This document provides a survey of techniques for transferring big data. It discusses using grids and parallel transfers to distribute large datasets. Grid computing allows for coordinated sharing of computational and storage resources across distributed systems. Parallel transfer techniques divide files into segments and transfer portions simultaneously from multiple servers to improve download speeds. However, these techniques require significant user involvement. The document then introduces a new NICE model for big data transfers. This store-and-forward approach transfers data to staging servers during periods of low network traffic to avoid impacting other users. It can accommodate different time zones and bandwidth variations between senders and receivers.
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.
Applications of SOA and Web Services in Grid Computingyht4ever
This document discusses applications of service-oriented architecture (SOA) and web services in grid computing. It provides an overview of key concepts like SOA, web services, OGSA, WSRF, and how they have evolved and been applied in grid computing. Specifically, it describes how early specifications like OGSI aimed to standardize grid services but faced issues aligning with web services standards. This led to the development of the Web Services Resource Framework (WSRF) to better integrate grid and web service standards by treating stateful resources as web services.
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.
Grid computing involves distributing computing resources across a network to tackle large problems. The Worldwide LHC Computing Grid (WLCG) was established to support the Large Hadron Collider (LHC) experiment, which produces around 15 petabytes of data annually. The WLCG uses a four-tiered model, with raw data stored at Tier-0 (CERN), copies distributed to Tier-1 data centers, computational resources provided by Tier-2 centers, and Tier-3 facilities providing additional analysis capabilities. This distributed model has proven effective in supporting the first year of LHC data collection and analysis through globally shared computing resources.
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 or network computing is developed to make the available electric power in the similar way
as it is available for the grid. For that we just plug in the power and whoever needs power, may use it. In
grid computing if a system needs more power than available it can share the computing with other
machines connected in a grid. In this way we can use the power of a super computer without a huge cost
and the CPU cycles that were wasted previously can also be utilized. For performing grid computation in
joined computers through the internet, the software must be installed which supports grid computation on
each computer inside the VO. The software handles information queries, storage management, processing
scheduling, authentication and data encryption to ensure information security.
Grid Computing - Collection of computer resources from multiple locationsDibyadip Das
Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files.
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.
Applications of SOA and Web Services in Grid Computingyht4ever
This document discusses applications of service-oriented architecture (SOA) and web services in grid computing. It provides an overview of key concepts like SOA, web services, OGSA, WSRF, and how they have evolved and been applied in grid computing. Specifically, it describes how early specifications like OGSI aimed to standardize grid services but faced issues aligning with web services standards. This led to the development of the Web Services Resource Framework (WSRF) to better integrate grid and web service standards by treating stateful resources as web services.
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.
Grid computing involves distributing computing resources across a network to tackle large problems. The Worldwide LHC Computing Grid (WLCG) was established to support the Large Hadron Collider (LHC) experiment, which produces around 15 petabytes of data annually. The WLCG uses a four-tiered model, with raw data stored at Tier-0 (CERN), copies distributed to Tier-1 data centers, computational resources provided by Tier-2 centers, and Tier-3 facilities providing additional analysis capabilities. This distributed model has proven effective in supporting the first year of LHC data collection and analysis through globally shared computing resources.
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 or network computing is developed to make the available electric power in the similar way
as it is available for the grid. For that we just plug in the power and whoever needs power, may use it. In
grid computing if a system needs more power than available it can share the computing with other
machines connected in a grid. In this way we can use the power of a super computer without a huge cost
and the CPU cycles that were wasted previously can also be utilized. For performing grid computation in
joined computers through the internet, the software must be installed which supports grid computation on
each computer inside the VO. The software handles information queries, storage management, processing
scheduling, authentication and data encryption to ensure information security.
Grid Computing - Collection of computer resources from multiple locationsDibyadip Das
Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files.
The document provides an overview of grid computing, including:
1) Grid computing involves sharing distributed computational resources over a network and providing single login access for users. Resources may be owned by different organizations.
2) Examples of current grids discussed include the NSF PACI/NCSA Alliance Grid, the NSF PACI/SDSC NPACI Grid, and the NASA Information Power Grid.
3) The document also discusses various grid middleware tools and projects for using grid resources, such as Globus, Condor, Legion, Harness, and the Internet Backplane Protocol.
Grid computing has evolved over two generations to address the needs of utilizing widely distributed computing resources effectively. The first generation involved projects in the 1990s that linked supercomputing sites, allowing high-performance applications to leverage computational resources across multiple sites. This included projects like FAFNER, which distributed integer factorization computations via a web interface, and I-WAY, which scheduled jobs across 17 US sites connected by a high-performance network. The second generation focused on developing the necessary infrastructure for grid computing to function on a global scale, addressing issues like heterogeneity, scalability, and adaptability. This required core services for administration, communication, information, and naming across distributed systems.
The document discusses Grid computing and the Globus Toolkit. It provides an overview of Grid computing, describing it as the sharing of computer resources and coordinated problem solving across multiple institutions. It then summarizes the Globus Toolkit, describing it as open source software that provides basic components for Grid functionality, including security, execution management, data management, and monitoring. The Globus Toolkit aims to make it easier to build collaborative distributed applications that can exploit shared Grid infrastructure.
This document discusses grid architecture design. It covers building grid architectures, different types of grids like computational and data grids, common grid topologies including intra, extra, and inter grids. It also outlines the phases and activities in grid design like deciding the grid type, using a methodology of workshops, documentation, and prototyping. Finally, it discusses benefits of grids such as exploiting underutilized resources, enabling parallel processing and collaboration, improving access to and balancing of resources, and better reliability and management.
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.
