meta-scheduling of parallel applications on global grids

Meta-schedulers map jobs to computational resources that are part of a Grid, such as clusters, that in turn have their own local job schedulers. Existing Grid meta-schedulers either target system-centric metrics, such as utilisation and throughput, or prioritise jobs based on utility metrics provided by the users. The system-centric approach gives less importance to users’ individual utility, while the user-centric approach may have adverse effects such as poor system performance and unfair treatment of users. Therefore, this paper proposes a novel meta-scheduler, based on the well-known double auction mechanism that aims to satisfy users’ service requirements as well as ensuring balanced utilisation of resources across a Grid. We have designed valuation metrics that commodify both the complex resource requirements of users and the capabilities of available computational resources. Through simulation using real traces, we compare our scheduling mechanism with other common mechanisms widely used by both existing market-based and traditional meta-schedulers. The results show that our meta-scheduling mechanism not only satisfies up to 15% more user requirements than others, but also improves system utilisation through load balancing.     

Optimizing communication in mobile ad hoc network clustering

Mobile ad hoc networks (MANETs) are gaining popularity in recent years due to their flexibility, the proliferation of smart computing devices, and developments in wireless communications. Clustering is an important research problem for MANETs because it enables efficient utilization of resources, and must strike a delicate balance between battery energy, mobility, node degree, etc. In this paper, we consider the typical communication workload of every mobile node as well as the additional communication workload of clusterheads in MANET clustering. We propose an algorithm that optimizes communication workload, power consumption, clusterhead lifetime, and node degree. Experiment results show that our clustering approach produces effectively balanced clusters over a diverse set of random scenarios.     

High-throughput query scheduling with spatial clustering based on distributed exponential moving average

In distributed scientific query processing systems, leveraging distributed cached data is becoming more important. In such systems, a front-end query scheduler distributes queries among many application servers rather than processing queries in a few high-performance workstations. Although many query scheduling policies exist such as round-robin and load-monitoring, they are not sophisticated enough to exploit cached results as well as balance the workload. Efforts were made to improve the query processing performance using statistical methods such as exponential moving average. However, existing methods have limitations for certain query patterns: queries with hotspots, or dynamic query distributions. In this paper, we propose novel query scheduling policies that take into account both the contents of distributed caching infrastructure and the load balance among the servers. Our experiments show that the proposed query scheduling policies outperform existing policies by producing better query plans in terms of load balance and cache-hit ratio.