The Grid encompasses two areas of distributed system activity. One is operational with an objective of administrating and managing an interoperable collection of distributed compute resource clusters on which to execute client jobs, typically scientific/ HPC applications.

The procedures and protocols required to support clients from complex services built on distributed components that handle job submission, security, machine provisioning, and data staging. The Cloud has similar operational requirements for supporting complex services to provide clients with services on different levels of support such application, platform and infrastructure.

The Grid also represents as a coherent entity a collection of compute resources that may be under different administrative domains, such as universities, but inter-operate transparently to form virtual organizations.

Although interoperability is not a near term priority, there is a precedent for commercial Clouds to move in this direction similarly to how utilities such as power or communication contract with their competitors to provide overflow capacity.

The second aspect of distributed computing in the Grid is that job themselves are distributed, typically running on tightly coupled nodes within a cluster and leveraging middle ware services such as MPICH. Jobs running in the Grid are not typically interactive, and some may be part of more complex services such as e-science work flows.

Workloads in Clouds usually consist of more loosely coupled distributed jobs such as map/reduce, and HPC jobs written to minimize internode communication and leverage concurrency provided by large multi-core nodes.

Service instances that form components of a larger business process work flow are likely to be deployed in the Cloud. These workload aspects of jobs running in the Cloud or Grid have implications for structuring the services that administer and manage the quality of their execution.

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