Centralized Data Verification Scheme for Encrypted Cloud Data ServicesEditor IJMTER
Cloud environment supports data sharing between multiple users. Data integrity is violated
due to hardware / software failures and human errors. Data owners and public verifiers are involved to
efficiently audit cloud data integrity without retrieving the entire data from the cloud server. File and
block signatures are used in the integrity verification process.
“One Ring to RUle Them All” (Oruta) scheme is used for privacy-preserving public auditing process. In
oruta homomorphic authenticators are constructed using Ring Signatures. Ring signatures are used to
compute verification metadata needed to audit the correctness of shared data. The identity of the signer
on each block in shared data is kept private from public verifiers. Homomorphic authenticable ring
signature (HARS) scheme is applied to provide identity privacy with blockless verification. Batch
auditing mechanism supports to perform multiple auditing tasks simultaneously. Oruta is compatible
with random masking to preserve data privacy from public verifiers. Dynamic data management process
is handled with index hash tables. Traceability is not supported in oruta scheme. Data dynamism
sequence is not managed by the system. The system obtains high computational overhead
The proposed system is designed to perform public data verification with privacy. Traceability features
are provided with identity privacy. Group manager or data owner can be allowed to reveal the identity of
the signer based on verification metadata. Data version management mechanism is integrated with the
system.
The document discusses open source grid middleware packages. It describes how grid middleware lies between grid hardware and software, providing services like remote process management, storage allocation, resource allocation, and security. It then summarizes several popular open source middleware packages: BIONIC was developed for the SETI@HOME project; UNICORN provides a Java-based integrated grid computing environment; GLOBUS is a widely used toolkit developed by the Globus Alliance; and Condor-G builds a high-throughput computing environment from Condor.
Grid computing is a model of distributed computing that uses geographically and administratively disparate resources to solve large problems. It involves sharing computing power, data, and other resources across organizational boundaries. Key aspects include applying resources from many computers to a single problem, combining resources from multiple administrative domains for tasks requiring large processing power or data, and using middleware to coordinate resources as a virtual system. The document then discusses definitions of grid computing from various organizations and the core functional requirements and characteristics needed for grid applications and users.
DISTRIBUTED AND BIG DATA STORAGE MANAGEMENT IN GRID COMPUTINGijgca
Big data storage management is one of the most challenging issues for Grid computing environments, since large amount of data intensive applications frequently involve a high degree of data access locality. Grid applications typically deal with large amounts of data. In traditional approaches high-performance computing consists dedicated servers that are used to data storage and data replication. In this paper we present a new mechanism for distributed and big data storage and resource discovery services. Here we proposed an architecture named Dynamic and Scalable Storage Management (DSSM) architecture in grid environments. This allows in grid computing not only sharing the computational cycles, but also share the storage space. The storage can be transparently accessed from any grid machine, allowing easy data sharing among grid users and applications. The concept of virtual ids that, allows the creation of virtual spaces has been introduced and used. The DSSM divides all Grid Oriented Storage devices (nodes) into multiple geographically distributed domains and to facilitate the locality and simplify the intra-domain storage management. Grid service based storage resources are adopted to stack simple modular service piece by piece as demand grows. To this end, we propose four axes that define: DSSM architecture and algorithms description, Storage resources and resource discovery into Grid service, Evaluate purpose prototype system, dynamically, scalability, and bandwidth, and Discuss results. Algorithms at bottom and upper level for standardization dynamic and scalable storage management, along with higher bandwidths have been designed.
Grid computing involves linking together distributed computer resources from multiple administrative domains to achieve a common goal. Resources in a grid are heterogeneous and geographically dispersed. A grid differs from a cluster in that it provides a consistent, dependable, and transparent collection of computing resources across wide distances. Grid infrastructure must respect local autonomy, handle heterogeneous hardware, and be resilient and dynamic.
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.
Grid computing allows for the sharing of computer resources across a network. It utilizes both reliable tightly-coupled cluster resources as well as loosely-coupled unreliable machines. The grid system balances resource usage to provide quality of service to participants. Grid computing works by having at least one administrative computer and middleware that allows computers on the network to share processing power and data storage. It has advantages like improved efficiency, resilience, and ability to handle large applications, but also challenges around resource sharing and licensing across multiple servers.
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.
The huge volume of text documents available on the internet has made it difficult to find valuable
information for specific users. In fact, the need for efficient applications to extract interested knowledge
from textual documents is vitally important. This paper addresses the problem of responding to user
queries by fetching the most relevant documents from a clustered set of documents. For this purpose, a
cluster-based information retrieval framework was proposed in this paper, in order to design and develop
a system for analysing and extracting useful patterns from text documents. In this approach, a pre-
processing step is first performed to find frequent and high-utility patterns in the data set. Then a Vector
Space Model (VSM) is performed to represent the dataset. The system was implemented through two main
phases. In phase 1, the clustering analysis process is designed and implemented to group documents into
several clusters, while in phase 2, an information retrieval process was implemented to rank clusters
according to the user queries in order to retrieve the relevant documents from specific clusters deemed
relevant to the query. Then the results are evaluated according to evaluation criteria. Recall and Precision
(P@5, P@10) of the retrieved results. P@5 was 0.660 and P@10 was 0.655.
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.
Grid computing allows for the sharing and aggregation of distributed computing resources like computers, networks, databases and instruments. It provides a large virtual computing system for end users and applications. Key characteristics include facilitating solutions to large, complex problems across locations and organizations through integrated and collaborative use of heterogeneous resources. Popular applications include medical research, astronomy, climate modeling and more. Examples of operational grids discussed are TeraGrid, Pauá Grid Project and academic research projects like SETI@home.
