Heterogeneous Computational Grids Research Articles

Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems

We address a multicriteria non-preemptive energy-aware scheduling problem for computational Grid systems. This work introduces a new formulation of the scheduling problem for multicore heterogeneous computational Grid systems in which the minimization of the energy consumption, along with the makespan metric, is considered. We adopt a two-level model, in which a meta-broker agent (level 1) receives all user tasks and schedules them on the available resources, belonging to different local providers (level 2). The computing capacity and energy consumption of resources are taken from real multi-core processors from the main current vendors. Twenty novel list scheduling methods for the problem are proposed, and a comparative analysis of all of them over a large set of problem instances is presented. Additionally, a scalability study is performed in order to analyze the contribution of the best new bi-objective list scheduling heuristics when the problem dimension grows. We conclude after the experimental analysis that accurate trade-off schedules are computed by using the new proposed methods.

A Capacity-Based Load Balancing and Job Migration Algorithm for Heterogeneous Computational Grids

This paper addresses the problem of scheduling and load balancing in heterogeneous computational grids. We proposed a two-level load balancing policy for the multi -cluster grid environment where computational resources are dispersed in different adm inistrative domains or clusters which are located in different local area networks. The proposed load balancing policy takes into account the heterogeneity of the computational resources. It distributes the system workload based on the processing elements capacity which leads to minimize the overall job mean response time and maximize the system utilization and throughput at the steady state. To evaluate the performance of the proposed load balancing policy, an analytical model is developed. The results ob tained analytically are validated by simulating the model using Arena simulation package. The results show that the overall mean job response time obtained by simulation is very close to that obtained analytically. Also, the simulation results show that th e performance of the proposed load balancing policy outperforms that of theRandom and Uniform distribution load balancing policies in terms of mean job response time. The maximumimprovement ratio obtained when the system workload is low. It decreases slowly as the system workload gradually increases and it decreases rapidly when the system arrival rate λ approaches the system processing rate µ because the system gradually approaches its saturation point.

A Load Balancing Policy for Heterogeneous Computational Grids

Computational grids have the potential computing power for solving large-scale scientific computing applications. To improve the global throughput of these applications, workload has to be evenly distributed among the available computational resources in the grid environment. This paper addresses the problem of scheduling and load balancing in heterogeneous computational grids. We proposed a two-level load balancing policy for the multi-cluster grid environment where computational resources are dispersed in different administrative domains or clusters which are located in different local area networks. The proposed load balancing policy takes into account the heterogeneity of the computational resources. It distributes the system workload based on the processing elements capacity which leads to minimize the overall job mean response time and maximize the system utilization and throughput at the steady state. An analytical model is developed to evaluate the performance of the proposed load balancing policy. The results obtained analytically are validated by simulating the model using Arena simulation package. The results show that the overall mean job response time obtained by simulation is very close to that obtained analytically. Also, the simulation results show that the performance of the proposed load balancing policy outperforms that of the random and uniform distribution load balancing policies in terms of mean job response time. The improvement ratio increases as the system workload increases and the maximum improvement ratio obtained is about 72% in the range of system parameter values examined. Keywords-grid computing; resource management; load balancing; performance evaluation; queuing theory; simulation models.

Scheduling of tasks in the parareal algorithm

Parallelization of partial differential equations (PDEs) by time decomposition has attracted much interest, mainly due to its potential to enable very long time simulations beyond what is possible using spatial domain decomposition. However, there has only been limited performance analysis of the parareal algorithm in the literature, ignoring the efficient scheduling of tasks. This paper presents a detailed study of the scheduling of tasks in the parareal algorithm that achieves significantly better efficiency than the usual algorithm. Two algorithms are proposed, one which uses a manager–worker paradigm with overlap of sequential and parallel phases, and a second that is completely distributed. Experiments were conducted with the 2D heat equation. It was found that the rate of convergence decreases as the number of processors increases, in the case of strong scaling (fixed time interval). However, for weak scaling results the rate of convergence was unaffected by the number of processors. The results of this paper suggest that the parareal algorithm is a promising approach to solving long time evolution problems, particularly when the goal is simulation of longer times using more processors. It also exhibits characteristics that make it particularly suitable for execution on heterogeneous computational grids, such as low communication overhead and easy accommodation of different processor and network speeds.

