Connecting client objectives with resource capabilities: an essential component for grid service managent infrastructures

  • Authors:

  • Asit Dan;Catalin Dumitrescu;Matei Ripeanu

  • Affiliations:

  • IBM T.J. Watson Research Center, Hawthorne, NY;University of Chicago, Chicago, IL;University of Chicago, Chicago, IL

  • Venue:
  • Proceedings of the 2nd international conference on Service oriented computing
  • Year:
  • 2004

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Abstract

In large-scale, distributed systs such as Grids, an agreent between a client and a service provider specifies service level objectives both as expressions of client requirents and as provider assurances. Ideally, these objectives are expressed in a high-level, service- or application-specific manner rather than requiring clients to detail the necessary resources. Resource providers on the other hand, expect low-level, resource specific performance criteria that are uniform across applications and can easily be interpreted and provisioned. This paper presents a framework for Grid service managent that addresses this gap between high-level specification of client performance objectives and existing resource managent infrastructures It identifies three levels of abstraction for resource requirents that a service provider needs to manage, namely: detailed specification of raw resources, virtualization of heterogeneous resources as abstract resources, and performance objectives at an application level. The paper also identifies three key functions for managing service level agreents, namely: translation of resource requirents across abstraction layers, arbitration in allocating resources to client requests, and aggregation and allocation of resources from multiple lower level resource managers. One or more of these key functions may be present at each abstraction layer of a service level manager. Thus, the composition of these functions across resource abstraction layers enables modeling of a wide array of managent scenarios. We present a framework that supports these functions: it uses the service metadata and/or service performance models to map client requirents to resource capabilities, it uses business value associated with objectives in allocation decisions to arbitrate between competing requests, and it allocates resources based on previously negotiated agreents.