The document discusses the Open Grid Services Architecture (OGSA) standard. It describes OGSA's layered architecture including the physical/logical resources layer, web services layer using OGSI, OGSA services layer for core, program execution and data services, and applications layer. It also outlines the functional requirements of OGSA such as interoperability, resource sharing, optimization, quality of service, job execution, data services, security, cost reduction, scalability, and availability.
This presentation deals with how one can utilize multiple cores, while working with C/C++ applications using an API called OpenMP. It's a shared memory programming model, built on top of POSIX thread. Also the fork-join model, parallel design pattern are discussed using PetriNets.
The Open Grid Services Architecture (OGSA) defines standard approaches for common problems in grid systems, such as service communication, identity, authorization, discovery, errors, and collections. OGSA has three principal elements: the Open Grid Services Infrastructure, OGSA services, and OGSA models. Building on web services technologies, OGSA defines mechanisms for creating, managing, and exchanging information between grid services. OGSA services and models further define interfaces and behaviors for functions like discovery, data access, and monitoring.
1. Grid computing is a distributed computing approach that allows users to access computational resources over a network. It aims to dynamically allocate resources like processing power, storage, or software according to user demands.
2. Grid computing provides a utility-like model for accessing computing resources. Users can access resources from a grid in the same way users access utilities like power or water grids.
3. Key benefits of grid computing include maximizing resource utilization, providing fast and cheap computing services, and enabling collaboration through secure resource sharing across organizations. Grid computing has applications in scientific research, businesses, and e-governance.
Distributed deadlock occurs when processes are blocked while waiting for resources held by other processes in a distributed system without a central coordinator. There are four conditions for deadlock: mutual exclusion, hold and wait, non-preemption, and circular wait. Deadlock can be addressed by ignoring it, detecting and resolving occurrences, preventing conditions through constraints, or avoiding it through careful resource allocation. Detection methods include centralized coordination of resource graphs or distributed probe messages to identify resource waiting cycles. Prevention strategies impose timestamp or age-based priority to resource requests to eliminate cycles.
Load Balancing in Parallel and Distributed DatabaseMd. Shamsur Rahim
This document discusses load balancing techniques in distributed database systems. It describes different types of parallelism including inter-query, intra-query, intra-operation, and inter-operation parallelism. It also discusses problems that can occur with parallel execution such as initialization, interference, and skew. The document then focuses on techniques for load balancing within operators and between operators, including adaptive and specialized techniques. It describes how activations, activation queues, and threads can be used to improve load balancing in shared-memory systems.
This document discusses different types of file sharing semantics for shared files in distributed file systems. It describes four main types: UNIX semantics, which ensures all read operations see the effects of previous writes; immutable shared-file semantics, which treats shared files as immutable so changes create new versions; transaction-like semantics, which controls concurrent access using transactions; and session semantics, where changes in a session are only visible to the client's processes until the session closes.
This presentation deals with how one can utilize multiple cores, while working with C/C++ applications using an API called OpenMP. It's a shared memory programming model, built on top of POSIX thread. Also the fork-join model, parallel design pattern are discussed using PetriNets.
The Open Grid Services Architecture (OGSA) defines standard approaches for common problems in grid systems, such as service communication, identity, authorization, discovery, errors, and collections. OGSA has three principal elements: the Open Grid Services Infrastructure, OGSA services, and OGSA models. Building on web services technologies, OGSA defines mechanisms for creating, managing, and exchanging information between grid services. OGSA services and models further define interfaces and behaviors for functions like discovery, data access, and monitoring.
1. Grid computing is a distributed computing approach that allows users to access computational resources over a network. It aims to dynamically allocate resources like processing power, storage, or software according to user demands.
2. Grid computing provides a utility-like model for accessing computing resources. Users can access resources from a grid in the same way users access utilities like power or water grids.
3. Key benefits of grid computing include maximizing resource utilization, providing fast and cheap computing services, and enabling collaboration through secure resource sharing across organizations. Grid computing has applications in scientific research, businesses, and e-governance.
Distributed deadlock occurs when processes are blocked while waiting for resources held by other processes in a distributed system without a central coordinator. There are four conditions for deadlock: mutual exclusion, hold and wait, non-preemption, and circular wait. Deadlock can be addressed by ignoring it, detecting and resolving occurrences, preventing conditions through constraints, or avoiding it through careful resource allocation. Detection methods include centralized coordination of resource graphs or distributed probe messages to identify resource waiting cycles. Prevention strategies impose timestamp or age-based priority to resource requests to eliminate cycles.
Load Balancing in Parallel and Distributed DatabaseMd. Shamsur Rahim
This document discusses load balancing techniques in distributed database systems. It describes different types of parallelism including inter-query, intra-query, intra-operation, and inter-operation parallelism. It also discusses problems that can occur with parallel execution such as initialization, interference, and skew. The document then focuses on techniques for load balancing within operators and between operators, including adaptive and specialized techniques. It describes how activations, activation queues, and threads can be used to improve load balancing in shared-memory systems.
This document discusses different types of file sharing semantics for shared files in distributed file systems. It describes four main types: UNIX semantics, which ensures all read operations see the effects of previous writes; immutable shared-file semantics, which treats shared files as immutable so changes create new versions; transaction-like semantics, which controls concurrent access using transactions; and session semantics, where changes in a session are only visible to the client's processes until the session closes.
Open source grid middleware packages – Globus Toolkit (GT4) Architecture , Configuration – Usage of Globus – Main components and Programming model - Introduction to Hadoop Framework - Mapreduce, Input splitting, map and reduce functions, specifying input and output parameters, configuring and running a job – Design of Hadoop file system, HDFS concepts, command line and java interface, dataflow of File read & File write.
Distributed operating systems allow applications to run across multiple connected computers. They extend traditional network operating systems to provide greater communication and integration between machines on the network. While appearing like a regular centralized OS to users, distributed OSs actually run across multiple independent CPUs. Early research in distributed systems began in the 1970s, with many prototypes introduced through the 1980s-90s, though few achieved commercial success. Design considerations for distributed OSs include transparency, inter-process communication, resource management, reliability, and flexibility.
Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers - Grid computing Infrastructures – cloud computing - service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture.
Grid computing enables sharing of geographically distributed computing resources through a network. It allows for virtual organizations to collaborate on common goals without central control. The document discusses the types of grid computing including computational, data, and scavenging grids. It also outlines the key components of a grid including protocols, architecture, security, and resource management. Examples of existing grid projects are provided such as SETI@Home, EGEE, and BeINGrid.
