Grid computing involves applying the computing resources of many networked computers to a single large problem simultaneously. It allows for resource sharing and coordinated problem solving across dynamic virtual organizations. Idle systems on a network and their wasted CPU cycles can be united into a single large virtual system for efficient resource sharing at runtime through grid computing techniques. The document provides an example of a local area network of 20 systems where 10 are idle and 5 use low CPU, and how grid computing could efficiently utilize their wasted CPU cycles. It also outlines the major business areas that benefit from grid computing like life sciences, financial services, education, and engineering.
Application-Aware Big Data Deduplication in Cloud EnvironmentSafayet Hossain
Here I present a paper based on Application-Aware Big Data Deduplication in Cloud Environment. It is published on IEEE on 31 May 2017.
Abstract of this paper:
Deduplication has become a widely deployed technology in cloud data centers to improve IT resources efficiency. However, traditional techniques face a great challenge in big data deduplication to strike a sensible tradeoff between the conflicting goals of scalable deduplication throughput and high duplicate elimination ratio. We propose AppDedupe, an application-aware scalable inline distributed deduplication framework in cloud environment, to meet this challenge by exploiting application awareness, data similarity and locality to optimize distributed deduplication with inter-node two-tiered data routing and intra-node application-aware deduplication. It first dispenses application data at file level with an application-aware routing to keep application locality, then assigns similar application data to the same storage node at the super-chunk granularity using a handprinting-based stateful data routing scheme to maintain high global deduplication efficiency, meanwhile balances the workload across nodes. AppDedupe builds application-aware similarity indices with super-chunk handprints to speedup the intra-node deduplication process with high efficiency. Our experimental evaluation of AppDedupe against state-of-the-art, driven by real-world datasets, demonstrates that AppDedupe achieves the highest global deduplication efficiency with a higher global deduplication effectiveness than the high-overhead and poorly scalable traditional scheme, but at an overhead only slightly higher than that of the scalable but low duplicate-elimination-ratio approaches.
Link of this paper:
https://ieeexplore.ieee.org/document/7936577
Grid computing is the application of several computers to a single problem
at the same time.
This Presentation deals with the idea of Grid Computing, its Design
Considerations, How a Grid Works, and some of the existing Grids in the
World today.
Application-Aware Big Data Deduplication in Cloud EnvironmentSafayet Hossain
Here I present a paper based on Application-Aware Big Data Deduplication in Cloud Environment. It is published on IEEE on 31 May 2017.
Abstract of this paper:
Deduplication has become a widely deployed technology in cloud data centers to improve IT resources efficiency. However, traditional techniques face a great challenge in big data deduplication to strike a sensible tradeoff between the conflicting goals of scalable deduplication throughput and high duplicate elimination ratio. We propose AppDedupe, an application-aware scalable inline distributed deduplication framework in cloud environment, to meet this challenge by exploiting application awareness, data similarity and locality to optimize distributed deduplication with inter-node two-tiered data routing and intra-node application-aware deduplication. It first dispenses application data at file level with an application-aware routing to keep application locality, then assigns similar application data to the same storage node at the super-chunk granularity using a handprinting-based stateful data routing scheme to maintain high global deduplication efficiency, meanwhile balances the workload across nodes. AppDedupe builds application-aware similarity indices with super-chunk handprints to speedup the intra-node deduplication process with high efficiency. Our experimental evaluation of AppDedupe against state-of-the-art, driven by real-world datasets, demonstrates that AppDedupe achieves the highest global deduplication efficiency with a higher global deduplication effectiveness than the high-overhead and poorly scalable traditional scheme, but at an overhead only slightly higher than that of the scalable but low duplicate-elimination-ratio approaches.
Link of this paper:
https://ieeexplore.ieee.org/document/7936577
Grid computing is the application of several computers to a single problem
at the same time.
