7. Grid Computing Systems and Resource Management

1. 7. Grid Computing Systems and Resource Management 7.1 Grid Architecture and Service Modeling • The grid is a metacomputing infrastructure that brings together computers to form a large collection of compute, storage, and network resources to solve large-scale computation problems or to enable fast information retrieval by registered users or user group. • The coupling between hardware and software with special user applications is achieved by leasing the hardware, software, middleware, databases, instruments, and networks as computing utilities. • The goal of grid computing is to explore fast solutions for large-scale computing problems. Grid computing takes advantage of the existing computing resources scattered in a nation or internationally around the globe. 7.1.1 Grid History and Service Families Grid Computings-1

2. The idea of the grid was pioneered by Ian Foster, Carl Kesselman and Steve Tuecke in 2001 paper [16]. • The Globus Project supported by DARPA has promoted the maturity of grid technology with rich collection of software and middleware tools for grid computing. • Gids differ from conventional HPC clusters. • Cluster nodes are more homogenous machines that are better coordinated to work collectively and cooperatively. • The grid nodes are heterogeneous computers that are more loosely couples together over geographically dispersed sites. Grid Computings-1

3. Four Grid Service Families • Table 7.1 Four Grid Families Identified in the Great Global Grid (GGG) • Grid Service Protocol Stack • Fig. 7.1 The layered grid service protocol and their relationship with the Internet service protocols. • Grid Resource • Table 7.2 summaries typical resources that are required to perform grid computing. 7.1.2 CPU Scavenging and Virtual Supercomputers • CPU Scavenging and Virtual Supercomputers • The concept of creating a “grid” from unused resources in a network of computers is known as CPU scavenging. • The most famous simple is the SETI@Home, which applied over 3 million computers to achieve 23.37 Tflops as of Sept. 2001. • Grid Resource Aggregation • Several assumptions • The compute nodes and other necessary resources for grids do not join or leave the system incidentally, except when some serious faults occur in the grid. Grid Computings-1

4. Cloud resources are mostly provisioned from large data centers. • The joining or leaving of some peers has little impact on the needed functions of a P2P grid system. • The availability and specification of these open resources is provided by Grid Information Service (GIS) agencies. • Virtual Organization • The grid is a distributed system integrated from shared resources to form a virtual organization (VO). The VO offers dynamic cooperation built over multiple physical organizations. • The virtual resources contributed by these real organizations are managed autonomously. • Figure 7.2 7.1.3 Open Grid Services Architecture (OGSA) • The OGSA is an open source grid service standard jointly developed by academia and the IT industry under coordination of a working group in the Global Grid Forum (GGF). • OGSA Framework • The OGSA was built on two basic software technologies: Grid Computings-1

5. The Globus Toolkit widely adopted as grid technology solution for scientific and technical computing. • Web services (WS 2.0) as a popular standards-based framework for business and network applications. • The OGSA is intended to support the creation, termination, management, and invocation of stateful, transient grid services via standard interfaces and conventions. • The OGSA framework specifies the physical environment, security, infrastructure profile, resource provisioning, virtual domains, and execution environment for various grid services and API access tools. • OGSA Interfaces • Grid services demand special well-defined application interfaces. • These interfaces provide resource discovery, dynamic service creation, lifetime management, notification, and manageability. • Grid Service Handle • The OGSA employs a “handle-resolution” mechanism for mapping a GSH to a GSR (Grid Service Reference). • Grid Service Migration • Example 7.3 Grid Service Migration Using GSH and GSR Grid Computings-1

6. 7.1.4 Data-Intensive Grid Service Models • Data Replication and Unified Namespace • This data access method is also known as caching, which is often applied to enhance data efficiency in a grid environment. • By replicating the same data blocks and scattering them in multiple regions of a grid, users can access the same data with locality of references. • Grid Data Access Models • Four access models for organizing a data grid • Monadic model: is a centralized data repository model. • Hierarchical model: is suitable for building a large data grid which has only one large data access directory. • Federation model: is better suited for designing a data grid with multiple sources of data supplies. • Hybrid model: combines the best features of a hierarchical and mesh models. • Parallel vs. Striped Data Transfer • Parallel data transfer opens multiple data streams for passing subdivided segments of a file simultaneously. • In striped data transfer, a data object is partitioned into a number of sections, and each section is placed in an individual site in a data grid. Grid Computings-1

7. 7.2 Grid Projects and Grid System Built 7.2.1 National Grids and International Projects • Table 7.4 Some National Grid Projects in Five Countries • Table 7.5 International Grid Projects Grid Computings-1

8. 7.2.2 NSF TeraGrid in the US • TeraGrid is an open scientific discovery infrastructure combining leadership-class resources at 11 partner sites to create an integrated, persistent computational resource in the USA. • TeraGrid resources are integrated through SOA. Grid Computings-1

9. 7.2.3 DataGrid in the European Union • Fig. 7.8 Hierarchical data distribution of the European Data Grid (EDG). Grid Computings-1