The document discusses the five layers of the grid protocol architecture: 1) the fabric layer which provides access to different resource types, 2) the connectivity layer which defines core communication and authentication protocols, 3) the resource layer which defines protocols for publishing, discovering, and accessing individual resources, 4) the collective layer which captures interactions across collections of resources through directory services, and 5) the application layer which comprises user applications built on top of the lower layers and operate in virtual organization environments.
Provides a simple and unambiguous taxonomy of three service models
- Software as a service (SaaS)
- Platform as a service (PaaS)
- Infrastructure as a service (IaaS)
(Private cloud, Community cloud, Public cloud, and Hybrid cloud)
This document discusses how Open Grid Services Architecture (OGSA) mechanisms can support virtual organizations (VO) structures. It describes how a basic grid environment uses factories to create services, registries to discover available services, and handle maps to access services. More complex environments can use higher-level factories and registries that delegate to lower-level ones to span distributed resources. Collective operations allow synthesizing multiple lower-level service instances into single higher-level ones.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
This document discusses M2M and IoT design methodologies. It begins with an overview of M2M architecture, including the key components of an M2M area network, M2M core network, M2M gateways, and M2M applications. It then contrasts M2M and IoT, noting differences in communication protocols, types of connected devices, emphasis on hardware vs software, how data is collected and analyzed, and applications. The document also introduces software-defined networking (SDN) and network function virtualization (NFV) as approaches to address limitations of conventional network architectures for IoT.
Advanced Operating System- IntroductionDebasis Das
Introduction to Advanced Operating systems. Many university courses run advanced/ distributed operating system courses in their 4 year engineering programs. This is based on WBUT CS 704 D course but matches many such courses run by different universities. If you need to downloaad this presentation, please send me an email at ddas15847@gmail.com
The document summarizes recovery in multi-database systems. It discusses the architecture of a multi-database system which includes a global transaction manager and interface servers that connect to local database systems. It also describes the two-phase commit protocol used for recovery. This protocol involves a voting phase where databases prepare to commit and a commit phase where the transaction is either committed at all databases or rolled back at all databases to maintain consistency. The two-phase commit ensures that transactions either fully commit or fully rollback across all databases in a recovery-friendly manner.
This document discusses load balancing in distributed systems. It provides definitions of static and dynamic load balancing, compares their approaches, and describes several dynamic load balancing algorithms. Static load balancing assigns tasks at compile time without migration, while dynamic approaches migrate tasks at runtime based on current system state. Dynamic approaches have overhead from migration but better utilize resources. Specific dynamic algorithms discussed include nearest neighbor, random, adaptive contracting with neighbor, and centralized information approaches.
This document discusses the key resources that make up a computing grid: networks, processors, storage, and other resources. It describes how networks are the fundamental resource that enables grid computing by linking together the distributed processors, storage, and other resources. It also discusses how processors, storage, and other resources like scientific instruments can be shared across a grid to provide more computation power, data storage, and capabilities than any single system could alone.
This document discusses implementing Non-Uniform Memory Access (NUMA) systems. It provides background on NUMA, describing how in NUMA architectures each processor has local memory that it can access directly, while still being able to access other processors' memory through interconnects. It discusses shared memory, cache coherence challenges, and strategies for managing coherence like directory-based approaches. It includes a memory architecture block diagram, circuit diagram, memory chip algorithm and example of the memory architecture in operation.
This document provides an overview of distributed computing. It discusses the history and introduction of distributed computing. It describes the working of distributed systems and common types like grid computing, cluster computing and cloud computing. It covers the motivations, goals, characteristics, architectures, security challenges and examples of distributed computing. Advantages include improved performance and fault tolerance, while disadvantages are security issues and lost messages.
In computing, It is the description about Grid Computing.
It gives deep idea about grid, what is grid computing? , why we need it? , why it is so ? etc. History and Architecture of grid computing is also there. Advantages , disadvantages and conclusion is also included.
The client-server model defines which processes initiate interactions and provide services. In the model, client processes request services from server processes, which provide services and return results. Clients are applications that temporarily access remote services, while servers are dedicated to providing a single service and handle multiple clients simultaneously. The two-tier model places database logic on the server, while the three-tier model separates application and data servers.
The document discusses the Open Grid Services Architecture (OGSA) and related concepts. Some key points:
- OGSA is a service-oriented architecture for grids based on integrating grid and web services concepts.
- The Open Grid Services Infrastructure (OGSI) specification defines interfaces and protocols for services in a grid environment to provide interoperability.
- Core constructs of OGSA include functional blocks, protocols, grid services, APIs, and software development kits.
The document discusses the Open Grid Services Architecture (OGSA) and Open Grid Services Infrastructure (OGSI) standards for grid computing. OGSA defines the overall structure and services for grid environments using a distributed computing model. OGSI specifies a set of service primitives and behaviors for grid services. These standards leverage existing web service standards like WSDL to provide interfaces for grid services.
Ogsa ogsi service elements and layered modelPooja Dixit
1) The document discusses the architecture of the Open Grid Services Architecture (OGSA), which has two main components: the Web services layer and the OGSA services layer.
2) The OGSA services layer contains four categories of services: grid core services, grid program execution services, grid data services, and domain-specific services.
3) Grid core services include service management, service communication, policy management, and security services, which provide functions for managing services, enabling communication between services, creating policies for system operation, and supporting security.
Open source grid middleware packages – Globus Toolkit (GT4) Architecture , Configuration – Usage of Globus – Main components and Programming model - Introduction to Hadoop Framework - Mapreduce, Input splitting, map and reduce functions, specifying input and output parameters, configuring and running a job – Design of Hadoop file system, HDFS concepts, command line and java interface, dataflow of File read & File write.
Distributed operating systems allow applications to run across multiple connected computers. They extend traditional network operating systems to provide greater communication and integration between machines on the network. While appearing like a regular centralized OS to users, distributed OSs actually run across multiple independent CPUs. Early research in distributed systems began in the 1970s, with many prototypes introduced through the 1980s-90s, though few achieved commercial success. Design considerations for distributed OSs include transparency, inter-process communication, resource management, reliability, and flexibility.
Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers - Grid computing Infrastructures – cloud computing - service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture.