This document discusses the evolution of distributed computing from centralized mainframes to modern cloud, grid, and parallel computing systems. It covers key topics like:
- The shift from high-performance computing (HPC) to high-throughput computing (HTC) and new paradigms like cloud, grid, and peer-to-peer networks.
- The progression of computing platforms and generations from mainframes to personal computers to modern distributed systems.
- Degrees of parallelism including bit-level, instruction-level, data-level, task-level, and job-level and how these have improved over time.
- Major applications that have driven distributed computing including science, engineering, banking, and
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.
A Literature Survey on Resource Management Techniques, Issues and Challenges ...TELKOMNIKA JOURNAL
Cloud computing is a large scale distributed computing which provides on demand services for
clients. Cloud Clients use web browsers, mobile apps, thin clients, or terminal emulators to request and
control their cloud resources at any time and anywhere through the network. As many companies are
shifting their data to cloud and as many people are being aware of the advantages of storing data to cloud,
there is increasing number of cloud computing infrastructure and large amount of data which lead to the
complexity management for cloud providers. We surveyed the state-of-the-art resource management
techniques for IaaS (infrastructure as a service) in cloud computing. Then we put forward different major
issues in the deployment of the cloud infrastructure in order to avoid poor service delivery in cloud
computing.
This document discusses security issues related to cloud computing, MapReduce, and Hadoop environments. It provides an overview of key concepts like cloud computing, big data, Hadoop, MapReduce, and HDFS. It then discusses the motivation for securing these systems and related work done by others. Finally, it outlines several challenges to security in cloud computing environments, including issues related to distributed nodes, distributed data, internode communication, data protection, administrative rights, authentication, and logging.
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.
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 document provides an overview of grid computing, including:
1) Grid computing involves sharing distributed computational resources over a network and providing single login access for users. Resources may be owned by different organizations.
2) Examples of current grids discussed include the NSF PACI/NCSA Alliance Grid, the NSF PACI/SDSC NPACI Grid, and the NASA Information Power Grid.
3) The document also discusses various grid middleware tools and projects for using grid resources, such as Globus, Condor, Legion, Harness, and the Internet Backplane Protocol.
Grid computing has evolved over two generations to address the needs of utilizing widely distributed computing resources effectively. The first generation involved projects in the 1990s that linked supercomputing sites, allowing high-performance applications to leverage computational resources across multiple sites. This included projects like FAFNER, which distributed integer factorization computations via a web interface, and I-WAY, which scheduled jobs across 17 US sites connected by a high-performance network. The second generation focused on developing the necessary infrastructure for grid computing to function on a global scale, addressing issues like heterogeneity, scalability, and adaptability. This required core services for administration, communication, information, and naming across distributed systems.
The document discusses Grid computing and the Globus Toolkit. It provides an overview of Grid computing, describing it as the sharing of computer resources and coordinated problem solving across multiple institutions. It then summarizes the Globus Toolkit, describing it as open source software that provides basic components for Grid functionality, including security, execution management, data management, and monitoring. The Globus Toolkit aims to make it easier to build collaborative distributed applications that can exploit shared Grid infrastructure.
This document discusses grid architecture design. It covers building grid architectures, different types of grids like computational and data grids, common grid topologies including intra, extra, and inter grids. It also outlines the phases and activities in grid design like deciding the grid type, using a methodology of workshops, documentation, and prototyping. Finally, it discusses benefits of grids such as exploiting underutilized resources, enabling parallel processing and collaboration, improving access to and balancing of resources, and better reliability and management.
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.
Centralized Data Verification Scheme for Encrypted Cloud Data ServicesEditor IJMTER
Cloud environment supports data sharing between multiple users. Data integrity is violated
due to hardware / software failures and human errors. Data owners and public verifiers are involved to
efficiently audit cloud data integrity without retrieving the entire data from the cloud server. File and
block signatures are used in the integrity verification process.
“One Ring to RUle Them All” (Oruta) scheme is used for privacy-preserving public auditing process. In
oruta homomorphic authenticators are constructed using Ring Signatures. Ring signatures are used to
compute verification metadata needed to audit the correctness of shared data. The identity of the signer
on each block in shared data is kept private from public verifiers. Homomorphic authenticable ring
signature (HARS) scheme is applied to provide identity privacy with blockless verification. Batch
auditing mechanism supports to perform multiple auditing tasks simultaneously. Oruta is compatible
with random masking to preserve data privacy from public verifiers. Dynamic data management process
is handled with index hash tables. Traceability is not supported in oruta scheme. Data dynamism
sequence is not managed by the system. The system obtains high computational overhead
The proposed system is designed to perform public data verification with privacy. Traceability features
are provided with identity privacy. Group manager or data owner can be allowed to reveal the identity of
the signer based on verification metadata. Data version management mechanism is integrated with the
system.
The document discusses open source grid middleware packages. It describes how grid middleware lies between grid hardware and software, providing services like remote process management, storage allocation, resource allocation, and security. It then summarizes several popular open source middleware packages: BIONIC was developed for the SETI@HOME project; UNICORN provides a Java-based integrated grid computing environment; GLOBUS is a widely used toolkit developed by the Globus Alliance; and Condor-G builds a high-throughput computing environment from Condor.
Grid computing is a model of distributed computing that uses geographically and administratively disparate resources to solve large problems. It involves sharing computing power, data, and other resources across organizational boundaries. Key aspects include applying resources from many computers to a single problem, combining resources from multiple administrative domains for tasks requiring large processing power or data, and using middleware to coordinate resources as a virtual system. The document then discusses definitions of grid computing from various organizations and the core functional requirements and characteristics needed for grid applications and users.
