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this paper, we outline its design and detail the predictive performance of the forecasts it generates. While the forecasting methods are general, we focus on their ability to predict the TCP/IP end-to-end throughput and latency that is attainable by an application using systems located at different sites. Such network forecasts are needed both to support scheduling [5], and by the metacomputing software infrastructure to develop quality-of-service guarantees [10, 17]. Keywords: scheduling, metacomputing, quality-of-service, statistical forecasting, network performance monitoring

The Network Weather Service is a generalizable and extensible facility designed to provide dynamic resource performance forecasts in metacomputing environments. In this paper, we outline its design and detail the predictive performance of the forecasts it generates. While the forecasting methods are general, we focus on their ability to predict the TCP/IP end-to-end throughput and latency that is attainable by an application using systems located at different sites. Such network forecasts are needed both to support scheduling [5], and by the metacomputing software infrastructure to develop quality-of-service guarantees [10, 17]. Keywords: scheduling, metacomputing, quality-ofservice, statistical forecasting, network performance monitoring 1. Introduction As network technology advances, the resulting improvements in interprocess communication speeds make it possible to use interconnected but separate computer systems as a high-performance computational platform or metacomputer. Effect...

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

Ensembles of distributed, heterogeneous resources, also known as Computational Grids are emerging as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic Grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in dynamic, heterogeneous, multi-user Grid environments. In this paper, we discuss the AppLeS project and outline our results.

In this paper, we investigate G-commerce — computational economies for controlling resource allocation in Computational Grid settings. We define hypothetical resource consumers (representing users and Grid-aware applications) and resource producers (representing resource owners who “sell ” their resources to the Grid). We then measure the efficiency of resource allocation under two different market conditions: commodities markets and auctions. We compare both market strategies in terms of price stability, market equilibrium, consumer efficiency, and producer efficiency. Our results indicate that commodities markets are a better choice for controlling Grid resources than previously defined auction strategies. 1

In this paper we describe the design and implementation of a system called the Network Weather Service (NWS) that takes periodic measurements of deliverable resource performance from distributed networked resources, and uses numerical models to dynamically generate forecasts of future performance levels. These performance forecasts, along with measures of performance fluctuation (e.g. the mean square prediction error) and forecast lifetime that the NWS generates, are made available to schedulers and other resource management mechanisms at runtime so that they may determine the quality-of-service that will be available from each resource. We describe the architecture of the NWS and implementations that we have developed and are currently deploying for the Legion [13] and Globus/Nexus [7] metacomputing infrastructures. We also detail NWS forecasts of resource performance using both the Legion and Globus/Nexus implementations. Our results show that simple forecasting techniques substanti...

this paper, we focus on the problem of making short and medium term forecasts of CPU availability on time-shared Unix systems. We evaluate the accuracy with which availability can be measured using Unix load average, the Unix utility vmstat, and the Network Weather Service CPU sensor that uses both. We also examine the autocorrelation between successive CPU measurements to determine their degree of self-similarity. While our observations show a long-range autocorrelation dependence, we demonstrate how this dependence manifests itself in the short and medium term predictability of the CPU resources in our study.

In this chapter, we describe the use of economic principles as the basis for Grid resource allocation policies and mechanisms. A computational economy in which users “buy ” resources from their owners is an attractive method of controlling Grid resource allocation for several reasons. Economies are intuitively easy to understand, they fit the model of flexible resource usage under local control (which is fundamental to Grid computing), and they can be analyzed through a considerable body of extant theory. We discuss many of the fundamental characteristics of computational economies, particularly as they pertain to Grid computing. We also present G-commerce — a framework that we have used to investigate Grid resource economies — as an example of the type of results that are possible. Finally, we discuss several of the issues associated with empirical investigation of Grid economies as a motivation for future work. 1

In this paper we propose an adaptive scheduling approach designed to improve the performance of parallel applications in Computational Grid environments. A primary contribution of our work is that our design is modular and provides a separation of the scheduler itself from the application-specific components needed for the scheduling process. As part of the scheduler, we have also developed a search procedure which effectively and efficiently identifies desirable schedules. As test cases for our approach, we selected two applications from the class of iterative, mesh-based applications. For each of the test applications, we developed data mappers and performance models. We used a prototype of our approach in conjunction with these application-specific components to perform validation experiments in production Grid environments. Our results show that our scheduler provides significantly better application performance than conventional scheduling strategies. We also show that our scheduler gracefully handles degraded levels of availability of application and Grid resource information. Finally, we demonstrate that the overheads introduced by our methodology