Grid computing enables sharing of geographically distributed computing resources through a network. It allows for virtual organizations to collaborate on common goals without central control. The document discusses the types of grid computing including computational, data, and scavenging grids. It also outlines the key components of a grid including protocols, architecture, security, and resource management. Examples of existing grid projects are provided such as SETI@Home, EGEE, and BeINGrid.
The document discusses the five layers of the grid protocol architecture: 1) the fabric layer which provides access to different resource types, 2) the connectivity layer which defines core communication and authentication protocols, 3) the resource layer which defines protocols for publishing, discovering, and accessing individual resources, 4) the collective layer which captures interactions across collections of resources through directory services, and 5) the application layer which comprises user applications built on top of the lower layers and operate in virtual organization environments.
Provides a simple and unambiguous taxonomy of three service models
- Software as a service (SaaS)
- Platform as a service (PaaS)
- Infrastructure as a service (IaaS)
(Private cloud, Community cloud, Public cloud, and Hybrid cloud)
This document discusses how Open Grid Services Architecture (OGSA) mechanisms can support virtual organizations (VO) structures. It describes how a basic grid environment uses factories to create services, registries to discover available services, and handle maps to access services. More complex environments can use higher-level factories and registries that delegate to lower-level ones to span distributed resources. Collective operations allow synthesizing multiple lower-level service instances into single higher-level ones.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
This document discusses M2M and IoT design methodologies. It begins with an overview of M2M architecture, including the key components of an M2M area network, M2M core network, M2M gateways, and M2M applications. It then contrasts M2M and IoT, noting differences in communication protocols, types of connected devices, emphasis on hardware vs software, how data is collected and analyzed, and applications. The document also introduces software-defined networking (SDN) and network function virtualization (NFV) as approaches to address limitations of conventional network architectures for IoT.
Advanced Operating System- IntroductionDebasis Das
Introduction to Advanced Operating systems. Many university courses run advanced/ distributed operating system courses in their 4 year engineering programs. This is based on WBUT CS 704 D course but matches many such courses run by different universities. If you need to downloaad this presentation, please send me an email at ddas15847@gmail.com
The document summarizes recovery in multi-database systems. It discusses the architecture of a multi-database system which includes a global transaction manager and interface servers that connect to local database systems. It also describes the two-phase commit protocol used for recovery. This protocol involves a voting phase where databases prepare to commit and a commit phase where the transaction is either committed at all databases or rolled back at all databases to maintain consistency. The two-phase commit ensures that transactions either fully commit or fully rollback across all databases in a recovery-friendly manner.
This document discusses load balancing in distributed systems. It provides definitions of static and dynamic load balancing, compares their approaches, and describes several dynamic load balancing algorithms. Static load balancing assigns tasks at compile time without migration, while dynamic approaches migrate tasks at runtime based on current system state. Dynamic approaches have overhead from migration but better utilize resources. Specific dynamic algorithms discussed include nearest neighbor, random, adaptive contracting with neighbor, and centralized information approaches.
This document discusses the key resources that make up a computing grid: networks, processors, storage, and other resources. It describes how networks are the fundamental resource that enables grid computing by linking together the distributed processors, storage, and other resources. It also discusses how processors, storage, and other resources like scientific instruments can be shared across a grid to provide more computation power, data storage, and capabilities than any single system could alone.
This document discusses implementing Non-Uniform Memory Access (NUMA) systems. It provides background on NUMA, describing how in NUMA architectures each processor has local memory that it can access directly, while still being able to access other processors' memory through interconnects. It discusses shared memory, cache coherence challenges, and strategies for managing coherence like directory-based approaches. It includes a memory architecture block diagram, circuit diagram, memory chip algorithm and example of the memory architecture in operation.
This document provides an overview of distributed computing. It discusses the history and introduction of distributed computing. It describes the working of distributed systems and common types like grid computing, cluster computing and cloud computing. It covers the motivations, goals, characteristics, architectures, security challenges and examples of distributed computing. Advantages include improved performance and fault tolerance, while disadvantages are security issues and lost messages.
In computing, It is the description about Grid Computing.
It gives deep idea about grid, what is grid computing? , why we need it? , why it is so ? etc. History and Architecture of grid computing is also there. Advantages , disadvantages and conclusion is also included.
The client-server model defines which processes initiate interactions and provide services. In the model, client processes request services from server processes, which provide services and return results. Clients are applications that temporarily access remote services, while servers are dedicated to providing a single service and handle multiple clients simultaneously. The two-tier model places database logic on the server, while the three-tier model separates application and data servers.
The document discusses the Open Grid Services Architecture (OGSA) and related concepts. Some key points:
- OGSA is a service-oriented architecture for grids based on integrating grid and web services concepts.
- The Open Grid Services Infrastructure (OGSI) specification defines interfaces and protocols for services in a grid environment to provide interoperability.
- Core constructs of OGSA include functional blocks, protocols, grid services, APIs, and software development kits.
The document discusses the Open Grid Services Architecture (OGSA) and Open Grid Services Infrastructure (OGSI) standards for grid computing. OGSA defines the overall structure and services for grid environments using a distributed computing model. OGSI specifies a set of service primitives and behaviors for grid services. These standards leverage existing web service standards like WSDL to provide interfaces for grid services.
Ogsa ogsi service elements and layered modelPooja Dixit
1) The document discusses the architecture of the Open Grid Services Architecture (OGSA), which has two main components: the Web services layer and the OGSA services layer.
2) The OGSA services layer contains four categories of services: grid core services, grid program execution services, grid data services, and domain-specific services.
3) Grid core services include service management, service communication, policy management, and security services, which provide functions for managing services, enabling communication between services, creating policies for system operation, and supporting security.
Grid and Cloud Computing Lecture-2a.pptxDrAdeelAkram2
The document discusses grid architecture and tools. It covers the hourglass model of grid architecture, which focuses on core services to enable diverse solutions. It also discusses the layered grid architecture with four layers - fabric, connectivity, collective, and application. Simulation tools for modeling grid environments like GridSim are presented. The document then discusses clouds and defines cloud computing. Key aspects of clouds like scalability, virtualization, and on-demand services are covered. It compares clouds to grids and other paradigms. Finally, it introduces service-oriented architecture and defines the characteristics of services.
This document provides an overview of grid computing. It defines a grid as a collection of distributed heterogeneous computing and data resources available through network tools and protocols. It discusses several examples of grid computing projects like SETI@home, Distributed.net, and virtual organizations. It also covers types of grids based on shared resources, topology, and behavior. The document outlines the layered structure of a grid and standards like OGSA, OGSI, and GSI that enable interoperability. It provides descriptions of key grid components like resource brokers, information services, security, data transfer, job submission, and problem solving environments.
