Abstract not found

ASSIST (A Software development System based upon Integrated Skeleton Technology) is a programming environment oriented to the development of parallel and distributed high-performance applications according to a unified approach. The language and implementation features of ASSIST are a result of our long-term research in parallel programming models and tools. ASSIST is evolving towards programming environments for high-performance complex enabling platforms, especially Grids. In this paper, we show how ASSIST can act as a valid research vehicle to study, experiment and realize Grid-aware programming environments for high-performance applications. Special emphasis is put on the innovative methodologies, strategies and tools for dynamically adaptive applications, that represent the necessary step for the success of Grid platforms. First we discuss the conceptual framework for Grid-aware programming environments, based upon structured parallel programming and components technology, anticipating how ASSIST possesses the essential features required by

Abstract—In scheduling a large number of user jobs for parallel execution on an open-resource Grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. This paper models the risk and insecure conditions in Grid job scheduling. Three risk-resilient strategies, preemptive, replication, and delay-tolerant, are developed to provide security assurance. We propose six risk-resilient scheduling algorithms to assure secure Grid job execution under different risky conditions. We report the simulated Grid performances of these new Grid job scheduling algorithms under the NAS and PSA workloads. The relative performance is measured by the total job makespan, Grid resource utilization, job failure rate, slowdown ratio, replication overhead, etc. In addition to extending from known scheduling heuristics, we developed a new space-time genetic algorithm (STGA) based on faster searching and protected chromosome formation. Our simulation results suggest that, in a wide-area Grid environment, it is more resilient for the global job scheduler to tolerate some job delays instead of resorting to preemption or replication or taking a risk on unreliable resources allocated. We find that delay-tolerant Min-Min and STGA job scheduling have 13-23 percent higher performance than using risky or preemptive or replicated algorithms. The resource overheads for replicated job scheduling are kept at a low 15 percent. The delayed job execution is optimized with a delay factor, which is 20 percent of the total makespan. A Kiviat graph is proposed for demonstrating the quality of Grid computing services. These riskresilient job scheduling schemes can upgrade Grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky Grid computing environment. Index Terms—Grid computing, job scheduling heuristics, genetic algorithm, replication scheduling, risk resilience, NAS and PSA benchmarks, performance metrics, distributed supercomputing. 1

In this paper, our contributions are two-fold: First, we enhance the Min-Min and Sufferage heuristics under three risk modes driven by security concerns. Second, we propose a new Space-Time Genetic Algorithm (STGA) for trusted job scheduling, which is very fast and easy to implement. Under our new model, a job can possibly fail if the site security level is lower than the job security demand. We consider three security-driven heuristic modes: secure, risky, and f -risky. The secure mode always dispatches jobs to secure sites meeting the job security demands. The risky mode allocates jobs to any available resource site, taking whatever the risk it may face. The f -risky mode tries to limit the risk to be at most certain probability f . Our extensive simulation results indicated that the proposed STGA is highly effective in scheduling two types of practical workloads: NAS (Numerical Aerodynamic Simulation) and PSA (parametersweep application). The STGA outperforms the Min-Min and Sufferage heuristics under three risk modes, in terms of a wide range of performance metrics including makespan, average response time, site utilization, slowdown ratio, and job failure rate.

processing on computational grids

Developing grid applications is a challenging endeavor, which at the moment requires both extensive labor and expertise. The Grid Application Development Software Project (GRADS) provides a system to simplify grid application development. This system incorporates tools at all stages of the application development and execution cycle. In this chapter we focus on application scheduling, and present the three scheduling approaches developed in GRADS: development of an initial application schedule (launch-time scheduling), modification of the execution platform during execution (rescheduling), and negotiation between multiple applications in the system (metascheduling). These approaches have been developed and evaluated for platforms that consist of distributed networks of shared workstations, and applied to real-world parallel applications.

In multicluster systems, and more generally in grids, jobs may require co-allocation, that is, the simultaneous or coordinated access of single applications to resources of possibly multiple types in multiple locations managed by different resource managers. Co-allocation presents new challenges to resource management in grids, such as locating sufficient resources in geographically distributed sites, allocating and managing resources in multiple, possibly heterogeneous sites for single applications, and coordinating the execution of single jobs at multiple sites. Moreover, as single jobs now may have to rely on multiple resource managers, co-allocation introduces reliability problems. In this paper, we present the design and implementation of a co-allocating grid scheduler named KOALA that meets these co-allocation challenges. In addition, we report on the results of an analysis of the performance in our multicluster testbed of the co-allocation policies built into KOALA. We also include the results of a performance and reliability test of

Abstract. The goal of the Encyclopedia of Life (EOL) Project is to predict structural information for all proteins, in all organisms. This calculation presents challenges both in terms of the scale of the computational resources required (approximately 1.8 million CPU hours), as well as in data and workflow management. While tools are available that solve some subsets of these problems, it was necessary for us to build software to integrate and manage the overall Grid application execution. In this paper, we present this workflow system, detail its components, and report on the performance of our initial prototype implementation for runs over a large-scale Grid platform during the SC’03 conference. 1

The Grid has the potential to grow significantly over the course of the next decade and therefore the mechanisms that make the Grid possible need to become more efficient in order for the Grid to scale. One of these mechanisms revolves around resource management; ultimately, there will be so many resources in the Grid, that if they are not managed properly, only a very small fraction of those resources will be utilized. While good resource utilization is very important, it is also a hard problem due to widely distributed dynamic environments normally found in the Grid. It is important to develop an experimental methodology for automatically characterizing grid software in a manner that allows accurate evaluation of the software’s behavior and performance before deployment in order to make better informed resource management decisions. Many Grid services and software are designed and characterized today largely based on the designer’s intuition and on ad hoc experimentation; having the capability to automatically map complex, multi-dimensional requirements and performance data among resource providers and consumers is a necessary step to ensure consistent good resource utilization in the Grid. This automatic matching between the software characterization and a set of raw or logical resources is a much needed functionality that is currently lacking in today’s Grid resource management infrastructure. Ultimately, my proposed work, which addresses performance modeling with the goal to improve resource management, could ensure that the efficiency of

In multicluster grid systems, parallel applications may benefit from processor co-allocation, that is, the simultaneous allocation of processors in multiple clusters. Although co-allocation allows the allocation of more processors than available in a single cluster, it may severely increase the execution time of applications due to the relatively slow widearea communication. The aim of this paper is to investigate the benefit of co-allocation in multicluster grid systems, despite this drawback. To this end, we have conducted experiments in a real multicluster grid environment, as well as in a simulated environment, and we evaluate the performance of co-allocation for various applications that range from computation-intensive to communication-intensive and for various system load settings. In addition, we compare the performance of scheduling policies that are specifically designed for co-allocation. We demonstrate that considering latency in the resource selection phase improves the performance of co-allocation, especially for communicationintensive parallel applications.