DISTRIBUTED AND BIG DATA STORAGE MANAGEMENT IN GRID COMPUTINGijgca
Big data storage management is one of the most challenging issues for Grid computing environments, since large amount of data intensive applications frequently involve a high degree of data access locality. Grid applications typically deal with large amounts of data. In traditional approaches high-performance computing consists dedicated servers that are used to data storage and data replication. In this paper we present a new mechanism for distributed and big data storage and resource discovery services. Here we proposed an architecture named Dynamic and Scalable Storage Management (DSSM) architecture in grid environments. This allows in grid computing not only sharing the computational cycles, but also share the storage space. The storage can be transparently accessed from any grid machine, allowing easy data sharing among grid users and applications. The concept of virtual ids that, allows the creation of virtual spaces has been introduced and used. The DSSM divides all Grid Oriented Storage devices (nodes) into multiple geographically distributed domains and to facilitate the locality and simplify the intra-domain storage management. Grid service based storage resources are adopted to stack simple modular service piece by piece as demand grows. To this end, we propose four axes that define: DSSM architecture and algorithms description, Storage resources and resource discovery into Grid service, Evaluate purpose prototype system, dynamically, scalability, and bandwidth, and Discuss results. Algorithms at bottom and upper level for standardization dynamic and scalable storage management, along with higher bandwidths have been designed.
Grid computing involves linking together distributed computer resources from multiple administrative domains to achieve a common goal. Resources in a grid are heterogeneous and geographically dispersed. A grid differs from a cluster in that it provides a consistent, dependable, and transparent collection of computing resources across wide distances. Grid infrastructure must respect local autonomy, handle heterogeneous hardware, and be resilient and dynamic.
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.
Grid computing allows for the sharing of computer resources across a network. It utilizes both reliable tightly-coupled cluster resources as well as loosely-coupled unreliable machines. The grid system balances resource usage to provide quality of service to participants. Grid computing works by having at least one administrative computer and middleware that allows computers on the network to share processing power and data storage. It has advantages like improved efficiency, resilience, and ability to handle large applications, but also challenges around resource sharing and licensing across multiple servers.
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.
The huge volume of text documents available on the internet has made it difficult to find valuable
information for specific users. In fact, the need for efficient applications to extract interested knowledge
from textual documents is vitally important. This paper addresses the problem of responding to user
queries by fetching the most relevant documents from a clustered set of documents. For this purpose, a
cluster-based information retrieval framework was proposed in this paper, in order to design and develop
a system for analysing and extracting useful patterns from text documents. In this approach, a pre-
processing step is first performed to find frequent and high-utility patterns in the data set. Then a Vector
Space Model (VSM) is performed to represent the dataset. The system was implemented through two main
phases. In phase 1, the clustering analysis process is designed and implemented to group documents into
several clusters, while in phase 2, an information retrieval process was implemented to rank clusters
according to the user queries in order to retrieve the relevant documents from specific clusters deemed
relevant to the query. Then the results are evaluated according to evaluation criteria. Recall and Precision
(P@5, P@10) of the retrieved results. P@5 was 0.660 and P@10 was 0.655.
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.
Grid computing allows for the sharing and aggregation of distributed computing resources like computers, networks, databases and instruments. It provides a large virtual computing system for end users and applications. Key characteristics include facilitating solutions to large, complex problems across locations and organizations through integrated and collaborative use of heterogeneous resources. Popular applications include medical research, astronomy, climate modeling and more. Examples of operational grids discussed are TeraGrid, Pauá Grid Project and academic research projects like SETI@home.
This document discusses the evolution of distributed computing from centralized mainframes to modern cloud, grid, and parallel computing systems. It covers key topics like:
- The shift from high-performance computing (HPC) to high-throughput computing (HTC) and new paradigms like cloud, grid, and peer-to-peer networks.
- The progression of computing platforms and generations from mainframes to personal computers to modern distributed systems.
- Degrees of parallelism including bit-level, instruction-level, data-level, task-level, and job-level and how these have improved over time.
- Major applications that have driven distributed computing including science, engineering, banking, and
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.
A Literature Survey on Resource Management Techniques, Issues and Challenges ...TELKOMNIKA JOURNAL
Cloud computing is a large scale distributed computing which provides on demand services for
clients. Cloud Clients use web browsers, mobile apps, thin clients, or terminal emulators to request and
control their cloud resources at any time and anywhere through the network. As many companies are
shifting their data to cloud and as many people are being aware of the advantages of storing data to cloud,
there is increasing number of cloud computing infrastructure and large amount of data which lead to the
complexity management for cloud providers. We surveyed the state-of-the-art resource management
techniques for IaaS (infrastructure as a service) in cloud computing. Then we put forward different major
issues in the deployment of the cloud infrastructure in order to avoid poor service delivery in cloud
computing.
This document discusses security issues related to cloud computing, MapReduce, and Hadoop environments. It provides an overview of key concepts like cloud computing, big data, Hadoop, MapReduce, and HDFS. It then discusses the motivation for securing these systems and related work done by others. Finally, it outlines several challenges to security in cloud computing environments, including issues related to distributed nodes, distributed data, internode communication, data protection, administrative rights, authentication, and logging.
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.
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.
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.
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.
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.
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.
This document summarizes a research article that analyzed the surface degradation of polypropylene (PP) and high-density polyethylene (HDPE) composites with 5% and 10% banana fiber loads when immersed in distilled water, ethanol, and sodium chloride solutions for up to 200 days. Samples were weighed over time to measure degradation and absorption in different environments. Surface degradation was also evaluated using scanning electron microscopy. The researchers found that longer immersion times led to greater material degradation regardless of environment.
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.
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.
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.
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.
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.
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.
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.