Inroduction to grid computing by gargi shankar vermagargishankar1981
Grid computing allows for sharing and coordination of distributed computer resources to address large-scale computation problems. It enables dynamic, scalable, and inexpensive access to computing power by connecting computers and other resources together with open standards. Key aspects of grid computing include dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities through coordination of distributed and often heterogeneous resources not subject to centralized control.
Grid computing is the sharing of computer resources from multiple administrative domains to achieve common goals. It allows for independent, inexpensive access to high-end computational capabilities. Grid computing federates resources like computers, data, software and other devices. It provides a single login for users to access distributed resources for tasks like drug discovery, climate modeling and other data-intensive applications. Current grids are used for distributed supercomputing, high-throughput computing, on-demand computing and other methods. Grids benefit scientists, engineers and other users who need to solve large problems or collaborate globally.
The document discusses OGSI (Open Grid Services Infrastructure), which defines mechanisms for creating, managing, and exchanging information among grid services. OGSI builds on web services standards like SOAP, XML, and WS-Security to provide a common way to access grid services. The Open Grid Services Architecture (OGSA) defines the overall structure and services provided in grid environments. OGSI further specifies the behaviors and interfaces that define how clients interact with grid capabilities. Standards are important for making grid computing practical by enabling interoperability, application portability, and efficient resource sharing across systems.
This document provides an overview of cloud computing. It defines cloud computing as manipulating, configuring, and accessing applications online through virtualization of network resources that are managed and maintained remotely. The key components of cloud infrastructure are servers, storage, networking hardware, management software, deployment platforms, and hypervisors that allow sharing of physical resources. There are various cloud deployment models including public, private, hybrid, and community clouds. In addition, the document outlines several cloud service models such as IaaS, PaaS, SaaS, and IDaaS. Technologies that enable cloud computing are also discussed, including virtualization, service-oriented architecture, grid computing, and utility computing.
The document discusses Grid Computing, which uses distributed computing resources like computer clusters connected via high-speed networks to provide high computational power. It describes the Globus Toolkit, an open-source software toolkit that provides basic services for building Grids. Key components of the Globus Toolkit allow for resource management, security, data management, and communication. The document also discusses parallel programming using MPI (Message Passing Interface) and potential applications of Grid Computing such as distributed supercomputing, real-time systems, and data-intensive processing.
What Does Real World Mass Adoption of Decentralized Tech Look Like?All Things Open
Presented at All Things Open 2023
Presented by Karl Mozurkewich - Storj
Title: What Does Real World Mass Adoption of Decentralized Tech Look Like?
Abstract: We delve into the transformative potential of decentralized technology. Beginning with a brief overview of the rise of centralization with the advent of the internet and the counter-shift marked by blockchain we explore the intrinsic characteristics of decentralized and distributed systems, such as trustless operations, peer-to-peer networks, and enterprise application scalability. Various sectors, including finance, supply chains, media and entertainment, data science and cloud infrastructure are on the brink of disruption. The societal implications are vast, with the potential for greater individual empowerment, a greener planet and more viable resource utilization, but concerns about data security persist.
Find more info about All Things Open:
On the web: https://www.allthingsopen.org/
Twitter: https://twitter.com/AllThingsOpen
LinkedIn: https://www.linkedin.com/company/all-things-open/
Instagram: https://www.instagram.com/allthingsopen/
Facebook: https://www.facebook.com/AllThingsOpen
Mastodon: https://mastodon.social/@allthingsopen
Threads: https://www.threads.net/@allthingsopen
2023 conference: https://2023.allthingsopen.org/
‘Grids’areanapproachforbuildingdynamicallyconstructedproblem-solvingenvironmentsusing
geographically and organizationally dispersed,
high-performance computing and
data handling resources.
Gridsalsoprovideimportantinfrastructuresupportingmulti-institutionalcollaboration.
The document discusses cloud computing, including its definition, basic concepts, service models, and examples. It describes the main service models of infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). The document also discusses grid computing, defining it as a distributed architecture that allows sharing and coordinated use of diverse resources. It outlines the pros and cons of both cloud computing and grid computing.
Grid computing allows for sharing and coordination of computing resources across dynamic virtual organizations. It provides consistent and transparent access to distributed computing resources like computers, software, data and other resources. Key aspects of grid computing include resource sharing, coordinated problem solving, and its focus on large-scale multi-institutional collaborations.
The Grid means the infrastructure for the Advanced Web, for computing, collaboration and communication.
The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources.
“Grid” computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and ,in some cases, high-performance orientation .
We presented the Grid concept in analogy with that of an electrical power grid and Grid vision
This document discusses grid architecture and service modeling. It begins with a brief history of grid computing and identifies four main grid families (computational, information, business, and peer-to-peer grids). It then describes a layered grid architecture modeled after the Internet architecture. Next, it examines Open Grid Services Architecture (OGSA) and some of its core interfaces. It also discusses security models in OGSA. Finally, it covers data-intensive grid service models, data replication strategies, and different grid data access models.
Data Mesh is the decentralized architecture where your units of architecture is a domain driven data set that is treated as a product owned by domains or teams that most intimately know that data either creating it or they are consuming it and re-sharing it and allocated specific roles that have the accountability and the responsibility to provide that data as a product abstracting away complexity into infrastructure layer a self-serve infrastructure layer so that create these products more much more easily.
Grid computing is a form of distributed computing that utilizes a network of loosely coupled computers acting together to perform large tasks. It facilitates large-scale resource sharing and coordinated problem solving among organizations. The key aspects of grid computing covered in the document include grid middleware, methods of grid computing like distributed supercomputing and data-intensive computing, grid architectures like layered grid architecture and data grid architecture, and simulation tools for modeling grid systems.
Data Tactics dhs introduction to cloud technologies wtcDataTactics
Data Tactics Corporation is an established company that provides cloud computing and data management solutions. They operate several secure clouds for government customers and have experience hosting large scale data and applications. The document provides an overview of cloud computing definitions, models, and capabilities relevant for intelligence community applications. It describes Data Tactics' cloud solutions, experience, and the types of features and services they can provide such as scalable data storage, analytics, and user access tools.
