A novel approach to resource scheduling for parallel query processing on computational grids

Advances in network technologies and the emergence of Grid computing have both increased the need and provided the infrastructure for computation and data intensive applications to run over collections of heterogeneous and autonomous nodes. In the context of database query processing, existing parallelisation techniques cannot operate well in Grid environments because the way they select machines and allocate tasks compromises partitioned parallelism. The main contribution of this paper is the proposal of a low-complexity, practical resource selection and scheduling algorithm that enables queries to employ partitioned parallelism, in order to achieve better performance in a Grid setting. The evaluation results show that the scheduler proposed outperforms current techniques without sacrificing the efficiency of resource utilisation. Recommended by: Ioannis Vlahavas     

在无中心式作业调度中的动态网格负载平衡实现

提出一个新颖的递归算法,用于实现动态的网格负载平衡。实验仿真了松耦合无中心式调度框架,基于传统并行系统的workload模型构建了网格workload模型,保守式装填法用作各结点上的本地调度策略。结果表明,在实现网格负载平衡上,这里的递归算法比静态调度方法更有效。     

Dynamic load balancing with adaptive factoring methods in scientific applications

To improve the performance of scientific applications with parallel loops, dynamic loop scheduling methods have been proposed. Such methods address performance degradations due to load imbalance caused by predictable phenomena like nonuniform data distribution or algorithmic variance, and unpredictable phenomena such as data access latency or operating system interference. In particular, methods such as factoring, weighted factoring, adaptive weighted factoring, and adaptive factoring have been developed based on a probabilistic analysis of parallel loop iterates with variable running times. These methods have been successfully implemented in a number of applications such as: N-Body and Monte Carlo simulations, computational fluid dynamics, and radar signal processing. The focus of this paper is on adaptive weighted factoring (AWF), a method that was designed for scheduling parallel loops in time-stepping scientific applications. The main contribution of the paper is to relax the time-stepping requirement, a modification that allows the AWF to be used in any application with a parallel loop. The modification further allows the AWF to adapt to load imbalance that may occur during loop execution. Results of experiments to compare the performance of the modified AWF with the performance of the other loop scheduling methods in the context of three nontrivial applications reveal that the performance of the modified method is comparable to, and in some cases, superior to the performance of the most recently introduced adaptive factoring method.

A performance-based parallel loop scheduling on grid environments

The effectiveness of loop self-scheduling schemes has been shown on traditional multiprocessors in the past and computing clusters in the recent years. However, parallel loop scheduling has not been widely applied to computing grids, which are characterized by heterogeneous resources and dynamic environments. In this paper, a performance-based approach, taking the two characteristics above into consideration, is proposed to schedule parallel loop iterations on grid environments. Furthermore, we use a parameter, SWR, to estimate the proportion of the workload which can be scheduled statically, thus alleviating the effect of irregular workloads. Experimental results on a grid testbed show that the proposed approach can reduce the completion time for applications with regular or irregular workloads. Consequently, we claim that parallel loop scheduling can benefit applications on grid environments.     

A scalable,parallel algorithm for maximal clique enumeration

The problem of maximal clique enumeration (MCE) is to enumerate all of the maximal cliques in a graph. Once enumerated, maximal cliques are widely used to solve problems in areas such as 3-D protein structure alignment, genome mapping, gene expression analysis, and detection of social hierarchies. Even the most efficient serial MCE algorithms require large amounts of time to enumerate the maximal cliques in networks arising from these problems that contain hundreds, thousands, or larger numbers of vertices. The previous attempts to provide practical solutions to the MCE problem through parallel implementation have had limited success, largely due to a number of challenges inherent to the nature of the MCE combinatorial search space. On the one hand, MCE algorithms often create a backtracking search tree that has a highly irregular and hard-or-impossible to predict structure; therefore, almost any static decomposition of the search tree by parallel processors results in highly unbalanced processor execution times. On the other hand, the data-intensive nature of the MCE problem often makes naive dynamic load distribution strategies that require extensive data movement prohibitively expensive. As a result, good scaling of the overall execution time of parallel MCE algorithms has been reported for only up to a couple hundred processors. In this paper, we propose a parallel, scalable, and memory-efficient MCE algorithm for distributed and/or shared memory high performance computing architectures, whose runtime scales linearly for thousands of processors on real-world application graphs with hundreds and thousands of nodes. Its scalability and efficiency are attributed to the proposed: (a) representation of the search tree decomposition to enable parallelization; (b) parallel depth-first backtracking search to both constrain the search space and minimize memory requirement; (c) least stringent synchronization to minimize data movement; and (d) on-demand work stealing intelligently coupled with work stack splitting to minimize computing elements’ idle time. To the best of our knowledge, the proposed parallel MCE algorithm is the first to achieve a linear scaling runtime using up to 2048 processors on Cray XT machines for a number of real-world biological networks.     