Kelompok 1 matematika titik, garis, bidang dan kurvaRestu Waras Toto
Dokumen tersebut membahas tentang kelompok 1 yang terdiri dari 6 orang siswa yang membahas tentang titik, garis, kurva, dan bidang. Dijelaskan pula pengertian dan macam-macam dari setiap bahasan tersebut.
This short document promotes creating presentations using Haiku Deck on SlideShare. It encourages the reader to get started making their own Haiku Deck presentation by providing a button to click to begin the process. The document is advertising the ability to easily create presentations on SlideShare using Haiku Deck.
Este documento presenta 10 pistas sobre obras de arte público, cada una con la fotografía de la obra, su título, autor, una breve reseña biográfica del autor y una descripción de aspectos artísticos y expresivos de la obra. Las obras cubren diferentes medios como murales, esculturas y estructuras de metal y tratan temas como la identidad cultural, la crítica social y la transfiguración humana.
Opticon Marketing Marketing for Chiropractics PowerPointOpticon Marketing
This document contains contact information for Opticon Marketing, including their email address, phone number, and website address, repeated multiple times.
TCAIC II - Trabajo Práctico Modelo 02 BIS - Gestión de Proyectosmnllorente
El documento describe un proyecto para organizar una jornada de difusión de la carrera de ingeniería civil en la Universidad Nacional de La Rioja. El proyecto incluye tareas como conseguir permisos, organizar las jornadas, difusión, inscripciones y la realización de la jornada. Se especifican las fechas de inicio y duración de cada tarea, así como los recursos y costos involucrados. Se pide planificar el proyecto en Microsoft Project según las instrucciones provistas.
CONTENT BASED DATA TRANSFER MECHANISM FOR EFFICIENT BULK DATA TRANSFER IN GRI...ijgca
A new class of Data Grid infrastructure is needed to support management, transport, distributed access, and analysis of terabyte and peta byte of data collections by thousands of users. Even though some of the existing data management systems (DMS) of Grid computing infrastructures provides methodologies to handle bulk data transfer. These technologies are not usable in addressing some kind of simultaneous data
access requirements. Often, in most of the scientific computing environments, a common data will be needed to access from different locations. Further, most of such computing entities will wait for a common scientific data (such as a data belonging to an astronomical phenomenon) which will be published only
when it is available. These kinds of data access needs were not yet addressed in the design of data component Grid Access to Secondary Storage (GASS) or GridFTP. In this paper, we address an application layer content based data transfer scheme for grid computing environments. By using the
proposed scheme in a grid computing environment, we can simultaneously move bulk data in an efficient way using simple subscribe and publish mechanism.
BIG DATA NETWORKING: REQUIREMENTS, ARCHITECTURE AND ISSUESijwmn
The document discusses requirements, architectures, and issues related to networking for big data. It begins by outlining the network requirements for big data, including resiliency, congestion mitigation, performance consistency, scalability, partitioning, and application awareness. It then describes the MapReduce and Hadoop architectures commonly used for big data processing and some of the research challenges they present for networks. Finally, it discusses fabric network infrastructures and software defined networks that can help address networking needs for big data.
The document discusses a system for integrating structured and unstructured data from heterogeneous environments. The system uses OGSA-DAI services and the Globus Toolkit to provide an abstraction layer that allows database operations on both structured data from databases and unstructured file-based data. It generates metadata from unstructured data and configures the abstraction layer to query across the different data sources. This provides users an integrated view of both structured and unstructured data through a single interface.
This document discusses a system for integrating structured and unstructured data from heterogeneous sources and allowing querying of both data types. The system uses Open Grid Services Architecture Data Access and Integration (OGSA-DAI) services supported by the Globus Toolkit to provide a data abstraction layer. This layer generates metadata from unstructured files to allow database operations on both structured and unstructured data. The system provides a unified interface for users to search and retrieve data from different sources in various formats.
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 document discusses grid computing, which connects many computers together into a network to solve large problems requiring massive computing power. It provides high-speed connections that are 10,000 times faster than broadband. Grid computing shares and aggregates resources like supercomputers, storage, and data sources across geographic locations. It has the potential to greatly change business, science, and society by enabling new forms of collaboration and computation. Developers must design applications to take advantage of this distributed, parallel environment.
A Study of Protocols for Grid Computing EnvironmentCSCJournals
This document summarizes a study of communication protocols for grid computing environments. It discusses the limitations of TCP for high bandwidth-delay networks and the need for new protocols to efficiently transfer bulk data across long distances. It categorizes various protocols that have been proposed into TCP-based, UDP-based, and application-layer protocols and evaluates them based on their operation, congestion control, throughput, fairness and other factors. The document also outlines issues in designing high performance protocols for grid computing and reviews several TCP variants and reliable transport protocols developed to improve performance over high-speed networks.
This document provides a review of grid computing. It begins with definitions and explanations of grid computing and its key characteristics including decentralized control, open standards, and coordinated resource sharing across organizations. The document then discusses the types of grids, architectures, benefits including improved resource utilization and fault tolerance techniques like checkpointing and replication. It also reviews the evolution of grid technologies like Globus Toolkit and the Open Grid Services Architecture (OGSA). The challenges of programming and managing resources across administrative domains in grid environments are also summarized.
Internet is the networking infrastructure which helps in connecting many users through interconnected networks through which users can communicate to each other. The World Wide Web is built on top of the internet to share information. The grid is again a service that is built on top of internet but is able to share computational power, databases, disk storage and software applications. The paper mainly focuses on significance Grid computing, its architecture, the grid middleware Globus toolkit and wireless grid computing.