This document provides an overview of Cloud-Based Information System Architecture (CISA). It discusses how CISA leverages cloud computing technologies like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) to provide scalable, flexible access to computing resources. Key components of CISA include middleware, APIs, virtualization technologies, and orchestration tools. The document also examines how IaaS, PaaS, and SaaS integrate within CISA and outlines various security measures employed in CISA to protect data and applications.
The Open Grid Services Architecture (OGSA) defines a set of standards for building grid systems. It has four main layers:
1) The application layer which includes physical resources like servers and storage, and logical resources like database and workflow managers.
2) A web services layer which defines how resources and services can interact using Open Grid Services Infrastructure (OGSI) and grid services.
3) OGSI specifies five interfaces for grid services: Factory, Life Cycle, State Management, Service Groups, and Notification.
4) Together these layers define a standardized architecture for building grid systems using web services and interfaces to manage resources and their interactions.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
2. UNIT II GRID SERVICES
Introduction to Open Grid Services Architecture
(OGSA) – Motivation – Functionality Requirements –
Practical & Detailed view of OGSA/OGSI – Data
intensive grid service models – OGSA services.
4. The Hourglass Model
Focus on architecture issues
Propose set of core services as basic
infrastructure
Used to construct high-level, domain-specific
solutions (diverse)
Design principles
Keep participation cost low
Enable local control
Support for adaptation
“IP hourglass” model
Diverse global services
Core
services
Local OS
A p p l i c a t i o n s
5. Layered Grid Architecture
(By Analogy to Internet Architecture)
Application
Fabric
“Controlling things locally”: Access to, & control
of, resources
Connectivity
“Talking to things”: communication (Internet
protocols) & security
Resource
“Sharing single resources”: negotiating access,
controlling use
Collective
“Coordinating multiple resources”: ubiquitous
infrastructure services, app-specific distributed
services
Internet
Transport
Application
Link
InternetProtocolArchitecture
6. We define Grid architecture in terms of a layered collection of protocols.
•Fabric layer includes the protocols and interfaces that provide access to the resources that are being
shared, including computers, storage systems, datasets, programs, and networks. This layer is a logical view
rather then a physical view. For example, the view of a cluster with a local resource manager is defined by
the local resource manger, and not the cluster hardware. Likewise, the fabric provided by a storage system is
defined by the file system that is available on that system, not the raw disk or tapes.
•The connectivity layer defines core protocols required for Grid-specific network transactions. This layer
includes the IP protocol stack (system level application protocols [e.g. DNS, RSVP, Routing], transport and
internet layers), as well as core Grid security protocols for authentication and authorization.
•Resource layer defines protocols to initiate and control sharing of (local) resources. Services defined at
this level are gatekeeper, GRIS, along with some user oriented application protocols from the Internet
protocol suite, such as file-transfer. (Grid Resource Information Service is the repository of local resource
information derived from information providers)
•Collective layer defines protocols that provide system oriented capabilities that are expected to be wide
scale in deployment and generic in function. This includes GIIS, bandwidth brokers, resource brokers,(Grid
Index Information Service: (GIIS): represents a centralized MDS server that provides information about all of
your resources) *Master Data Services (MDS) enables your organization to manage a trusted version of data
•Application layer defines protocols and services that are parochial in nature, targeted towards a specific
application domain or class of applications.
7. Example:
Data Grid Architecture
Discipline-Specific Data Grid Application
Coherency control, replica selection, task management, virtual data catalog,
virtual data code catalog, …
Replica catalog, replica management, co-allocation, certificate authorities,
metadata catalogs,
Access to data, access to computers, access to network performance data, …
Communication, service discovery (DNS), authentication, authorization,
delegation
Storage systems, clusters, networks, network caches, …
Collective
(App)
App
Collective
(Generic)
Resource
Connect
Fabric
9. Simulation tool
GridSim is a Java-based toolkit for modeling, and simulation of
distributed resource management and scheduling for conventional
Grid environment.
GridSim is based on SimJava, a general purpose discrete-event
simulation package implemented in Java.
All components in GridSim communicate with each other through
message passing operations defined by SimJava.
10. Salient features of the GridSim
It allows modeling of heterogeneous types of resources.
Resources can be modeled operating under space- or time-shared
mode.
Resource capability can be defined (in the form of MIPS (Million
Instructions Per Second) benchmark.
Resources can be located in any time zone.
Weekends and holidays can be mapped depending on resource’s local
time to model non-Grid (local) workload.
Resources can be booked for advance reservation.
Applications with different parallel application models can be simulated.
11. Salient features of the GridSim
Application tasks can be heterogeneous and they can be CPU or I/O
intensive.
There is no limit on the number of application jobs that can be submitted to
a resource.
Multiple user entities can submit tasks for execution simultaneously in the
same resource, which may be time-shared or space-shared. This feature
helps in building schedulers that can use different market-driven economic
models for selecting services competitively.
Network speed between resources can be specified.
It supports simulation of both static and dynamic schedulers.
Statistics of all or selected operations can be recorded and they can be
analyzed using GridSim statistics analysis methods.
12. A Modular Architecture for GridSim Platform and Components.
Appn Conf Res Conf User Req Grid Sc Output
Application, User, Grid Scenario’s input and Results
Grid Resource Brokers or Schedulers
…
Appn
modeling
Res entity Info serv Job mgmt Res alloc Statis
GridSim Toolkit
Single CPU SMPs Clusters Load Netw Reservation
Resource Modeling and Simulation
SimJava Distributed SimJava
Basic Discrete Event Simulation Infrastructure
PCs Workstation ClustersSMPs Distributed Resources
Virtual Machine
13. What is the OGSA Standard?
Acronym for Open Grid Service Architecture
OGSA define how different components in grid interact
Open Grid Services Architecture (OGSA) is a set of standards
defining the way in which information is shared among diverse
components of large, heterogeneous grid systems.
In this context, a grid system is a scalable wide area network (WAN) that
supports resource sharing and distribution.
14. major goals of OSGA
Identify the use cases that can drive the OGSA platform
components.
Identify and define the core OGSA platform components.
Define hosting and platform specific bindings.
Define resource models and resource profiles with interoperable
solutions.