A new heuristic for workload balancing on identical parallel machines and a statistical perspective on the workload balancing criteria

We consider the multiprocessor scheduling problem in which independent jobs are scheduled on identical parallel machines, with the objective of minimizing the normalized sum of square for workload deviations (NSSWD) criterion in order to obtain workload balancing. NSSWD and other criteria for the related problem of number partitioning are presented from a statistical viewpoint, which allows to derive some insightful connections with statistical measures of dispersion. A new local search algorithm is also developed. The algorithm at first generates and merges a set of partial solutions in order to obtain a feasible solution for the multiprocessor scheduling problem. Then a set of interchange procedures are utilized in order to improve the solution. The effectiveness of this approach is evaluated by solving a large number of benchmark instances.     

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.     

Dynamic slack allocation algorithms for energy minimization on parallel machines

We explore novel algorithms for DVS (Dynamic Voltage Scaling) based energy minimization of DAG (Directed Acyclic Graph) based applications on parallel and distributed machines in dynamic environments. Static DVS algorithms for DAG execution use the estimated execution time. The estimated time in practice is overestimated or underestimated. Therefore, many tasks may be completed earlier or later than expected during the actual execution. For overestimation, the extra available slack can be added to future tasks so that energy requirements can be reduced. For underestimation, the increased time may cause the application to miss the deadline. Slack can be reduced for future tasks to reduce the possibility of not missing the deadline. In this paper, we present novel dynamic scheduling algorithms for reallocating the slack for future tasks to reduce energy and/or satisfy deadline constraints. Experimental results show that our algorithms are comparable to static algorithms applied at runtime in terms of energy minimization and deadline satisfaction, but require considerably smaller computational overhead.     

A dynamic and adaptive load balancing strategy for parallel file system with large-scale I/O servers

Many solutions have been proposed to tackle the load imbalance issue of parallel file systems. However, all these solutions either adopt centralized algorithms, or lack considerations for both the network transmission and the tradeoff between benefits and side-effects of each dynamic file migration. Therefore, existing solutions will be prohibitively inefficient in large-scale parallel file systems. To address this problem, this paper presents SALB, a dynamic and adaptive load balancing algorithm which is totally based on a distributed architecture. To be also aware of the network transmission, SALB on the one hand adopts an adaptively adjusted load collection threshold in order to reduce the message exchanges for load collection, and on the other hand it employs an on-line load prediction model with a view to reducing the decision delay caused by the network transmission latency. Moreover, SALB employs an optimization model for selecting the migration candidates so as to balance the benefits and the side-effects of each dynamic file migration. Extensive experiments are conducted to prove the effectiveness of SALB. The results show that SALB achieves an optimal performance not only on the mean response time but also on the resource utilization among the schemes for comparison. The simulation results also indicate that SALB is able to deliver high scalability.     

Simultaneous Scheduling of Replication and Computation for Data-Intensive Applications on the Grid

Managing large datasets has become one major application of Grids. Life science applications usually manage large databases that should be replicated to scale applications. The growing number of users and the simple access to Internet-based application has stressed Grid middleware. Such environment are thus asked to manage data and schedule computation tasks at the same time. These two important operations have to be tightly coupled. This paper presents an algorithm (Scheduling and Replication Algorithm, SRA) that combines data management and scheduling using a steady-state approach. Using a model of the platform, the number of requests as well as their distribution, the number and size of databases, we define a linear program to satisfy all the constraints at every level of the platform in steady-state. The solution of this linear program will give us a placement for the databases on the servers as well as providing, for each kind of job, the server on which they should be executed. Our theoretical results are validated using simulation and logs from a large life science application. This work was supported in part by the ACI GRID and Grid5000 projects of the French Department of Research.     

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.     

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.     