Analysis of Threshold Voltage Distribution Due to Random Dopants: A 100 000-Sample 3-D Simulation Study

Using the Glasgow ldquoatomisticrdquo simulator, we have performed 3D statistical simulations of random-dopant-induced threshold voltage variation in state-of-the-art 35- and 13-nm bulk MOSFETs consisting of statistical samples of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> or more microscopically different transistors. Simulation on such an unprecedented scale has been enabled by grid technology, which allows the distribution and the monitoring of very large ensembles on heterogeneous computational grids, as well as the automated handling of large amounts of output data. The results of these simulations show a pronounced asymmetry in the distribution of the MOSFET threshold voltages, which increases with transistor scaling. A comprehensive statistical analysis enabled by the large sample size reveals the origin of this observed asymmetry, provides a detailed insight into the underlying physical processes, and enables the statistical enhancement of simulations of random-dopant-induced threshold voltage variation.

Adaptive Processor Allocation for Moldable Jobs in Computational Grid

In a computational grid environment, a common practice is try to allocate an entire parallel job onto a single participating site. Sometimes a parallel job, upon its submission, cannot fit in any single site due to the occupation of some resources by running jobs. How the job scheduler handles such situations is an important issue which has the potential to further improve the utilization of grid resources as well as the performance of parallel jobs. This article develops adaptive processor allocation policies based on the moldable property of parallel jobs to deal with such situations in a heterogeneous computational grid environment. The proposed policies are evaluated through a series of simulations using real workload traces. The results indicate that the proposed adaptive processor allocation policies can further improve the system performance of a heterogeneous computational grid significantly.

Patient-specific simulation as a basis for clinical decision-making

Patient-specific medical simulation holds the promise of determining tailored medical treatment based on the characteristics of an individual patient (for example, using a genotypic assay of a sequence of DNA). Decision-support systems based on patient-specific simulation can potentially revolutionize the way that clinicians plan courses of treatment for various conditions, ranging from viral infections to arterial abnormalities. Basing medical decisions on the results of simulations that use models derived from data specific to the patient in question means that the effectiveness of a range of potential treatments can be assessed before they are actually administered, preventing the patient from experiencing unnecessary or ineffective treatments. We illustrate the potential for patient-specific simulation by first discussing the scale of the evolving international grid infrastructure that is now available to underpin such applications. We then consider two case studies, one concerned with the treatment of patients with HIV/AIDS and the other addressing neuropathologies associated with the intracranial vasculature. Such patient-specific medical simulations require access to both appropriate patient data and the computational resources on which to perform potentially very large simulations. Computational infrastructure providers need to furnish access to a wide range of different types of resource, typically made available through heterogeneous computational grids, and to institute policies that facilitate the performance of patient-specific simulations on those resources. To support these kinds of simulations, where life and death decisions are being made, computational resource providers must give urgent priority to such jobs, for example by allowing them to pre-empt the queue on a machine and run straight away. We describe systems that enable such priority computing.

Evaluating a High-Level Parallel Language (GpH) for Computational GRIDs

Computational GRIDs potentially offer low-cost, readily available, and large-scale high-performance platforms. For the parallel execution of programs, however, computational GRIDs pose serious challenges: they are heterogeneous and have hierarchical and often shared interconnects, with high and variable latencies between clusters. This paper investigates whether a programming language with high-level parallel coordination and a distributed shared memory (DSM) model can deliver good and scalable performance on a range of computational GRID configurations. The high-level language Glasgow parallel Haskell (GpH) abstracts over the architectural complexities of the computational GRID, and we have developed GRID-GUM2, a sophisticated grid-specific implementation of GpH, to produce the first high-level DSM parallel language implementation for computational Grids. We report a systematic performance evaluation of GRID-GUM2 on combinations of high/low and homogeneous/heterogeneous computational GRIDS. We measure the performance of a small set of kernel parallel programs representing a variety of application areas, two parallel paradigms, and ranges of communication degree and parallel irregularity. We investigate GRID-GUM2's performance scalability on medium-scale heterogeneous and high-latency computational GRIDs and analyze the performance with respect to the program characteristics of communication frequency and degree of irregular parallelism.

Performance prediction with skeletons

The performance skeleton of an application is a short running program whose performance in any scenario reflects the performance of the application it represents. Specifically, the execution time of the performance skeleton is a small fixed fraction of the execution time of the corresponding application in any execution environment. Such a skeleton can be employed to quickly estimate the performance of a large application under existing network and node sharing. This paper presents a framework for automatic construction of performance skeletons of a specified execution time and evaluates their use in performance prediction with CPU and network sharing. The approach is based on capturing the execution behavior of an application and automatically generating a synthetic skeleton program that reflects that execution behavior. The paper demonstrates that performance skeletons running for a few seconds can predict the application execution time fairly accurately. Relationship of skeleton execution time, application characteristics, and nature of resource sharing, to accuracy of skeleton based performance prediction, is analyzed in detail. The goal of this research is accurate performance estimation in heterogeneous and shared computational grids.