15. Functional requirements of OGSA.
Interoperability and Support for Dynamic and Heterogeneous
Environments
Resource Sharing Across Organizations
Optimization
Quality of Service (QoS) Assurance
Job Execution
Data Services
Security
Administrative Cost Reduction
Scalability
Availability
Ease of Use and Extensibility
16. Architecture of OGSA
Comprised of 4 main layers
1. Physical and Logical Resources Layer
2. Web Service Layer
3. OGSA Architected Grid Services Layer
4. Grid Applications Layer
19. OGSA Architecture - Web Services Layer
Web service is software available online that could interact with other
software using XML
Consists of Open Grid Services Infrastructure (OGSI) sub-layer which
specifies grid services and provide consistent way to interact with grid
services
Also extends Web Service Capabilities
Consists of 5 interfaces:
1. Factory: provide way for creation of new grid services
2. Life Cycle: Manages grid service life cycles
3. State Management: Manage grid service states
4. Service Groups: collection of indexed grid services
5. Notification: Manages notification between services & resources
21. OGSA Architecture – OGSA Architected Services -
Layer
Classified into 3 service categories
1. Grid Core Services
2. Grid Program Execution Services
3. Grid Data Services
22. OGSA Architected Services – Grid Core Services
Composed of 4 main types of services:
1. Service Management: assist in installation, maintenance, &
troubleshooting tasks in grid system
2. Service Communication: include functions that allow grid
services to communicate
3. Policy Services: Provide framework for creation,
administration & management of policies for system operation
4. Security Services: provide authentication & authorization
mechanisms to ensure systems interoperate securely
23. OGSA Architected Services – Grid Program
Execution Services
Supports unique grid systems in high performance
computing, collaboration, parallelism
Support virtualization of resource processing
24. OGSA Architected Services – Grid Data Services
Support data virtualization
Provide mechanism for access to distributed
resources such as databases, files
26. OGSA Architecture – Grid Applications Layer
This layer comprise of applications that use the
grid architected services
27. Functional requirements of
OGSA
Interoperability and Support for Dynamic and Heterogeneous Environments
Resource Sharing Across Organizations
Optimization
Quality of Service (QoS) Assurance
Job Execution
Data Services
Security
Administrative Cost Reduction
Scalability
Availability
Ease of Use and Extensibility
28. Interoperability and Support for Dynamic and Heterogeneous
Environments
The need to support heterogeneous systems leads to requirements that include the
following:
• Resource virtualization. Essential to reduce the complexity of managing
heterogeneous systems and to handle diverse resources in a unified way.
• Common management capabilities. Simplifying administration of a heterogeneous
system requires mechanisms for uniform and consistent management of resources.
A minimum set of common manageability capabilities is required.
• Resource discovery and query. Mechanisms are required for discovering resources
with desired attributes and for retrieving their properties. Discovery and query
should handle a highly dynamic and heterogeneous system.
• Standard protocols and schemas. Important for interoperability. In addition, standard
protocols are also particularly important as their use can simplify the transition to
using Grids.
29. Resource Sharing Across Organizations
One major purpose of OGSA is to support resource sharing and utilization across
administrative domains, whether different work units within an enterprise or even
different institutions.
Resource sharing requirements include:
• Global name space. To ease data and resource access. OGSA entities should be
able to access other OGSA entities transparently, subject to security constraints,
without regard to location or replication.
• Metadata services. Important for finding, invoking, and tracking entities. It should
be possible to allow for access to and propagation, aggregation, and management
of entity metadata across administrative domains.
• Site autonomy. Mechanisms are required for accessing resources across sites
while respecting local control and policy
• Resource usage data. Mechanisms and standard schemas for collecting and
exchanging resource usage (i.e., consumption) data across organizations—for the
purpose of accounting, billing, etc.
30. Optimization
Optimization refers to techniques used to allocate resources effectively to meet
consumer and supplier requirements. Optimization applies to both suppliers
(supply-side) and consumers (consume-side) of resources and services
Quality of Service (QoS) Assurance
Services such as job execution and data services must provide the agreed-upon
QoS. Key QoS dimensions include, but are not limited to, availability, security, and
performance.
QoS assurance requirements include:
• Service level agreement. QoS should be represented by agreements which are established
through negotiation between service requester and provider prior to service execution.
Standard mechanisms should be provided to create and manage agreements.
• Service level attainment. If the agreement requires attainment of Service Level, the resources
used by the service should be adjusted so that the required QoS is maintained. Therefore,
mechanisms for monitoring service quality, estimating resource utilization, and planning for
and adjusting resource usage are required.
• Migration. It should be possible to migrate executing services or applications to adjust
workloads for performance or availability
31. Job Execution
Functions such as scheduling, provisioning, job control and exception handling of
jobs must be supported, even when the job is distributed over a great number of
heterogeneous resources.
Job execution requirements include:
• Support for various job types. Execution of various types of jobs must be supported
including simple jobs and complex jobs such as workflow and composite services.
• Job management. It is essential to be able to manage jobs during their entire
lifetimes, types of groupings of jobs (e.g., workflows, job arrays). Mechanisms are
also required for controlling the execution of individual job steps as well as
orchestration or choreography services.
• Scheduling. The ability to schedule and execute jobs based on such information as
specified priority and current allocation of resources is required. It is also required to
realize mechanisms for scheduling across administrative domains, using multiple
schedulers.
• Resource provisioning. To automate the complicated process of resource
allocation, deployment, and configuration. It must be possible to deploy the required
applications and data to resources and configure them automatically.
32. Data Services
require support for the sharing and integration of distributed
data, for example enabling access to information stored in
databases that are managed and administered independently,
with appropriate security assurances.
Data services requirements include:
• Policy specification & management. Examples include
specification of who can access data, where data will be required,
what transformations are permitted on the data, whether use is
exclusive, what performance or availability is required, how much
resources can be used, what consistency is mandated between
replicas, and similar constraints.
Data storage. Disk and tape systems, amongst others, store
33. • Data access. Clients require easy and efficient access to various
types of data (such as databases, files, streams and
integrated/federated data) through a uniform set of interfaces is
required, independent of its physical location or platform, by
abstracting underlying data resources.
• Data transfer. High-bandwidth transfer of data is required,
independent of the physical attributes of the data sources and
sinks, which can exploit relevant features of those sources and
sinks if required.
• Data location management. These services manage where data
is physically located, OGSA should support multiple methods for
making data available to a client at a given location, according to
34. Data update. Although some data resources are read only, many if
not most provide some users with update privileges. OGSA must
provide update facilities which ensure that the specified consistency
can be maintained when cached or replicated data is modified.
• Data persistency. Data should be preserved according to specified
policy and its association with its metadata should be maintained in
accordance with that policy. It should be possible to use one of
many possible persistency models.
