Arash Baratloo

Charlotte: metacomputing on the Web

Abstract Parallel computing on local area networks is generally based on mechanisms that specifically target the properties of the local area network environment. However, these mechanisms do not effectively extend to wide area networks due to issues such as heterogeneity, security, and administrative boundaries. We present a system which enables application programmers to write parallel programs in Java and allows Java-capable browsers to execute parallel tasks. It comprises a virtual machine model which isolates the program from the execution environment, and a runtime system realizing this virtual machine on the Web. Load balancing and fault masking are transparently provided by the runtime system.

Metacomputing with MILAN

The MILAN project, a joint effort involving Arizona State University and New York University, has produced and validated fundamental techniques for the realization of efficient reliable, predictable virtual machines, that is, metacomputers, on top of environments that consist of an unreliable and dynamically changing set of machines. In addition to the techniques, the principal outcomes of the project include three parallel programming systems-Calypso, Chime, and Charlotte-which enable applications to be developed for ideal, shared memory, parallel machines to execute on distributed platforms that are subject to failures, slowdowns, and changing resource availability. The lessons learned from the MILAN project are being used to design Computing Communities, a metacomputing framework for general computations.

Mechanisms for just-in-time allocation of resources to adaptive parallel programs

Adaptive parallel computations-computations that can adapt to changes in resource availability and requirement-can effectively use networked machines because they dynamically expand as machines become available and dynamically acquire machines as needed. While most parallel programming systems provide the means to develop adaptive programs, they do not provide any functional interface to external resource management systems. Thus no existing resource management system has the capability to manage resources on commodity system software, arbitrating the demands of multiple adaptive computations written using diverse programming environments. This paper presents a set of novel mechanisms that facilitate dynamic allocation of resources to adaptive parallel computations. The mechanisms are built on low-level features common to many programming systems, and unique in their ability to transparently manage multiple adaptive parallel programs that were not developed to have their resources managed by external systems. We also describe the design and the implementation of the initial prototype of ResourceBroker, a resource management system built to validate these mechanisms.