Grid computing and cloud computing are two related paradigms used to access and use vast amounts of computational resources. The resources are often owned and managed by a third party, relieving the users from the costs and burdens of aquiring and man- aging a considerably large infrastructure themselves. Commonly, the resources are either contributed by different stakeholders participating in shared projects (grids), or owned and managed by a single entity and made available to the public by charging the users for the resources they consume (clouds). Individual grid or cloud sites can form collaborations with other sites, giving each site access to more resources that can be used to execute tasks submitted by users. There are several different models of collaborations between sites, each suitable for different scenarios and each posing additional requirements on the underlying technologies.

Metadata concering the status and resource consumption of tasks are created dur- ing the execution of the task on the infrastructure. This metadata is used as the primary input in many core managment processes, e.g., as a base for accounting and billing, as input when prioritizing and placing incoming task, and as a base for managing the amount of resources allocated to different tasks.

Focusing on management and utilization of metadata, this thesis contributes to a better understanding of the requirements and challenges imposed by different collab- oration models in both grids and clouds. The underlying design criteria and resulting architectures of several software systems are presented in detail. Each system ad- dresses different challenges imposed by cross-site grid and cloud architectures:

• The LUTSfed approach provides a lean and optional mechanism for filtering and management of usage data between grid or cloud sites.

• An accounting and billing system natively designed to support cross-site clouds demonstrates usage data management despite unknown placement and dynamic task resource allocation.

• The FSGrid system enables fairshare job prioritization across different grid sites, mitigating the problems of heterogeneous scheduling software and local management policies.

The results and experiences from these systems are not only theoretical, as full scale implementations of each system has been developed and analyzed as a part of this work. Early theoretical work on structure-based service management forms a fundation for future work on structured-aware service placement in cross-site clouds.