• Data federation. OGSA should support data integration for
heterogeneous and distributed data. Heterogeneous data includes
data organized according to different schemas and data stored
using different technologies (e.g., relational vs. flat file).
35. Security
Safe administration requires controlling access to services
through robust security protocols and according to provided
security policy.
Security requirements include:
• Authentication and authorization.
• Multiple security infrastructures. Distributed operation implies a
need to integrate and interoperate with multiple security
infrastructures. OGSA needs to integrate and interoperate with
existing security architectures and models.
• Perimeter security solutions. Resources may have to be
accessed across organizational boundaries, without
compromising local security mechanisms, such as firewall policy
36. • Isolation. Various kinds of isolation must be ensured, such as
isolation of users, performance isolation, and isolation between
content offerings within the same Grid system.
• Delegation. Mechanisms that allow for delegation of access rights
from service requestors to service providers are required. The risk
of misuse of delegated rights must be minimized
• Security policy exchange. Service requestors and providers
should be able to exchange dynamically security policy information
to establish a negotiated security context between them.
• Intrusion detection, protection, and secure logging. Strong
monitoring is required for
intrusion detection and identification of misuses, malicious or
otherwise, including virus or worm attacks.
37. Administrative Cost Reduction
The complexity of administering large-scale distributed,
heterogeneous systems increases administration costs and the risk
of human errors
Policy-based management is required to automate Grid system control,
so that its operations conform to the goals of the organization that
operates and utilizes the Grid system.
Application contents management mechanisms can facilitate the
deployment, configuration, and maintenance of complex systems, by
allowing all application-related information to be specified and managed
as a single logical unit.
Problem determination mechanisms are needed, so that administrators
can recognize and cope quickly with emerging problems.
38. Scalability:A large-scale Grid system can create added value such as drastically
reducing job turn around (or elapsed) time, allowing for utilizing huge number of
resources, thereby enabling new services.
Availability:
mean-time-to-repair (MTTR) -- heterogeneity of the Grid
Disaster recovery mechanisms are needed so that the operation of a Grid system can
be recovered quickly and efficiently in case of natural or human-caused disaster,
avoiding long-term service disruption. Remote backup and simplifying or automating
recovery procedures is required.
Fault management mechanisms can be required so that running jobs are not lost
because of resource faults. Mechanisms are required for monitoring, fault detection,
and diagnosis of causes or impacts on running jobs. In addition, automation of fault-
handling, using techniques such as checkpoint recovery, is desirable.
Ease of Use and Extensibility: mechanism and policy must be realized via extensible
and replaceable components, to permit OGSA to evolve over time and allow users to
construct their own mechanisms and policies to meet specific needs.
39. Conclusion
Grid-Computing allows networked resources to be combined
and used
Grid-Computing offers great benefit to an organization
OGSA are comprehensive standards which governs grid-
computing
40. Open Grid Services Infrastructure (OGSI)
Gives a formal and technical specification of what a grid
service is.
Its a excruciatingly(exceedingly elaborate or intense) / incredibly /
detailed specification of how Grid Services work.
GT3 includes a complete implementation of OGSI.
It is a formal and technical specification of the concepts
described in OGSA.
The Globus Toolkit 3 is an implementation of OGSI.
Some other implementations are OGSI::Lite (Perl)1 and the
UNICORE OGSA demonstrator2 from the EU GRIP project.
OGSI specification defines grid services and builds upon web
services.
41.
42. The Open Grid Services Infrastructure (OGSI) was published by
the Global Grid Forum (GGF) as a proposed
recommendation in June 2003.[1] It was intended to provide an
infrastructure layer for the Open Grid Services Architecture
(OGSA)
43.
44.
45.
46. OGSI creates an extension model for WSDL called GWSDL (Grid
WSDL). The reason is:
Interface inheritance
Service Data (for expressing state information)
Components:
Lifecycle
State management
Service Groups
Factory
Notification
Handle Map
Open Grid Services Infrastructure (OGSI)
47. OSGi (Open Service Gateway
Initiative) is a Java framework
for developing and deploying
modular software programs
and libraries.
Each bundle is a tightly
coupled, dynamically loadable
collection of classes, jars, and
configuration files that
explicitly declare their external
dependencies (if any).
OSGi Service Gateway Architecture
48. The framework is conceptually divided into the following areas:
Bundles Bundles are normal jar components with extra manifest headers.
Services The services layer connects bundles in a dynamic way by offering
a publish-find-bind model for Plain Old Java Interfaces (POJI) or Plain Old
Java Objects (POJO).
Services Registry The application programming interface for management
services (ServiceRegistration, ServiceTracker and ServiceReference).
Life-Cycle The application programming interface for life cycle
management (install, start, stop, update, and uninstall) for bundles.
Modules The layer that defines encapsulation and declaration of
dependencies (how a bundle can import and export code).
Security The layer that handles the security aspects by limiting bundle
functionality to pre-defined capabilities.
49. Execution Environment Defines what methods and classes are
available in a specific platform. There is no fixed list of execution
environments, since it is subject to change as the Java Community
Process creates new versions and editions of Java.
However, the following set is currently supported by most OSGi
implementations:
CDC-1.0/Foundation-1.0
CDC-1.1/Foundation-1.1
OSGi/Minimum-1.0
OSGi/Minimum-1.1
50. Data intensive grid service models
Applications in the grid are normally grouped into two categories
Computation-intensive and Data intensive
Data intensive applications deals with massive amounts of data.
The grid system must specially designed to discover, transfer
and manipulate the massive data sets.
Transferring the massive data sets is a time consuming task.
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 block and scattering them in
multiple regions in a grid, users can access the same data with
locality of references.
51. Replication strategies determine when and where to create a replica of the data.
The strategies of replications can be classified into dynamic and static
Static method
The locations and number of replicas are determined in advance and will not be modified.
Replication operation require little overhead
Static strategic cannot adapt to changes in demand, bandwidth and storage variability
Optimization is required to determine the location and number of data replicas.
Dynamic strategies
Dynamic strategies can adjust locations and number of data replicas according to change in
conditions
Frequent data moving operations can result in much more overhead the static strategies
Optimization may be determined based on whether the data replica is being created,
deleted or moved.
The most common replication include preserving locality, minimizing update costs and
maximizing profits .
