Rajkumar Buyya defines grid computing as follows: Grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed ‘autonomous’ resources dynamically at runtime depending on their availability, capability, performance, cost, and users’ quality-of-service requirements…It should be noted that Grids aim at exploiting synergies that result from cooperation–ablity to share and agreegrate distributed computational capabilities and deliver them as service…The key distinction between clusters and grids is mainly lie in the way resources are managed. In case of clusters, the resource allocation is performed by a centralised resource manager and all nodes cooperatively work together as a single unified resource. In case of Grids, each node has its own resource manager and don’t aim for providing a single system view.
A 2000 paper entitled The Anatomy of the Grid: Enabling Scalable Virtual Organisations by Ian Foster, Carl Kesselman and Steven Tuecke defined the field of grid computing:
The real and specific problem that underlies the Grid concept is coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations. The sharing that weare concerned with is not primarily file exchange but rather direct access to computers, software, data, and other resources, as is required by a range of collaborative problem-solving and resource brokering strategies emerging in industry, science, and engineering. This sharing is, necessarily, highly controlled, with resource providers and consumers defining clearly and carefully just what is shared, who is allowed to share, and the conditions under which sharing occurs. A set of individuals and/or institutions defined by such sharing rules form what we call a virtual organization (VO).
The following are examples of VOs: the application service providers, storage service providers, cycle providers, and consultants engaged by a car manufacturer to perform scenario evaluation during planning for a new factory; members of an industrial consortium bidding on a new aircraft; a crisis management team and the databases and simulation systems that they use to plan
a response to an emergency situation; and members of a large, international, multiyear high energy physics collaboration. Each of these examples represents an approach to computing and problem solving based on collaboration in computation- and data-rich environments.
As these examples show, VOs vary tremendously in their purpose, scope, size, duration, structure, community, and sociology. Nevertheless, careful study of underlying technology requirements leads us to identify a broad set of common concerns and requirements. In particular, we see a need for highly flexible sharing relationships, ranging from client-server to peer-to-peer; for sophisticated and precise levels of control over how shared resources are used, including fine-grained and multi-stakeholder access control, delegation, and application of local and global policies; for sharing of varied resources, ranging from programs, files, and data to computers, sensors, and networks; and for diverse usage modes, ranging from single user to multi-user and from performance sensitive to cost-sensitive and hence embracing issues of quality of service, scheduling, co-allocation, and accounting.
Tomorrow: Grid Computing (continued)
TECH TALK CommPuting Grid+T