A refinement-tree based partitioning method for dynamic load balancing with adaptively refined grids

The partitioning of an adaptive grid for distribution over parallel processors is considered in the context of adaptive multilevel methods for solving partial differential equations. A partitioning method based on the refinement-tree is presented. This method applies to most types of grids in two and three dimensions. For triangular and tetrahedral grids, it is guaranteed to produce connected partitions; no other partitioning method makes this guarantee. The method is related to the OCTREE method and space filling curves. Numerical results comparing it with several popular partitioning methods show that it computes partitions in an amount of time similar to fast load balancing methods like recursive coordinate bisection, and with mesh quality similar to slower, more optimal methods like the multilevel diffusive method in ParMETIS.     

Static heuristics for robust resource allocation of continuously executing applications

We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, i.e., the load presented by the set of sensors, is likely to change unpredictably, possibly resulting in a QoS violation. The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were “seeded” by the best solution found by using a set of fast greedy heuristics.     

基于模糊多目标决策的网格资源调度算法研究

针对如何提高网格资源的使用效率和用户满意度及系统效率等问题,提出了一个基于层次调度模型的、将资源的表示与需求用XML方式描述、以模糊多目标决策理论为资源调度策略,以用户满意度和系统资源利用率为主要目标的综合网格资源调度算法.该算法不仅最大程度提高用户的满意度,而且较好地解决了网格资源的均衡使用,极大地提高了系统效率,对网格系统综合性能有明显地提高.     

基于公平调度算法的分布式系统负载均衡研究

负载均衡是提高分布式系统性能的重要技术,同时也是系统高可用性、可扩展性、冗余性的必然要求.针对分布式系统任务调度不均衡问题,在分析和建立系统仿真和任务调度模型的基础上,提出了一种基于公平指标的任务调度负载均衡算法,推导出在多节点条件下的任务分配方法,并在此模型下改进了基于公平指标的负载均衡算法.最后,在Linux平台下,进行了仿真实验和性能比较.实验结果表明,该算法是有效的,它可以有效地提高分布式系统的性能和效率.     

Use of run time predictions for automatic co-allocation of multi-cluster resources for iterative parallel applications

Metaschedulers co-allocate resources by requesting a fixed number of processors and usage time for each cluster. These static requests, defined by users, limit the initial scheduling and prevent rescheduling of applications to other resource sets. It is also difficult for users to estimate application execution times, especially on heterogeneous environments. To overcome these problems, metaschedulers can use performance predictions for automatic resource selection. This paper proposes a resource co-allocation technique with rescheduling support based on performance predictions for multi-cluster iterative parallel applications. Iterative applications have been used to solve a variety of problems in science and engineering, including large-scale computations based on the asynchronous model more recently. We performed experiments using an iterative parallel application, which consists of benchmark multiobjective problems, with both synchronous and asynchronous communication models on Grid’5000. The results show run time predictions with an average error of 7% and prevention of up to 35% and 57% of run time overestimations to support rescheduling for synchronous and asynchronous models, respectively. The performance predictions require no application source code access. One of the main findings is that as the asynchronous model masks communication and computation, it requires no network information to predict execution times. By using our co-allocation technique, metaschedulers become responsible for run time predictions, process mapping, and application rescheduling; releasing the user from these burden tasks.     

Grid平台上的NGS编译技术

随着网络技术的发展,在异构平台上使用共同的计算和信息资源将很快成为可能。Grid(网格)就是这样一种提供资源共享的新兴平台,而在其之上的下一代软件程序(NGS)则对编译器提出了新的挑战犤1犦。未来Grid平台上的编译系统将是能够进行动态编译和优化,根据实时系统以及网络的性能不断进行自我调整的软件模型,同时它还能为具有自适应性的应用程序提供编译支持。     

基于自适应与主动消息的任务调度策略研究与实现

在并行分布计算中,任务调度策略是影响并行分布计算性能的重要因素,结合现有任务调度法存在的问题,运用有效聚合与充分释放的思想,提出并实现了一种具有自适应和主动消息的任务调度策略,有效地提高了系统的整体性能。     

A novel formulation of the dynamic balancing of five-bar linkages with applications to link optimization

In this report, we derive design equations and techniques for the dynamic balancing of five-bar linkage, using a novel and simplified approach. Firstly, in order to derive the dynamic equations of the mechanism we have applied the natural orthogonal complement method. Subsequently, an optimization method for the dynamic balancing of the linkage is proposed. The conditions of dynamic balancing of the five-bar linkage are expressed as seven equations and four inequalities, with twelve linkage parameters. The dynamic balancing of the mechanism is formulated and solved as an optimization problem under equality constraints. The application of the new approach is illustrated through a numerical example.