<p>During the past decade, Grid computing has gained popularity as a means to build powerful computing infrastructures by aggregating distributed computing capacity. Grid technology allows computing resources that belong to different organizations to be integrated into a single unified system image – a Grid. As such, Grid technology constitutes a key enabler of large-scale, crossorganizational sharing of computing resources. An important objective for the Virtual Organizations (VOs) that result from such sharing is to tame the distributed capacity of the Grid in order to manage it and make fair and efficient use of the pooled computing resources.</p><p>Most Grids to date have, however, been completely unregulated, essentially serving as a “source of free CPU cycles” for authorized Grid users. Whenever unrestricted access is admitted to a shared resource there is a risk of overexploitation and degradation of the common resource, a phenomenon often referred to as “the tragedy of the commons”. This thesis addresses this problem by presenting two complementary Grid capacity allocation systems that allow the aggregate computing capacity of a Grid to be divided between users in order to protect the Grid from overuse while delivering fair service that satisfies the individual computational needs of different user groups.</p><p>These two Grid capacity allocation mechanisms constitute the core contribution of this thesis. The first mechanism, the SweGrid Accounting System (SGAS), addresses the need for coordinated soft, real-time quota enforcement across Grid sites. The SGAS project was an early adopter of the serviceoriented principles that are now common practice in the Grid community, and the system has been tested in the Swegrid production environment. Furthermore, SGAS has been included in the Globus Toolkit, the de-facto standard Grid middleware toolkit. SGAS employs a credit-based allocation model where research projects are granted quota allowances that can be spent across the Grid resources, which charge users for their resource consumption. This enforcement of usage limits thus produces real-time overuse protection.</p><p>The second approach, employed by the Fair Share Grid (FSGrid) system, uses a share-based allocation model where project entitlements are expressed in terms of hierarchical share policies that logically divide the Grid capacity between user groups. By coordinating local job scheduling to maintain these global capacity shares, the Grid resources collectively strive to schedule users for a “share of the Grid”. We refer to this cooperative scheduling model as decentralized Grid-wide fairshare scheduling.</p>

Computing infrastructures offer remote access to computing power that can be employed, e.g., to solve complex mathematical problems or to host computational services that need to be online and accessible at all times. From the perspective of the infrastructure provider, large amounts of distributed and often heterogeneous computer resources need to be united into a coherent platform that is then made accessible to and usable by potential users. Grid computing and cloud computing are two paradigms that can be used to form such unified computational infrastructures. Resources from several independent infrastructure providers can be joined to form large-scale decentralized infrastructures. The primary advantage of doing this is that it increases the scale of the available resources, making it possible to address more complex problems or to run a greater number of services on the infrastructures. In addition, there are advantages in terms of factors such as fault-tolerance and geographical dispersion. Such multi-domain infrastructures require sophisticated management processes to mitigate the complications of executing computations and services across resources from different administrative domains. This thesis contributes to the development of management processes for distributed infrastructures that are designed to support multi-domain environments. It describes investigations into how fundamental management processes such as scheduling and accounting are affected by the barriers imposed by multi-domain deployments, which include technical heterogeneity, decentralized and (domain-wise) self-centric decision making, and a lack of information on the state and availability of remote resources. Four enabling technologies or approaches are explored and developed within this work: (I) The use of explicit definitions of cloud service structure as inputs for placement and management processes to ensure that the resulting placements respect the internal relationships between different service components and any relevant constraints. (II) Technology for the runtime adaptation of Virtual Machines to enable the automatic adaptation of cloud service contexts in response to changes in their environment caused by, e.g., service migration across domains. (III) Systems for managing meta-data relating to resource usage in multi-domain grid computing and cloud computing infrastructures. (IV) A global fairshare prioritization mechanism that enables computational jobs to be consistently prioritized across a federation of several decentralized grid installations. Each of these technologies will facilitate the emergence of decentralized computational infrastructures capable of utilizing resources from diverse infrastructure providers in an automatic and seamless manner. / <p>Note that the author changed surname from Henriksson to Espling in 2011</p>

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 acquiring and managing 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 its users with charging based on actual resource consumption (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 concerning the status and resource consumption of tasks are created during the execution of the task on the infrastructure. This metadata is used as the primary input in many core management 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 collaboration models in both grids and clouds. The underlying design criteria and resulting architectures of several software systems are presented in detail. Each system addresses 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 both theoretical and practical, 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 foundation for future work on structured-aware service placement in cross- site clouds.

During the past decade, Grid computing has gained popularity as a means to build powerful computing infrastructures by aggregating distributed computing capacity. Grid technology allows computing resources that belong to different organizations to be integrated into a single unified system image – a Grid. As such, Grid technology constitutes a key enabler of large-scale, crossorganizational sharing of computing resources. An important objective for the Virtual Organizations (VOs) that result from such sharing is to tame the distributed capacity of the Grid in order to manage it and make fair and efficient use of the pooled computing resources. Most Grids to date have, however, been completely unregulated, essentially serving as a “source of free CPU cycles” for authorized Grid users. Whenever unrestricted access is admitted to a shared resource there is a risk of overexploitation and degradation of the common resource, a phenomenon often referred to as “the tragedy of the commons”. This thesis addresses this problem by presenting two complementary Grid capacity allocation systems that allow the aggregate computing capacity of a Grid to be divided between users in order to protect the Grid from overuse while delivering fair service that satisfies the individual computational needs of different user groups. These two Grid capacity allocation mechanisms constitute the core contribution of this thesis. The first mechanism, the SweGrid Accounting System (SGAS), addresses the need for coordinated soft, real-time quota enforcement across Grid sites. The SGAS project was an early adopter of the serviceoriented principles that are now common practice in the Grid community, and the system has been tested in the Swegrid production environment. Furthermore, SGAS has been included in the Globus Toolkit, the de-facto standard Grid middleware toolkit. SGAS employs a credit-based allocation model where research projects are granted quota allowances that can be spent across the Grid resources, which charge users for their resource consumption. This enforcement of usage limits thus produces real-time overuse protection. The second approach, employed by the Fair Share Grid (FSGrid) system, uses a share-based allocation model where project entitlements are expressed in terms of hierarchical share policies that logically divide the Grid capacity between user groups. By coordinating local job scheduling to maintain these global capacity shares, the Grid resources collectively strive to schedule users for a “share of the Grid”. We refer to this cooperative scheduling model as decentralized Grid-wide fairshare scheduling.