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.
Trust models for Grid security environment – Authentication and Authorization methods – Grid security infrastructure – Cloud Infrastructure security: network, host and application level – aspects of data security, provider data and its security, Identity and access management architecture, IAM practices in the cloud, SaaS, PaaS, IaaS availability in the cloud, Key privacy issues in the cloud.
Learning objectives
• Understand how to handle massive amount of data using data grid.
• Explains data replication and namespaces
• Identify the various data access model.
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.
Introduction to Cloud Computing and Cloud InfrastructureSANTHOSHKUMARKL1
Introduction, Cloud Infrastructure: Cloud computing, Cloud computing delivery models and services, Ethical issues, Cloud vulnerabilities, Cloud computing at Amazon, Cloud computing the Google perspective, Microsoft Windows Azure and online services, Open-source software platforms for private clouds.
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.
Trust models for Grid security environment – Authentication and Authorization methods – Grid security infrastructure – Cloud Infrastructure security: network, host and application level – aspects of data security, provider data and its security, Identity and access management architecture, IAM practices in the cloud, SaaS, PaaS, IaaS availability in the cloud, Key privacy issues in the cloud.
Learning objectives
• Understand how to handle massive amount of data using data grid.
• Explains data replication and namespaces
• Identify the various data access model.
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.
Introduction to Cloud Computing and Cloud InfrastructureSANTHOSHKUMARKL1
Introduction, Cloud Infrastructure: Cloud computing, Cloud computing delivery models and services, Ethical issues, Cloud vulnerabilities, Cloud computing at Amazon, Cloud computing the Google perspective, Microsoft Windows Azure and online services, Open-source software platforms for private clouds.
High Performance & High Throughput Computing - EUDAT Summer School (Giuseppe ...EUDAT
Giuseppe will present the differences between high-performance and high-throughput applications. High-throughput computing (HTC) refers to computations where individual tasks do not need to interact while running. It differs from High-performance (HPC) where frequent and rapid exchanges of intermediate results is required to perform the computations. HPC codes are based on tightly coupled MPI, OpenMP, GPGPU, and hybrid programs and require low latency interconnected nodes. HTC makes use of unreliable components distributing the work out to every node and collecting results at the end of all parallel tasks.
Visit: https://www.eudat.eu/eudat-summer-school
Cloud deployment models: public, private, hybrid, community – Categories of cloud computing: Everything as a service: Infrastructure, platform, software - Pros and Cons of cloud computing – Implementation levels of virtualization – virtualization structure – virtualization of CPU, Memory and I/O devices – virtual clusters and Resource Management – Virtualization for data center automation.
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The core of Apache Hadoop consists of a storage part (HDFS) and a processing part (MapReduce).
These All Cloud Computing Architectures have been Discussed in this Lecture
Hypervisor Clustering Architecture
Load Balanced Virtual Server Instances Architecture
Non-Disruptive Service Relocation Architecture
Zero Downtime Architecture
Cloud Balancing Architecture
Resource Reservation Architecture
Dynamic Failure Detection and Recovery Architecture
Bare-Metal Provisioning Architecture
Rapid Provisioning Architecture
Storage Workload Management Architecture
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Cloud computing introduction and concept as per the RGPV, BE syllabus. PPt contains the material from various cloud Draft (NIST) and other research material to fulfill the Syllabus requirement.
High Performance & High Throughput Computing - EUDAT Summer School (Giuseppe ...EUDAT
Giuseppe will present the differences between high-performance and high-throughput applications. High-throughput computing (HTC) refers to computations where individual tasks do not need to interact while running. It differs from High-performance (HPC) where frequent and rapid exchanges of intermediate results is required to perform the computations. HPC codes are based on tightly coupled MPI, OpenMP, GPGPU, and hybrid programs and require low latency interconnected nodes. HTC makes use of unreliable components distributing the work out to every node and collecting results at the end of all parallel tasks.
Visit: https://www.eudat.eu/eudat-summer-school
Cloud deployment models: public, private, hybrid, community – Categories of cloud computing: Everything as a service: Infrastructure, platform, software - Pros and Cons of cloud computing – Implementation levels of virtualization – virtualization structure – virtualization of CPU, Memory and I/O devices – virtual clusters and Resource Management – Virtualization for data center automation.
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The core of Apache Hadoop consists of a storage part (HDFS) and a processing part (MapReduce).
These All Cloud Computing Architectures have been Discussed in this Lecture
Hypervisor Clustering Architecture
Load Balanced Virtual Server Instances Architecture
Non-Disruptive Service Relocation Architecture
Zero Downtime Architecture
Cloud Balancing Architecture
Resource Reservation Architecture
Dynamic Failure Detection and Recovery Architecture
Bare-Metal Provisioning Architecture
Rapid Provisioning Architecture
Storage Workload Management Architecture
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Cloud computing introduction and concept as per the RGPV, BE syllabus. PPt contains the material from various cloud Draft (NIST) and other research material to fulfill the Syllabus requirement.