This Presentation deals with the idea of Grid Computing, its Design
Considerations, How a Grid Works, and some of the existing Grids in the
World today.
Grid Computing is the emerging technology. you will learn all the stuff related to grid computing in this slides. this slide shows various architecture and its easy explanation.
automation in it's next level,applications of fog computing,need of fog computing,fog vs cloud, Internet of things,fog vs cloud vs IOT ,existing cloud system, proposed system presentation conclusion
Concept of edge computing is to leverage new generation technologies, processes, services, and applications that are built to take an advantage of new infrastructure.
Put processing closer to the edge of the network pre-process data and send to the cloud.
Fog Computing extends the Cloud Computing paradigm close to the edge of network, and thus enabling a new breed of applications and services.
This is why it is also sometimes called as Edge computing but the defining characteristics of the Fog are: Low latency, Mobility, High efficiency and performance.
In this presentation we will talk about What is Fog Computing, how is it different from Edge computing, its benefits and limitations and how it will shape the future of IoT.
Fog computing is the next stage of cloud computing. The presentation provides a comparison between cloud and fog computing and discusses how live migration is useful in the field of fog computing.
Grid Computing is the emerging technology. you will learn all the stuff related to grid computing in this slides. this slide shows various architecture and its easy explanation.
automation in it's next level,applications of fog computing,need of fog computing,fog vs cloud, Internet of things,fog vs cloud vs IOT ,existing cloud system, proposed system presentation conclusion
Concept of edge computing is to leverage new generation technologies, processes, services, and applications that are built to take an advantage of new infrastructure.
Put processing closer to the edge of the network pre-process data and send to the cloud.
Fog Computing extends the Cloud Computing paradigm close to the edge of network, and thus enabling a new breed of applications and services.
This is why it is also sometimes called as Edge computing but the defining characteristics of the Fog are: Low latency, Mobility, High efficiency and performance.
In this presentation we will talk about What is Fog Computing, how is it different from Edge computing, its benefits and limitations and how it will shape the future of IoT.
Fog computing is the next stage of cloud computing. The presentation provides a comparison between cloud and fog computing and discusses how live migration is useful in the field of fog computing.
Cloud computing notes unit I as per RGPV syllabusNANDINI SHARMA
Cloud Computing
Historical development ,Vision of Cloud Computing, Characteristics of cloud
computing as per NIST , Cloud computing reference model ,Cloud computing environments,
Cloud services requirements, Cloud and dynamic infrastructure, Cloud Adoption and rudiments
.Overview of cloud applications: ECG Analysis in the cloud, Protein structure prediction, Gene
Expression Data Analysis ,Satellite Image Processing ,CRM and ERP ,Social networking .
This is the presentation on clusters computing which includes information from other sources too including my own research and edition. I hope this will help everyone who required to know on this topic.
Cluster computing is a type of computing where a group of several computers are linked together, allowing the entire group of computers to behave as if it were a single entity. There are a wide variety of different reasons why people might use cluster computing for various computer tasks. It s also used to make sure that a computing system will always be available. It is unknown when this cluster computing concept was first developed, and several different organizations have claimed to have invented it.
Introduction to Cloud Computing
Cloud computing is a transformative technology that allows businesses and individuals to access computing resources over the internet. Instead of owning and maintaining physical hardware and software, users can leverage cloud services provided by companies like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others. This shift has revolutionized how we think about IT infrastructure, software development, data storage, and more.
Key Concepts of Cloud Computing
On-Demand Self-Service:
Users can provision computing resources as needed without human intervention from the service provider. This includes servers, storage, and applications.
Broad Network Access:
Cloud services are available over the network and accessed through standard mechanisms, enabling use from a variety of devices like laptops, smartphones, and tablets.
Resource Pooling:
Providers use a multi-tenant model to serve multiple customers with dynamically assigned resources. This model allows for economies of scale and efficient resource utilization.
