Metacomputing is the aggregation of distributed and high-performance resources on coordinated networks. With careful scheduling, resource-intensive applications can be implemented efficiently on metacomputing systems at the sizes of interest to developers and users. In this paper, we focus on the problem of scheduling applications on metacomputing systems. We introduce the concept of application-centric scheduling in which everything about the system is evaluated in terms of its impact on the application. Application-centric scheduling is used by virtually all metacomputer programmers to achieve performance on metacomputing systems. We describe two successful metacomputing applications to illustrate this approach, and describe AppLeS scheduling agents which generalize the application-centric scheduling approach. Finally, we show preliminary results which compare AppLeS-derived schedules with conventional strip and blocked schedules for a two-dimensional Jacobi code. 1 Introduction Inc...

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.

Computational Grids are becoming an increasingly important and powerful platform for the execution of largescale, resource-intensive applications. However, it remains a challenge for applications to tap into the potential of Grid resources in order to achieve performance. In this paper, we illustrate how work queue applications can leverage Grids to achieve performance through coallocation. We describe our experiences developing a scheduling strategy for a production tomography application targeted to Grids that contain both workstations and parallel supercomputers. Our strategy uses dynamic information exported by a supercomputer's batch scheduler to simultaneously schedule tasks on workstations and immediately available supercomputer nodes. This strategy is of great practical interest because it combines resources available to the typical research lab: time-shared workstations and CPU time in remote space-shared supercomputers. We show that this strategy improves the performance of ...

Heterogeneous network computing allows the development of a single complex application using a distributed network of machines; these machines may differ in terms of CPU and memory capacity and/or architecture and specialized functions. The design of efficient applications targeted to this class of systems is a complex task requiring the evaluation of different performance tradeoffs. In this paper we present a modeling technique, based on Generalized Stochastic Petri Nets (GSPNs), for the performance analysis of heterogeneous applications. The technique identifies the features that affect the performance of the application; these features are modeled in isolation by GSPN submodels. The model for a given application is obtained by composing the submodels corresponding to the particular features exposed by the application. We illustrate the use of the proposed technique by modeling the CASA 3D-REACT heterogeneous application (which is one of the applications belonging to the CASA Gigabit...

Heterogeneous network computing is defined as the implementation of a large, complex application on a network of possibly diverse computers. With the increase in network communication speeds and the availability of fast workstations and multiprocessors, heterogeneous network computing is emerging as a viable option for the development of performance-efficient applications. In this paper, we describe Zoom, a hierarchical representation in which heterogeneous applications can be described. The goal of Zoom is to provide an abstraction that computer and computational scientists can use to describe heterogeneous applications, and to provide a foundation from which program development tools for heterogeneous network computing can be built. Three levels (structure, implementation and data) of the Zoom hierarchy are described and are used to illustrate two heterogeneous applications. Extensions to Zoom to include additional resource parameters required by program development tools are also di...