Survey on Synchronizing File Operations Along with Storage Scalable MechanismIRJET Journal
The document summarizes research on efficient file operations and storage scalability mechanisms. It discusses how data is divided into chunks and distributed to nodes for transmission in peer-to-peer networks. The proposed system aims to provide efficient load balancing, eliminate single points of failure, and ensure synchronization and security during data transmission. It uses synchronization algorithms and a hybrid distribution model combining features of peer-to-peer and client-server networks. The system is designed to securely handle insertions, deletions, splits, and concatenations of file chunks in a distributed storage system.
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.
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.
This document provides a survey of file replication techniques used in grid systems. It begins with an introduction to grid systems and discusses their use of replication to improve response times and reduce bandwidth consumption. It then categorizes replication techniques as static or dynamic and describes challenges of replication including maintaining consistency and overhead. The document surveys various replication strategies for different grid topologies like peer-to-peer, tree and hybrid. It evaluates strategies based on factors like access latency, bandwidth consumption and fault tolerance. Specific replication techniques are discussed for peer-to-peer architectures aimed at availability, placement strategies and balancing workloads.
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
MataNui - Building a Grid Data Infrastructure that "doesn't suck!"Guy K. Kloss
This document discusses the development of a grid data infrastructure called MataNui to manage large amounts of observational astronomical data and metadata from a collaboration between researchers in New Zealand and Japan. The infrastructure uses existing open-source tools like MongoDB, GridFTP, and the DataFinder GUI client to allow distributed storage and access of data while meeting requirements like handling large data volumes, metadata, and remote access. This approach provides a robust, reusable, and user-friendly system to address common data management challenges in scientific collaborations.
This document summarizes a review paper on grid computing. It begins with an introduction to grid computing, describing it as a system that combines distributed computing resources to solve large-scale computational problems. It then discusses the layered grid architecture, including the fabric, connectivity, resource, and collective layers. Next, it outlines different types of grids like computational, data, service, and collaborative grids. It proceeds to examine challenges in grid computing such as security, resource discovery, and heterogeneity. It also describes characteristics of grids like their heterogeneous and user-centric nature. The document concludes by covering topics like grid resource management and security issues in grids.
‘Grids’areanapproachforbuildingdynamicallyconstructedproblem-solvingenvironmentsusing
geographically and organizationally dispersed,
high-performance computing and
data handling resources.
Gridsalsoprovideimportantinfrastructuresupportingmulti-institutionalcollaboration.
The document discusses security issues in distributed database systems. It begins by defining distributed databases and their architecture. It then discusses three main security aspects: access control, authentication, and encryption. The document also discusses distributed database system design considerations like concurrency control and data fragmentation. Emerging security tools for distributed databases mentioned include data warehousing, data mining, collaborative computing, distributed object systems, and web applications. Maintaining security when building and querying data warehouses from multiple sources is highlighted as a key challenge.
Grid computing allows for sharing and coordinated use of diverse computing resources virtually. It provides uniform access to computational resources over the Internet similar to how the web provides access to documents. Key motivations for grid computing include enabling large-scale science through geographically dispersed resources. Grid architectures have fabric, connectivity, resource, collective, and application layers. The Globus Toolkit is commonly used open source software that provides components for security, data management, scheduling, and more. Grids are used in various domains like earthquake and climate simulation.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
The Microsoft 365 Migration Tutorial For Beginner.pptx
Dq36708711
1. Kiran Kumar Reddi et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.708-711
RESEARCH ARTICLE
www.ijera.com
OPEN ACCESS
Different Techniques to Transfer Big data: a Survey
Kiran Kumar Reddi1 Dnvsls Indira2
1
2
Department of Computer Science, Krishna University, Machilipatnam, Krishna Dt., AP- 521001
Research Scholar, Department of CS, Krishna University, Machilipatnam, Krishna Dt., AP- 521001
ABSTRACT
Now a days the world is moving around the social networks, i.e most of the people are interacting with each
other via internet only. Transmitting data via the Internet is a routine and common task for users today.
Transferring a gigabyte of data in an entire day was normal, however users are now transmitting multiple
gigabytes in a single hour. The Big Data is the combination of structured, semi-structured, unstructured,
homogeneous and heterogeneous data. With the influx of big data and massive scientific data sets that are
measured in tens of petabytes, a user has the propensity to transfer even larger amounts of data. When
transferring data sets of this magnitude on public or shared networks, the performance of all workloads in the
system will be impacted. This paper addresses the issues and challenges inherent with transferring big data
over networks. A survey of current transfer techniques is provided in this paper.
Key words: Big Data, Grid, Parallel Transfer, NICE
is a complicated and time-consuming process.
These long duration transfers could take tens of hours
I.
INTRODUCTION
to several days and a normal "one click and wait"
Over the past several years there has been
method will not suffice. During the course of the
a tremendous increase in the amount of data being
transfer, servers may go off-line and network
transferred between Internet users. Escalating usage
conditions may change that either hinder or stop the
of streaming multimedia and other Internet based
transfer completely. The user needs to know how to
applications has contributed to this surge in data
maintain the data transmission until completion. This
transmissions. Another facet of the increase is
paper gives an overview of previously existing
due to the expansion of Big Data, which refers
techniques to transfer files around the world.
to data sets that are an order of magnitude larger
This section concentrates on grid computing
than the standard file transmitted via the Internet.
to distribute data and parallel transfer techniques.
Big Data can range in size from hundreds of
gigabytes to petabytes. Today everything is being
2.1 GRIDS
stored digitally.