The Message Passing Interface (MPI) in Layman's TermsJeff Squyres
Introduction to the basic concepts of what the Message Passing Interface (MPI) is, and a brief overview of the Open MPI open source software implementation of the MPI specification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However, existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This paper presents the current state-of-the-art research challenges and possible solutions on big data networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to handle today’s idly growing digital world and compare and contrast them to identify relevant problems and solutions.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However,
existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This
paper presents the current state-of-the-art research challenges and possible solutions on big data
networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop
paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to
handle today’s idly growing digital world and compare and contrast them to identify relevant problems and
solutions.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
A flexible, efficient and secure networking architecture is required in order to process big data. However,
existing network architectures are mostly unable to handle big data. As big data pushes network resources
to the limits it results in network congestion, poor performance, and detrimental user experiences. This
paper presents the current state-of-the-art research challenges and possible solutions on big data
networking theory. More specifically, we present the state of networking issues of big data related to
capacity, management and data processing. We also present the architectures of MapReduce and Hadoop
paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to
handle today’s idly growing digital world and compare and contrast them to identify relevant problems and
solutions.
A Platform for Large-Scale Grid Data Service on Dynamic High-Performance Netw...Tal Lavian Ph.D.
Data intensive Grid applications often deal with multiple terabytes and even petabytes of data. For them to be effectively deployed over distances, it is crucial that Grid infrastructures learn how to best exploit high-performance networks
(such as agile optical networks). The network footprint of these Grid applications show pronounced peaks and valleys in utilization, prompting for a radical overhaul of traditional network provisioning styles such as peak-provisioning, point-and-click or operator-assisted provisioning. A Grid stack must become capable to dynamically orchestrate a complex set of variables related to application requirements, data services, and network provisioning services, all within a rapidly and continually changing environment. Presented here is a platform that addresses some of these issues. This service platform closely integrates a set of large-scale data services with those for dynamic bandwidth allocation, through a network resource middleware service, using an OGSA-compliant interface allowing direct access by external applications. Recently, this platform has been implemented as an experimental research prototype on a unique wide area optical networking testbed incorporating state-of-the-art photonic
components. The paper, which presents initial results of research conducted on this prototype, indicates that these methods have the potential to address multiple major challenges related to data intensive applications. Given the complexities of this topic, especially where scheduling is required, only selected aspects of this platform are considered in this paper.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The growth of internet of things and wireless technology has led to enormous generation of data for various application uses such as healthcare, scientific and data intensive application. Cloud based Storage Area Network (SAN) has been widely in recent time for storing and processing these data. Providing fault tolerant and continuous access to data with minimal latency and cost is challenging. For that efficient fault tolerant mechanism is required. Data replication is an efficient mechanism for providing fault tolerant mechanism that has been considered by exiting methodologies. However, data replica placement is challenging and existing method are not efficient considering application dynamic requirement of cloud based storage area network. Thus, incurring latency, due to which induce higher cost of data transmission. This work present an efficient replica placement and transmission technique using Bipartite Graph based Data Replica Placement (BGDRP) technique that aid in minimizing latency and computing cost. Performance of BGDRP is evaluated using real-time scientific application workflow. The outcome shows BGDRP technique minimize data access latency, computation time and cost over state-of-art technique.
A Survey of File Replication Techniques In Grid SystemsEditor IJCATR
Grid is a type of parallel and distributed systems that is designed to provide reliable access to data
and computational resources in wide area networks. These resources are distributed in different geographical
locations. Efficient data sharing in global networks is complicated by erratic node failure, unreliable network
connectivity and limited bandwidth. Replication is a technique used in grid systems to improve the
applications’ response time and to reduce the bandwidth consumption. In this paper, we present a survey on
basic and new replication techniques that have been proposed by other researchers. After that, we have a full
comparative study on these replication strategies.
Grid is a type of parallel and distributed systems that is designed to provide reliable access to data
and computational resources in wide area networks. These resources are distributed in different geographical
locations. Efficient data sharing in global networks is complicated by erratic node failure, unreliable network
connectivity and limited bandwidth. Replication is a technique used in grid systems to improve the
applications’ response time and to reduce the bandwidth consumption. In this paper, we present a survey on
basic and new replication techniques that have been proposed by other researchers. After that, we have a full
comparative study on these replication strategies
A Survey of File Replication Techniques In Grid SystemsEditor IJCATR
Grid is a type of parallel and distributed systems that is designed to provide reliable access to data
and computational resources in wide area networks. These resources are distributed in different geographical
locations. Efficient data sharing in global networks is complicated by erratic node failure, unreliable network
connectivity and limited bandwidth. Replication is a technique used in grid systems to improve the
applications’ response time and to reduce the bandwidth consumption. In this paper, we present a survey on
basic and new replication techniques that have been proposed by other researchers. After that, we have a full
comparative study on these replication strategies
‘Grids’areanapproachforbuildingdynamicallyconstructedproblem-solvingenvironmentsusing
geographically and organizationally dispersed,
high-performance computing and
data handling resources.
