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The realization of end-to-end quality of service (QoS) guarantees in emerging network-based applications requires mechanisms that support first dynamic discovery and then advance or immediate reservation of resources that will often be heterogeneous in type and implementation and independently controlled and administered. We propose the Globus Architecture for Reservation and Allocation (GARA) to address these four issues. GARA treats both reservations and computational elements such as processes, network flows, and memory blocks as first class entities, allowing them to be created, monitored, and managed independently and uniformly. It simplifies management of heterogeneous resource types by defining uniform mechanisms for computers, networks, disk, memory, and other resources. Layering on these standard mechanisms, GARA enables the construction of application-level co-reservation and coallocation libraries that applications can use to dynamically assemble collections of resources, guided by both application QoS requirements and the local administration policy of individual resources. We describe a prototype GARA implementation that supports three different resource typesparallel computers, individual CPUs under control of the Dynamic Soft Real-Time scheduler, and Integrated Services networks-and provide performance results that quantify the costs of our techniques.

Abstract-Reservation and adaptation are two well-known and effective techniques for enhancing the end-to-end performance of network applications. However, both techniques also have limitations, particularly when dealing with high-bandwidth, dynamic flows: fixed-capability reservations tend to be wasteful of resources and hinder graceful degradation in the face of congestion, while adaptive techniques fail when congestion becomes excessive. We propose an approach to quality of service (QoS) that overcomes these difficulties by combining features of reservations and adaptation. In this approach, a combination of online control interfaces for resource management, a sensor permitting online monitoring, and decision procedures embedded in resources enable a rich variety of dynamic feedback interactions between applications and resources. We describe a QoS architecture, GARA, that has been extended to support these mechanisms, and use three examples of application-level adaptive strategies to show how this framework can permit applications to adapt both their resource requests and behavior in response to online sensor information.

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