Trustworthy remote compiling services for grid-based scientific applications

Grid computing, which is characterized by large-scale sharing and collaboration of dynamic resources, is becoming an emerging computing platform on a global scale for data-intensive and computation-intensive scientific application. However, the complications of large-scale scientific computations and simulations harnessing massive computing resources are compounded by extensive heterogeneity in environments arising from "the Grid." Scientists and engineers lack an intuitive grid-based compilation tool, which has contributed to the difficulty of exploiting these diverse resources and developing their applications on the grid. While manual configuration of various toolkits simplifying the end-to-end completion of a job is adequate for a computational grid with a limited number of nodes, the compilation procedure becomes inefficient for a computational grid with an increasing number of heterogeneous computational service providers. On the other hand, a global-scale computational grid is a potentially untrustworthy computing environment. How to take advantage of the potentially untrustworthy grid resources to provide trustworthy computational services for large-scale scientific applications is another critical issue. In this article, a remote compiling service for a heterogeneous computational grid is developed. In addition to running compilation tasks, the remote compiling service provides security enforcement and validation facilities, including intermediate value checking, secure source program submission, restricted compilation, and binary inspection, to support trustworthy compilation and execution of grid-based scientific applications. Overall, it is expected that our remote compiling services on the grid can tackle the heterogeneity problem of the grid and provide a secure, trustworthy, reliable, and state-of-the-art mechanism to develop grid-aware scientific applications.

On the performance-driven load distribution for heterogeneous computational grids

Load balancing has been a key concern for traditional multiprocessor systems. The emergence of computational grids extends this challenge to deal with more serious problems, such as scalability, heterogeneity of computing resources and considerable transfer delay. In this paper, we present a dynamic and decentralized load balancing algorithm for computationally intensive jobs on a heterogeneous distributed computing platform. The time spent by a job in the system is considered as the main issue that needs to be minimized. Our main contributions are: (1) Our algorithm uses site desirability for processing power and transfer delay to guide load assignment and redistribution, (2) Our transfer and location policies are a combination of two specific strategies that are performance driven to minimize execution cost. These two policies are the Instantaneous Distribution Policy (IDP) and the Load Adjustment Policy (LAP), (3) The communication overhead involved in information collection is reduced using mutual information feedback. The simulation results show that our proposed algorithm outperforms conventional approaches over a wide range of system parameters.

Parallel simulation of three-dimensional complex flows: Application to two-phase compressible flows and turbulent wakes

In this paper, we present parallel simulations of three-dimensional complex flows obtained on an ORIGIN 3800 computer and on homogeneous and heterogeneous (processors of different speeds and RAM) computational grids. The solver under consideration, which is representative of modern numerics used in industrial computational fluid dynamics (CFD) software, is based on a mixed element-volume method on unstructured tedrahedrisations. The parallelisation strategy combines mesh partitioning techniques, a message-passing programming model and an additive Schwarz algorithm. The parallelisation performances are analysed on a two-phase compressible flow and a turbulent flow past a square cylinder.

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.

Guest Editorial: Special Section on Algorithm Design and Scheduling Techniques (Realistic Platform Models) for Heterogeneous Clusters