Data intensive grid service models
52. Grid data Access models
In general there are four access models for organizing a
data grid as listed here
1. Monadic method
2. Hierarchical model
3. Federation model
4. Hybrid model
53. Monadic method
This is a centralized data
repository model. All data is saved
in central data repository.
When users want to access some
data they have no submit request
directly to the central repository.
No data is replicated for preserving
data locality.
For a larger grid this model is not
efficient in terms of performance
and reliability.
Data replication is permitted in this
model only when fault tolerance is
demanded.
54. Hierarchical model
It is suitable for building a large data
grid which has only one large data
access directory
Data may be transferred from the
source to a second level center. Then
some data in the regional center is
transferred to the third level centre.
After being forwarded several times
specific data objects are accessed
directly by users. Higher level data
center has a wider coverage area.
PKI security services are easier to
implement in this hierarchical data
access model
55. Federation model
It is suited for designing a data grid
with multiple source of data supplies.
It is also known as a mesh model
The data is shared the data and
items are owned and controlled by
their original owners.
Only authenticated users are
authorized to request data from any
data source.
This mesh model cost the most when
the number of grid institutions
becomes very large
56. Hybrid model
This model combines the best
features of the hierarchical and mesh
models.
Traditional data transfer technology
such as FTP applies for networks
with lower bandwidth.
High bandwidth are exploited by high
speed data transfer tools such as
GridFTP developed with Globus
library.
The cost of hybrid model can be
traded off between the two extreme
models of hierarchical and mesh-
connected grids.
57. Parallel versus Striped Data Transfers
Parallel data transfer opens multiple data streams for passing
subdivided segments of a file simultaneously. Although the
speed of each stream is same as in sequential streaming, the
total time to move data in all streams can be significantly
reduced compared to FTP transfer.
Striped data transfer a data objects is partitioned into a number
of sections and each section is placed in an individual site in a
data grid. When a user requests this piece of data, a data stream
is created for each site in a data gird. When user requests this
piece of data, data stream is created for each site, and all the
sections of data objects ate transected simultaneously.
58. Grid Services and OGSA
Facilitate use and management of resources across distributed,
heterogeneous environments
Deliver seamless QoS
Define open, published interfaces in order to provide interoperability
of diverse resources
Exploit industry-standard integration technologies
Develop standards that achieve interoperability
Integrate, virtualize, and manage services and resources in a
distributed, heterogeneous environment
Deliver functionality as loosely coupled, interacting services aligned
with industry- accepted web service standards
59. OGSA services fall
into seven broad
areas, defined in
terms of capabilities
frequently required
in a grid scenario.
Figure shows the
OGSA architecture.
These services are
summarized as
follows:
60. OGSA services - seven broad areas
1. Infrastructure Services Refer to a set of common functionalities, such as
naming, typically required by higher level services.
2. Execution Management Services Concerned with issues such as starting
and managing tasks, including placement, provisioning, and life-cycle
management. Tasks may range from simple j obs to complex workflows or
composite services.
3. Data Management Services Provide functionality to move data to where it is
needed, maintain replicated copies, run queries and updates, and transform
data into new formats. These services must handle issues such as data
consistency, persistency, and integrity. An OGSA data service is a web service
that implements one or more of the base data interfaces to enable access to,
and management of, data resources in a distributed environment. The three
base interfaces, Da ta Access, Da ta Fa ctory, and Da ta Ma na gement,
define basic operations for representing, accessing, creating, and managing
data.
61. 4. Resource Management Services Provide management capabilities
for grid resources: management of the resources themselves,
management of the resources as grid components, and management
of the OGSA infrastructure. For example, resources can be
monitored, reserved, deployed, and configured as needed to meet
application QoS requirements. I t also requires an information model
(semantics) and data model (representation) of the grid resources
and services.
5. Security Services Facilitate the enforcement of security-related
policies within a (virtual) organization, and supports safe resource
sharing. Authentication, authorization, and integrity assurance are
essential functionalities provided by these services.
OGSA services - seven broad areas
62. 6. Information Services Provide efficient production of, and access to,
information about the grid and its constituent resources. The term
“information” refers to dynamic data or events used for status
monitoring; relatively static data used for discovery; and any data that is
logged. Troubleshooting is j ust one of the possible uses for information
provided by these services.
7. Self-Management Services Support service-level attainment for a set
of services (or resources), with as much automation as possible, to
reduce the costs and complexity of managing the system. These
services are essential in addressing the increasing complexity of owning
and operating an I T infrastructure.
OGSA services - seven broad areas
63. References
1. Kai Hwang, Geoffery C. Fox and Jack J. Dongarra, “Distributed and Cloud Computing: Clusters,
Grids, Clouds and the Future of Internet”, First Edition, Morgan Kaufman Publisher, an Imprint of
Elsevier, 2012.
2. https://www.dcc.fc.up.pt/~ines/aulas/1213/CG/OGSA.ppt
3. http://www.computerworld.com/article/2552339/networking/open-grid-services-architecture.html
4. http://searchsoa.techtarget.com/definition/Open-Grid-Services-Architecture
5. www.cs.umsl.edu/~sanjiv/classes/cs6740/presentation/OGSA.ppt
6. www.nesc.ac.uk/news/.../OpenGridServicesArchitectureApril20021.ppt
7. www.cse.buffalo.edu/~bina/cse486/spring2011/progtutorial_0.4.3.pdf
Editor's Notes
We define Grid architecture in terms of a layered collection of protocols.
Fabric layer includes the protocols and interfaces that provide access to the resources that are being shared, including computers, storage systems, datasets, programs, and networks. This layer is a logical view rather then a physical view. For example, the view of a cluster with a local resource manager is defined by the local resource manger, and not the cluster hardware. Likewise, the fabric provided by a storage system is defined by the file system that is available on that system, not the raw disk or tapes.
The connectivity layer defines core protocols required for Grid-specific network transactions. This layer includes the IP protocol stack (system level application protocols [e.g. DNS, RSVP, Routing], transport and internet layers), as well as core Grid security protocols for authentication and authorization.
Resource layer defines protocols to initiate and control sharing of (local) resources. Services defined at this level are gatekeeper, GRIS, along with some user oriented application protocols from the Internet protocol suite, such as file-transfer.
Collective layer defines protocols that provide system oriented capabilities that are expected to be wide scale in deployment and generic in function. This includes GIIS, bandwidth brokers, resource brokers,….
Application layer defines protocols and services that are parochial in nature, targeted towards a specific application domain or class of applications. These are are are … arrgh