The Computational Grid is a promising platform for the efficient execution of parameter sweep applications over large parameter spaces. To achieve performance on the Grid, such applications must be scheduled so that shared data files are strategically placed to maximize reuse, and so that the application execution can adapt to the deliverable performance potential of target heterogeneous, distributed and shared resources. Parameter sweep applications are an important class of applications and would greatly benefit from the development of Grid middleware that embeds a scheduler for performance and targets Grid resources transparently. In this paper we describe a user-level Grid middleware project, the AppLeS Parameter Sweep Template (APST), that uses application-level scheduling techniques [1] and various Grid technologies to allow the efficient deployment of parameter sweep applications over the Grid. We discuss...

The Computational Grid provides a promising platform for the efficient execution of parameter sweep applications over very large parameter spaces. Scheduling such applications is challenging because target resources are heterogeneous, because their load and availability varies dynamically, and because independent tasks may share common data files. In this paper, we propose an adaptive scheduling algorithm for parameter sweep applications on the Grid. We modify standard heuristics for task/host assignment in perfectly predictable environments (Max-min, Min-min, Sufferage), and we propose an extension of Sufferage called XSufferage. Using simulation, we demonstrate that XSufferage can take advantage of file sharing to achieve better performance than the other heuristics. We also study the impact of inaccurate performance prediction on scheduling. Our study shows that: (i) different heuristics behave differently when predictions are inaccurate; (ii) increased adaptivity leads to better performance.

While distributed, heterogeneous collections of computers ("Grids") can in principle be used as a computing platform, in practice the problems of first discovering and then configuring resources to meet application requirements are difficult problems. We present a general-purpose resource selection framework that addresses these problems by defining a resource selection service for locating Grid resources that match application requirements. At the heart of this framework is a simple but powerful declarative language based on a technique called set matching, which extends the Condor matchmaking framework to support both single resource and multiple resource selection. This framework also provides an open interface for loading application-specific mapping modules to personalize the resource selector. We present results obtained when this framework is applied in the context of a computational astrophysics application, Cactus. These results demonstrate the effectiveness of our technique.

There is a current need for scheduling policies that can leverage the performance variability of resources on multiuser clusters. We develop one solution to this problem called stochastic scheduling that utilizes a distribution of application execution performance on the target resources to determine a performance-efficient schedule. In this paper, we define a stochastic scheduling policy based on time-balancing for data parallel applications whose execution behavior can be represented as a normal distribution. Using three distributed applications on two contended platforms, we demonstrate that a stochastic scheduling policy can achieve good and predictable performance for the application as evaluated by several performance measures.

this paper describes how VNET works and presents performance results for local-area and widearea use. VNET is publicly available from us

Desktop resources are attractive for running computeintensive distributed applications. Several systems that aggregate these resources in desktop grids have been developed. While these systems have been successfully used for many high throughput applications there has been little insight into the detailed temporal structure of CPU availability of desktop grid resources. Yet, this structure is critical to characterize the utility of desktop grid platforms for both task parallel and even data parallel applications. We address the following questions: (i) What are the temporal characteristics of desktop CPU availability in an enterprise setting? (ii) How do these characteristics affect the utility of desktop grids? (iii) Based on these characteristics, can we construct a model of server "equivalents" for the desktop grids, which can be used to predict application performance ? We present measurements of an enterprise desktop grid with over 220 hosts running the Entropia commercial desktop grid software. We utilize these measurements to characterize CPU availability and develop a performance model for desktop grid applications for various task granularities, showing that there is an optimal task size. We then use a cluster equivalence metric to quantify the utility of the desktop grid relative to that of a dedicated cluster.

This paper evaluates linear models for predicting the Digital Unix five-second host load average from 1 to 30 seconds into the future. A detailed statistical study of a large number of long, fine grain load traces from a variety of real machines leads to consideration of the Box-Jenkins models (AR, MA, ARMA, ARIMA), and the ARFIMA models (due to self-similarity.) We also consider a simple windowed-mean model. The computational requirements of these models span a wide range, making some more practical than others for incorporation into an online prediction system. We rigorously evaluate the predictive power of the models by running a large number of randomized testcases on the load traces and then data-mining their results. The main conclusions are that load is consistently predictable to a very useful degree, and that the simple, practical models such as AR are sufficient for host load prediction. We recommend AR(16) models or better for host load prediction. We implement an online host load prediction system around the AR(16) model and evaluate its overhead, finding that it uses miniscule amounts of CPU time and network bandwidth

Abstract not found

Abstract—RPS is a publicly available toolkit that allows a practitioner to straightforwardly create flexible online and offline resource prediction systems in which resources are represented by independent, periodically sampled, scalar-valued measurement streams. The systems predict the future values of such streams from past values and are composed at runtime out of a large and extensible set of communicating components that are in turn constructed using RPS’s extensible sensor, prediction, wavelet, and communication libraries. This paper describes the design, implementation, and performance of RPS. We have used RPS extensively to evaluate predictive models and build online prediction systems for host load, Windows performance data, and network bandwidth. The computation and communication overheads involved in such systems are quite low. Index Terms—Distributed systems, performance of systems. æ 1

This paper evaluates linear models for predicting the Digital Unix five-second load average from 1 to 30 seconds into the future. A detailed statistical study of a large number of load traces leads to consideration of the Box-Jenkins models (AR, MA, ARMA, ARIMA), and the ARFIMA models (due to self-similarity.) These models, as well as a simple windowed-mean scheme, are evaluated by running a large number of randomized testcases on the load traces. The main conclusions are that load is consistently predictable to a useful degree, and that the simpler models such as AR are sufficient for doing this prediction.