Gridsalsoprovideimportantinfrastructuresupportingmulti-institutionalcollaboration.
An Algorithm to synchronize the local database with cloud DatabaseAM Publications
Since the cloud computing [1] platform is widely accepted by the industry, variety of applications are designed targeting to a cloud platform. Database as a Service (DaaS) is one of the powerful platform of cloud computing. There are many research issues in DaaS platform and one among them is the data synchronization issue. There are many approaches suggested in the literature to synchronise a local database by being in cloud environment. Unfortunately, very few work only available in the literature to synchronise a cloud database by being in the local database. The aim of this paper is to provide an algorithm to solve the problem of data synchronization from local database to cloud database.
An elastic , effective, activety or intelligent ,graceful networking architecture layout be desired to make processing massive data. next to that ,existent network architectures be considerably incapable for
cleatting the huge data. massive data thrusts network exchequers into border it consequence with in network overcrowding ,needy achievement, then permicious employer exprtises. this offered the current state-of-the-art research affronts ,potential solutions into huge data networking notion. More specifically, present the state of networking problems into massive data connected intrequirements,capacity,running ,
data manipulating also will introduce the architectures of MapReduce , Hadoop paradigm within research
requirements, fabric networks and software defined networks which utilizized into making today’s idly growing digital world and compare and contrast into identify relevant drawbacks and solutions.
Load Balance in Data Center SDN Networks IJECEIAES
In the last two decades, networks had been changed according to the rapid changing in its requirements. The current Data Center Networks have large number of hosts (tens or thousands) with special needs of bandwidth as the cloud network and the multimedia content computing is increased. The conventional Data Center Networks (DCNs) are highlighted by the increased number of users and bandwidth requirements which in turn have many implementation limitations. The current networking devices with its control a nd forwarding planes coupling result in network architectures are not suitable for dynamic computing and storage needs. Software Defined networking (SDN) is introduced to change this notion of traditional networks by decoupling control and forwarding planes. So, due to the rapid increase in the number of applications, websites, storage space, and some of the network resources are being underutilized due to static routing mechanisms. To overcome these limitations, a Software Defined Network based Openflow Data Center network architecture is used to obtain better performance parameters and implementing traffic load balancing function. The load balancing distributes the traffic requests over the connected servers, to diminish network congestions, and reduce un derutilization problem of servers. As a result, SDN is developed to afford more effective configuration, enhanced performance, and more flexibility to deal with huge network designs.
We are providing training on IEEE 2016-17 projects for Ph.D Scalars, M.Tech, B.E, MCA, BCA and Diploma students for
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Data Distribution Handling on Cloud for Deployment of Big Dataneirew J
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud
computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to
process massive amounts of data. Processing large datasets has become crucial in research and business
environments. The big challenges associated with processing large datasets is the vast infrastructure
required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be
provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can
be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer
combines individual output from each Vms to produce final result. we have proposed an algorithm to
reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst
Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall
data processing time in cloud.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
Similar to Cs6703 grid and cloud computing unit 2 (20)
BJT small signal model – Analysis of CE, CB, CC amplifiers- Gain and frequency response – MOSFET small signal model– Analysis of CS and Source follower – Gain and frequency response- High frequency analysis.
Orbits : types of satellites : frequency used link establishment, MA techniques used in satellite communication, earth station; aperture actuators used in satellite – Intelsat and Insat: fibers – types:
sources, detectors used, digital filters, optical link: power line carrier communications: SCADA
AM – Frequency spectrum – vector representation – power relations – generation of AM – DSB, DSB/SC, SSB, VSB AM Transmitter & Receiver; FM and PM – frequency spectrum – power relations : NBFM & WBFM, Generation of FM and DM, Armstrong method & Reactance modulations : FM & PM frequency.
PN junction diode –structure, operation and V-I characteristics, diffusion and transient capacitance - Rectifiers – Half Wave and Full Wave Rectifier,– Display devices- LED, Laser diodes- Zener diodecharacteristics-Zener Reverse characteristics – Zener as regulator,TRANSISTORS, BJT, JFET, MOSFET- structure, operation, characteristics and Biasing UJT, Thyristor and IGBT Structure and characteristics,BJT small signal model – Analysis of CE, CB, CC amplifiers- Gain and frequency response –
MOSFET small signal model– Analysis of CS and Source follower – Gain and frequency response- High frequency analysis,BIMOS cascade amplifier, Differential amplifier – Common mode and Difference mode analysis – FET input stages – Single tuned amplifiers – Gain and frequency response – Neutralization methods, power amplifiers –Types (Qualitative analysis),Advantages of negative feedback – voltage / current, series , Shunt feedback –positive feedback – Condition for oscillations, phase shift – Wien bridge, Hartley, Colpitts and Crystal oscillators.