THE last decade has seen a dramatic increase in the deployment of heterogeneous distributed computing platforms, in particular, those consisting of heterogeneous clusters, and multiple heterogeneous collections of clusters aggregated over wide-area networks into grids. The software infrastructures and mechanisms to deploy such platforms have been well studied and implementations are already used in production, so that heterogeneous platforms represent a significant, and growing, fraction of the computational power delivered by parallel platforms today. In spite of these successes, many research challenges remain, including those pertaining to distributed algorithms and scheduling algorithms, which are critical for ensuring that these platforms are used effectively. In this context, the goal of this special section on “Algorithm Design and Scheduling Techniques (Realistic Platform Models) for Heterogeneous Clusters” is to gather papers that further our understanding of the impact of platform heterogeneity on the design and evaluation of new such algorithms. In the paper entitled “Allocating Non-Real-Time and Soft Real-Time Jobs in Multiclusters,” Ligang He, Stephen A. Jarvis, Daniel P. Spooner, Hong Jiang, Donna N. Dillenberger, and Graham R. Nudd introduce two workload allocation strategies for large-scale heterogeneous platforms. The first strategy achieves an optimized mean response time for jobs having no real-time requirements. The second strategy obtains an optimized mean miss rate for jobs having soft real-time requirements (i.e., a fraction of jobs are permitted to miss the real-time constraints). Both strategies take into account average system behaviors (such as the mean arrival rate of jobs) to calculate the workload proportions for individual clusters, and update on-the-fly the workload allocation when the change in the mean arrival rate reaches a certain threshold. The allocation schemes are combined with two job dispatching strategies (weighted random and weighted round-robin) to generate new job scheduling algorithms for multicluster environments. In their paper “On the Distribution of Sequential Jobs in Random Brokering for Heterogeneous Computational Grids,” Vandy Berten, Joel Goossens, and Emmanuel Jeannot study resource brokering for scheduling sequential jobs onto a grid platform that consists of heterogeneous sets of homogeneous processors, such as a set of clusters. Resources in each cluster are managed by a local scheduler that maintains a job queue. The paper studies a centralized “metascheduler” that uses a randomized strategy to share available resources among competing jobs. This research considers two cases depending on whether the platform is heavily loaded or lightly loaded. For each case, it obtains both analytical and experimental characterizations of the queue lengths at each local scheduler, CPU utilization, and average job slowdowns. Furthermore, the paper presents a discussion of the system’s behavior when it transitions between a heavily loaded state and a lightly loaded one. All presented theoretical results are corroborated by simulations and provide a thorough description of randomized resource brokering. The research in “Multiple Job Scheduling in a Connection-Limited Data Parallel System” presents a new method for scheduling jobs in a distributed system where the critical resource is the bandwidth to access the stored data. The authors, Alessandro Amoroso and Keith Marzullo, describe an approach that supports the master-worker scheme and can be applied to data parallel computation. They consider a typical wide-area data grid that is comprised of a set of sites, where each site has one or more local area networks. The platform model used is based on the Nile data grid. This paper uses a set of synthetic jobs to compare three schedulers: Greedy, Maxfow, and Hybrid. They tested their new approach under various circumstances and measured its performance by means of several metrics. The new Hybrid scheduler is never worse than either of the other two schedulers, and in 20 percent of the simulated runs, it produced runs that were at least 20 percent better. The paper entitled “Capacity-Aware Multicast Algorithms on Heterogeneous Overlay Networks,” coauthored by Zhan Zhang, Shigang Chen, Yibei Ling, and Randy Chow, addresses the problem of multicast for group IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 17, NO. 2, FEBRUARY 2006 97

PaGrid: A Mesh Partitioner for Computational Grids

Computational Grids are emerging as a new infrastructure for high performance computing. Since the resources in a Grid can be heterogeneous and distributed, mesh-based applications require a mesh partitioner that considers both processor and network heterogeneity. We have developed a heterogeneous mesh partitioner, called PaGrid. PaGrid uses a multilevel graph partitioning approach, augmented by execution time load balancing in the final uncoarsening phase. We show that minimization of total communication cost (e.g., as used by JOSTLE) can lead to significant load being placed on processors connected by slow links, which results in higher application execution times. Therefore, PaGrid balances the estimated execution time of the application across processors. PaGrid performance is compared with two existing mesh partitioners, METIS 4.0 and JOSTLE 3.0, for mapping several application meshes to two models of heterogeneous computational Grids. PaGrid is found to produce significantly better partitions than JOSTLE and slightly better partitions than METIS in most cases, in terms of estimated application execution time averaged over a large number of runs with different random number seeds.

Managing Heterogeneity in a Grid Parallel Haskell

Computational Grids potentially offer cheap large-scale high-performance systems, but are a very challenging architecture, being heterogeneous, shared and hierarchical. Rather than requiring a programmer to explicitly manage this complex environment, we recommend using a high-level parallel functional language, like gph , with largely automatic management of parallel coordination. We present GridGUM , an initial port of the distributed virtual shared-memory implementation of gph for computational grids. We show that, GridGUM delivers acceptable speedups on relatively low latency homogeneous and heterogeneous computational Grids. Moreover, we find that for heterogeneous computational grids, load management limits performance. We present the initial design of GridGUM 2, that incorporates new load management mechanisms that cheaply and effectively combine static and dynamic information to adapt to heterogeneous grids. The mechanisms are evaluated by measuring four non-trivial programs with different parallel properties. The measurements show that the new mechanisms improve load distribution over the original implementation, reducing runtime by factors ranging from 17 % to 57 %, and the greatest improvement is obtained for the most dynamic program.