Advances in networking technologies will soon make it possible to use the global information infrastructure in a qualitatively different way—as a computational resource as well as an information resource. This idea for an integrated computation and information resource called the Computational Power Grid has been described by the recent book entitled The Grid: Blueprint for a New Computing Infrastructure [18]. The Grid will connect the nation’s computers, databases, instruments, and people in a seamless web, supporting emerging computation-rich application concepts such as remote computing, distributed supercomputing, tele-immersion, smart instruments, and data mining. To realize this vision, significant scientific and technical obstacles must be overcome. Principal among these is usability. Because the Grid will be inherently more complex than existing computer systems, programs that execute on the Grid will reflect some of this complexity. Hence, making Grid resources useful and accessible to scientists and engineers will require new software tools that embody major advances in both the theory and practice of building Grid applications. The goal of the Grid Application Development Software (GrADS) Project is to simplify distributed heterogeneous computing in the same way that the World Wide Web simplified information sharing

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Prior work has shown the value of changing application fidelity to adapt to varying resource levels in a mobile environment. Choosing the right fidelity requires us to predict its effect on resource consumption. In this paper, we describe a history-based mechanism for such predictions. Our approach generates predictors that are specialized to the hardware on which the application runs, and to the specific input data on which it operates. We are able to predict the CPU consumption of a complex graphics application to within 20% and the energy consumption of fetching and rendering web images to within 15%.

Achieving crisp interactive response in resource-intensive applications such as augmented reality, language translation, and speech recognition is a major challenge on resource-poor wearable hardware. In this paper we describe a solution based on multi-fidelity computation supported by predictive resource management. We show that such an approach can substantially reduce both the mean and the variance of response time. On a benchmark representative of augmented reality, we demonstrate a 60 % reduction in mean latency and a 30 % reduction in the coefficient of variation. We also show that a history-based approach to demand prediction is the key to this performance improvement. 1

The most commonly used scheduling algorithm for parallel supercomputers is FCFS with backfilling, as originally introduced in the EASY scheduler. Backfilling means that short jobs are allowed to run ahead of their time provided they do not delay previously queued jobs (or at least the first queued job). To make such determinations possible, users are required to provide estimates of how long jobs will run, and jobs that violate these estimates are killed. Empirical studies have repeatedly shown that user estimates are inaccurate, and that system-generated predictions based on history may be significantly better. However, predictions have not been incorporated into production schedulers, partially due to a misconception (that we resolve) claiming inaccuracy actually improves performance, but mainly because underprediction is technically unacceptable: users will not tolerate jobs being killed just because system predic-tions were too short. We solve this problem by divorcing kill-time from the runtime prediction, and correcting predictions adaptively as needed if they are proved wrong. The end result is a surprisingly simple scheduler, which requires minimal deviations from current practices (e.g. using FCFS as the basis), and behaves exactly like EASY as far as users are concerned; nev-

The Computational Grid is a promising platform for the deployment of various high-performance computing applications. A number of projects have addressed the idea of software as a service on the network. These systems usually implement client-server architectures with many servers running on distributed Grid resources and have commonly been referred to as Network-enabled servers (NES). An important question is that of scheduling in this multi-client multi-server scenario. Note that in this context most requests are computationally intensive as they are generated by high-performance computing applications. The Bricks simulation framework has been developed and extensively used to evaluate scheduling strategies for NES systems. In this paper we first present recent developments and extensions to the Bricks simulation models. We discuss a deadline scheduling strategy that is appropriate for the multi-client multi-server case, and augment it with “Load Correction” and “Fallback ” mechanisms which could improve the performance of the algorithm. We then give Bricks simulation results. The results show that future NES systems should use deadline-scheduling with multiple fallbacks and it is possible to allow users to make a trade-off between failure-rate and cost by adjusting the level of conservatism of deadlinescheduling algorithms.

ing with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works, requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept, ACM Inc., 1515 Broadway, New York, NY 10036 USA fax +1 (212) 869-0481, or permissions@acm.org. The Purdue University Network-Computing Hubs: Running Unmodified Simulation Tools via the WWW Nirav H. Kapadia Jos'e A. B. Fortes and Mark S. Lundstrom School of Electrical and Computer Engineering, Purdue University Association for Computing Machinery, Inc., 1515 Broadway, New York, NY 10036, USA Tel: (212) 555-1212; Fax: (212) 555-2000 This paper describes the Web interface management infrastructure of a functional networkcomputingsystem (PUNCH) that allows users to run unmodified simulation packages at geographically dispersed sites. The system currently contains more than forty university and commercial simulation to...

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This paper develops a performance model that is used to control the adaptive execution the ATR code for solving large stochastic optimization problems on computational grids. A detailed analysis of the execution characteristics of ATR is used to construct the performance model that is then used to specify (a) near-optimal dynamic values of parameters that govern the distribution of work, and (b) a new task scheduling algorithm. Together, these new features minimize ATR execution time on any collection of compute nodes, including a varying collection of heterogeneous nodes. The new adaptive code runs up to eight-fold faster than the previously optimized code, and requires no input parameters from the user to guide the distribution of work. Furthermore, the modeling process led to several changes in the Condor runtime environment, including the new task scheduling algorithm, that produce significant performance improvements for master-worker computations as well as possibly other types of grid applications.

Application run-time information is a fundamental component in application and job scheduling. However, accurate predictions of run times are difficult to achieve for parallel applications running in shared environments where resource capacities can change dynamically over time. In this paper, we propose a runtime prediction technique for parallel applications that uses regression methods and filtering techniques to derive the application execution time without using standard performance models. The experimental results show that our use of regression models delivers tolerable prediction accuracy and that we can improve the accuracy dramatically by using appropriate filters. 1.