Multinational Corporations – Environmental Ethics – Computer Ethics – Weapons Development – Engineers as Managers – Consulting Engineers – Engineers as Expert Witnesses and Advisors – Moral Leadership –Code of Conduct – Corporate Social Responsibility
Safety and Risk – Assessment of Safety and Risk – Risk Benefit Analysis and Reducing Risk - Respect for Authority – Collective Bargaining – Confidentiality – Conflicts of Interest – Occupational Crime – Professional Rights – Employee Rights – Intellectual Property Rights (IPR) – Discrimination
Senses of “Engineering Ethics” – Variety of moral issues – Types of inquiry – Moral dilemmas – Moral Autonomy – Kohlberg‟s theory – Gilligan‟s theory – Consensus and Controversy – Models of professional roles - Theories about right action – Self-interest – Customs and Religion – Uses of Ethical Theories
Morals, values and Ethics – Integrity – Work ethic – Service learning – Civic virtue – Respect for others – Living peacefully – Caring – Sharing – Honesty – Courage – Valuing time – Cooperation – Commitment – Empathy – Self confidence – Character – Spirituality – Introduction to Yoga and meditation for professional excellence and stress management.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
1. Dr Gnanasekaran Thangavel
Professor and Head
Faculty of Information Technology
R M K College of Engineering and
Technology
CS6703 GRID AND CLOUD COMPUTING
Unit 2
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.
7/17/20162 Dr Gnanasekaran Thangavel
3. What is the OGSA Standard?
7/17/2016Dr Gnanasekaran Thangavel3
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.
4. Architecture of OGSA
7/17/2016Dr Gnanasekaran Thangavel4
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
7. OGSA Architecture - Web Services Layer
7/17/2016Dr Gnanasekaran Thangavel7
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
9. OGSA Architecture – OGSA Architected Services -
Layer
7/17/2016Dr Gnanasekaran Thangavel9
Classified into 3 service categories
1. Grid Core Services
2. Grid Program Execution Services
3. Grid Data Services
10. OGSA Architected Services – Grid Core Services
7/17/2016Dr Gnanasekaran Thangavel10
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
11. OGSA Architected Services – Grid Program
Execution Services
7/17/2016Dr Gnanasekaran Thangavel11
Supports unique grid systems in high performance
computing, collaboration, parallelism
Support virtualization of resource processing
12. OGSA Architected Services – Grid Data Services
7/17/2016Dr Gnanasekaran Thangavel12
Support data virtualization
Provide mechanism for access to distributed
resources such as databases, files
14. OGSA Architecture – Grid Applications Layer
7/17/2016Dr Gnanasekaran Thangavel14
This layer comprise of applications that use the
grid architected services
15. MIE456
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
16. Open Grid Services Infrastructure (OGSI)
7/17/2016Dr Gnanasekaran Thangavel16
Gives a formal and technical specification of what a grid
service is.
Its a excruciatingly 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
17. 7/17/2016Dr Gnanasekaran Thangavel17
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)
18. Data intensive grid service models
7/17/2016Dr Gnanasekaran Thangavel18
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
19. 7/17/2016Dr Gnanasekaran Thangavel19
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.
Data intensive grid service models
20. Grid data Access models
7/17/2016Dr Gnanasekaran Thangavel20
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
21. Monadic method
7/17/2016Dr Gnanasekaran Thangavel21
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
22. Hierarchical model
7/17/2016Dr Gnanasekaran Thangavel22
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
23. Federation model
7/17/2016Dr Gnanasekaran Thangavel23
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 intuitions becomes very
24. Hybrid model
7/17/2016Dr Gnanasekaran Thangavel24
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-
25. Parallel versus Striped Data Transfers
7/17/2016Dr Gnanasekaran Thangavel25
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.
26. Grid Services and OGSA
7/17/2016Dr Gnanasekaran Thangavel26
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
27. 7/17/2016Dr Gnanasekaran Thangavel27
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:
28. OGSA services - seven broad areas
7/17/2016Dr Gnanasekaran Thangavel28
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
29. 7/17/2016Dr Gnanasekaran Thangavel29
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
30. 7/17/2016Dr Gnanasekaran Thangavel30
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
31. 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.ppt31 Dr Gnanasekaran Thangavel 7/17/2016