Within the past decade,
Grid computing has emerged as a
everything from banking transactions to medical
framework
for
aggregating
geographically
history has migrated to digital storage. This
distributed, heterogeneous resources that enables
change from physical documents to digital files has
secure and unified access to computing, storage and
necessitated the creation of large data sets and
networking resources (1). Grid applications have
consequently the transfer of large amounts of data.
vast datasets and/or complex computations that
There is no sign that the amount of data being stored
require
secure
resource
sharing
among
or transmitted by users is steady or even decreasing.
geographically distributed systems. The term "Grid"
Every year average Internet users are moving
was inspired by the electrical grid system, where a
more and more data through their Internet
user can plug in an appliance to a universal socket
connections. Depending on the bandwidth of these
and have instant access to power without knowing
connections and the size of the data sets being
exactly where that power was generated or how
transmitted, the duration of transfers could
it came to reach the socket (1). The vision for
potentially be measured in days or even weeks.
grids was similar.
A user could simply access as
There exists a need for an efficient transfer technique
much computing power as required through a
that can move large amounts of data quickly and
common interface without concern for who was
easily without impacting other users or
providing the resources. Currently, grids have not
applications.
This paper presents different
yet reached that level of simplicity. Grids offer
techniques to transfer the data across the globe. This
coordinated resource sharing and problem solving in
paper concentrated on survey on grids, parallel
dynamic, multi institutional virtual organizations (2).
techniques to transfer data on internet and a new
A virtual organization (VO) comprises a set of
model to transfer big data efficiently across the globe.
individuals and/or institutions having access to
computers, software, data, and other resources for
II.
RELATED WORK
collaborative problem-solving or other purposes (3).
Retrieving large data files (GB, TB, PB)
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A grid can also be defined as a system that
coordinates resources that are not subject to
centralized control, using standard, open, generalpurpose protocols and interfaces in order to
deliver nontrivial qualities of service (4). Data grids,
a specialized extension of grid computing, are
responsible for providing the infrastructure and
services to access, transfer, and modify massive
datasets stored in distributed storage resources (5).
They allow users to access computational and storage
resources in order to execute data-intensive
applications on remote data. The objective of a data
grid system is to integrate heterogeneous data files
stored in a large number of geographically distributed
sites into a single virtual data management system
and to provide diverse services to fit the needs of
high-performance distributed and data intensive
computing (5).
2.2 GRID MIDDLEWARE
The sharing of resources in a grid is facilitated
and controlled by a set of services that allow
resources to be discovered, accessed, allocated,
monitored, and accounted for, regardless of the
their physical location (6). Since these services
create a layer between physical resources and
applications, they are often referred to as Grid
Middleware.
Every grid has different service
requirements, therefore the architecture and grid
middleware implementation of every grid can vary.
The middleware of many grids is based on the
software architecture called the Globus Toolkit
(7).
The toolkit is a set of libraries and
programs that address common problems that
occur when building distributed system services
and applications (8). It provides a set of
infrastructure services that implement interfaces for
managing computational, storage,
and other
resources. The Globus Toolkit provides all of these
services and it is left to grid administrators to
determine whether or not to include certain services
in their grid implementation. These are a few of the
well known and widely used grids that deploy
the Globus Toolkit: TeraGrid, Open Science Grid,
EGEE, Worldwide LHC Computing Grid (WLCG)
(8), China National Grid, UK National Grid Service
and NAREGI (9). The architecture of the Globus
Toolkit contains several components, each of which
is responsible for different grid functions. A few of
these services are (7):
• Grid Resource Allocation and management
(GRAM) - This service initiates, monitors, and
manages the execution of computations on
remote computers. It allows a user to specify:
the quantity and type of resources needed, the
data sets required for their computation, the
executable application to be run, the
necessary security credentials, and the job
persistence requirements.
• Data access and movement - The reliable file
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transfer (RFT) service is provided to ensure that
data is successfully transferred from one location
to another.
• Replica Management - This service keeps track
of all replicas and their content using a replica
location service (RLS) and a data replication
service (DRS).
• Monitoring and Discovery - Multiple services
collect and process information about the
configuration and state of all resources to enable
monitoring of system status.
• Security - Services establish the identity of
users or services (authentication), protect
communications, and determine who is
allowed
to
perform
what
actions
(authorization), as well as manage user
credentials and maintain group membership
information.
There are several grid applications
available for moving data from one location in a
grid to another. The most widely-used data
movement tool, which is also a component of the
Globus Toolkit, is called GridFTP (10; 11). It is
an extension of the File Transfer Protocol (FTP) and
was designed specifically for grid environments.
Several data retrieval techniques developed
specifically for retrieving large files in grid
computing environments. The sizes of data files
requested in grids are much larger than normal web
data requests. It is not uncommon for a grid data
file size to be measured in gigabytes or terabytes.
Users want to be able to download these files as
quickly as possible, by any means necessary.
Since utilizing a single server can be limiting,
retrieving data from multiple servers in a parallel
(also known as data co-allocation) has been
suggested as an alternative.
III.
PARALLEL TRANSMISSION
TECHNIQUES USED ON THE
INTERNET
Rodriguez
and
Biersack
present
mechanisms for parallel access to data on the
Internet (11). They develop two different parallelaccess schemes: history-based and dynamic. The
goal for all of their schemes is to balance the load
amongst all available servers by allocating a
workload to each replica that is proportional to its
service rate. The authors state that parallel access has
additional overhead in comparison to a single access.