Rapid Elasticity:
Resources can be elastically provisioned and released, sometimes automatically, to scale rapidly outward and inward commensurate with demand.
Measured Service:
Cloud systems automatically control and optimize resource use by leveraging a metering capability, allowing for pay-as-you-go pricing models.
Types of Cloud Computing Services
Infrastructure as a Service (IaaS):
Provides virtualized computing resources over the internet. Examples include AWS EC2, Google Compute Engine, and Azure Virtual Machines.
Platform as a Service (PaaS):
Offers hardware and software tools over the internet, typically used for application development. Examples include Google App Engine, AWS Elastic Beanstalk, and Azure App Services.
Software as a Service (SaaS):
Delivers software applications over the internet, on a subscription basis. Examples include Google Workspace, Microsoft Office 365, and Salesforce.
Deployment Models
Public Cloud:
Services are delivered over the public internet and shared across multiple organizations. It offers cost savings but might pose concerns regarding data security and privacy.
Private Cloud:
Dedicated to a single organization, offering enhanced security and control over data and infrastructure. It's more expensive than public cloud but can be tailored to specific business needs.
Hybrid Cloud:
Combines public and private clouds, allowing data and applications to be shared between them. This model offers greater flexibility and optimization of existing infrastructure, security, and compliance.
Community Cloud:
Shared between organizations with common concerns (e.g., security, compliance, jurisdiction). It can be managed internally or by a third-party.
Advantages of Cloud Computing
Cost Efficiency: Reduces the need for significant capital expenditure on hardware and software.
Scalability and Flexibility: Easily scales up or down based on
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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2. Presentation outline;
Introduction
• What is Grid?
• What is Grid Computing?
• How To Use The Internet Grid
• Lay Out Of Internet Grid
• Grid Computing Works
• The Major Business Area For Grid
• Conclusion
3. Introduction
Grid computing or the use of a computational grid is applying the resources of
many computers in a net work to a single problem at the same time
Usually to a scientific or technical problem that requires a great number of computer
processing cycles or access to large amounts of data.
4. WHAT IS GRID?
“Resource sharing & coordinated problem solving in dynamic, multi-institutional
virtual organizations”.
5. What is Grid Computing?
Grid Computing is a technique in which the idle systems in the Network and their
“ wasted “ CPU cycles can be efficiently used by uniting pools of servers, storage
systems and networks into a single large virtual system for resource sharing
dynamically at runtime.
6.
7. Why is Grid Computing?
•In order to avoid this problem we have formulated our own Grid for such an
access to the Internet via an Intranet (LAN).
•By using our Grid these large numbers of files are distributed evenly to all the
systems in the Network by using our Grid.
8. Example :
we have taken into account of a small LAN that consists of around
20 systems out of which 10 systems are idle and 5 systems are using less
amount of CPU(for our consideration) and their CPU cycles are wasted. And
our work begins here, as we are going to efficiently utilize those “wasted CPU
cycles” into “working cycles”.
9.
10. HOW TO USE THE INTRANET GRID:
• To use the Grid Computing follow certain procedures must be taken:
•Security: single sign-on, authentication, authorization, and secure data transfer.
•Resource Management: remote job submission and management.
11. Data Management:
secure and robust data movement.
Fault Detection:
Checking the intranet.
Portability:
C bindings (header files) needed to build and compile programs.
13. The following are the major business areas for grid computing
Life sciences – analyze biological and chemical information.
Financial services- for complex financial models.
Higher education – data and computation-intensive research.
Engineering services – collaborative design and data-intensive testing.
Government – collaboration and agility in both civil and military departments
14. CONCLUSION;
•Grid computing was once said to be fading out but due to the technological convergence
it is blooming once again.
•By implementing our proposed Intranet Grid it is very easy to download multiple files
very fast
•No need to worry about the security as we are authenticating each and every step taking
place in our Grid
•In particular user to access the database. Further implementations could be carried out in
the nearest future