The additional overhead occurs when multiple
connections are opened and extra traffic is generated
to perform block requests. In order to minimize
these overhead costs, these techniques should only
be utilized for larger files. Their history-based
technique utilizes a database with information about
previous rates from the different servers to the
receiver in order to estimate future transfers. Using
these estimates the algorithm assigns varying
portions of the file to each replica with the goal that
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all servers will finish transferring the portions at the
same time. The authors evaluate their history-based
technique using live webservers on the Internet
distributed across the world. Due to the presence of
other Internet traffic, the authors found that the
performance of their technique varied at different
times of the day. They found that network
conditions rapidly change and estimating the
transfer rate to every server using past histories
results in poor estimates. Their results show that
during peak traffic times when transfer rates vary
dramatically and historical information is not a
good indicator of future performance, their historybased parallel
access technique
has higher
download times than clients accessing a single
server. In response to the performance of their
history-based technique, the authors develop a
dynamic technique that adjusts to changing
network conditions. Their dynamic technique
divides the desired file into a fixed number of
equal sized blocks. The client requests one block
from every replica. When a server completes a
request, another block is assigned. When there are a
small number of blocks outstanding, idle servers
are requested to deliver blocks that have already
been assigned to another server, but have yet to
be received. There will then be multiple servers
working on the same requests. The authors state that
the bandwidth wasted on overlapping these requests
is smaller than the worst-case scenario of waiting
for the slowest server to deliver the last block. To
further enhance the performance of their
technique, they utilize TCP-persistent connections
between the client and every server to minimize
the overhead of opening multiple TCP connections.
They also propose pipelining the requests to each
server in order to decrease inter block idle times.
With pipelining, a new block request is sent to a
server before the previous block request is
completely received. In the evaluations of their
dynamic technique, the authors find that there is
a significant speedup in comparison to a single
server access. Since the dynamic technique is not
relying on historical information and can adapt to
changing
network conditions, it
has greater
performance than requesting data from a single
server even under peak traffic conditions. They also
observe that the transfer time of a dynamic parallel
access is very close to the optimum transfer time.
Utilizing request pipelining, the authors demonstrate
that their technique would be almost equal to the
optimal transfer time. These advanced retrieval
techniques allow users to utilize multiple resources
simultaneously.
These
advanced
techniques
provide improved performance for users, however
they are quite complicated to implement and use.
They require significant user involvement and
require multiple user decisions that can
dramatically affect the performance of the transfer.
A user needs know how in order to make these
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techniques function properly and efficiently. Another
configuration option that is frequently left for the
user to determine is segment size.
In some
advanced techniques, the data file is divided into
small portions called segments. The segment size is
often left for the user to decide and the size chosen
can affect the performance of the transfer.
Determining the appropriate segment size is not a
simple task. If the size is too small, a server may
receive hundreds to thousands of requests for
portions of a single file. This will result in longer
disk service times at a server, as the number of
users increases. A server's storage system can best
service requests if it has greater knowledge of a user's
workload. It can better schedule reading from the
hard disks, as well as take advantage of prefetching and caching strategies.
IV.
NICE MODEL
A new, NICE Model for Big Data
transfers, which is based on a store-and-forward
model instead of an end-to-end approach, is
presented. This nice model ensures that Big Data
transfers only occur during low demand periods when
there is idle bandwidth that can be repurposed for
these large transfers. Under this model, Big Data are
transmitted when the Internet traffic at the senders
LAN is low. If the Internet traffic at the receivers
LAN is high at this time, then the data are stored at a
staging server and later transmitted to the receiver.
Similar to the nice command in Linux, a transfer
tool based on the nice algorithm, gives itself low
priority and is nice to other applications using the
Internet. The overall goal is to develop an
application that can transmit big data via the
Internet for all users with in any campus.
Fig 1: Nice Model architecture
In order to avail of maximum installed
bandwidth without impacting other users, the key is
to open multiple transmission streams during low
traffic. If the sender and receiver are in the
same time zone then a direct transmission from
sender to receiver is feasible. If the sender and
receiver are in different time zones, then the low
traffic periods at the two end points do not
coincide. In this instance, the file is transmitted
from the sender to one or more staging server(s),
placed in the Internet zone. Depending on the
Internet configuration between the sender and
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receiver, the file may be transmitted to a single
staging server via multiple streams or the file
may be divided and parts of the file are transmitted
concurrently to multiple staging servers. When the
receiver's LAN traffic is low, the file can be
transmitted from the staging server(s) to the
receiver. A file transmission tool such as GridFTP
could be used for the transmission from the sender to
the staging server(s) and from the staging server(s) to
the receiver.
V.
CONCLUSION & FUTURE WORK
Big data transfers via the Internet are
not a commonplace task for most users today.
Currently, there are no tools to facilitate these kinds
of transmissions. The task of transferring massive
amounts of data across the country or even the globe
is a challenging and daunting undertaking for any
user.
As the popularity of distributed storage
propagates and the amount of scientific data
continues to surge, the demand for big data transfers
will grow at a tremendous rate. The existing tools
for moving large amounts of data are based on
the parallel model, which is designed to grab as
much bandwidth as possible by opening
concurrent data streams. This greedy approach
may be good for a single user's transfer,
however it is not scalable for multiple users on a
shared network. The entire system suffers when
users attempt to grab bandwidth. Parallel system
supports multiple servers to transfer data via internet
but implementation of that system is very difficult.
The Nice model can be used for big data transfers.
Under this model, these transfers are relegated to low
demand periods when there is ample, idle bandwidth
available.
This bandwidth can then be
repurposed for big data transmissions without
impacting other users in the system. Since the
nice model uses a store-and-forward approach by
utilizing staging servers, the model is able to
accommodate differences in
time zones and
variations in bandwidth. There is a lot of challenging
issues like network protocols, security, compression
techniques, routing algorithms, Traffic Monitoring
and bandwidth management devices. New algorithms
are required to transfer big data around the globe by
concentrating all the above issues.
[3]
[4]
[5]
[6]
[7]
[8]
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[11]
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