This article discusses opportunities and challenges for efficient parallel data processing in cloud computing environments. It introduces Nephele, a new data processing framework designed specifically for clouds. Nephele is the first framework to leverage dynamic resource allocation in clouds for task scheduling and execution. The article analyzes how existing frameworks assume static resource environments unlike clouds, and how Nephele addresses this by dynamically allocating different compute resources during job execution. It then provides initial performance results for Nephele and compares it to Hadoop for MapReduce-style jobs on cloud infrastructure.
Dear Students
Ingenious techno Solution offers an expertise guidance on you Final Year IEEE & Non- IEEE Projects on the following domain
JAVA
.NET
EMBEDDED SYSTEMS
ROBOTICS
MECHANICAL
MATLAB etc
For further details contact us:
enquiry@ingenioustech.in
044-42046028 or 8428302179.
Ingenious Techno Solution
#241/85, 4th floor
Rangarajapuram main road,
Kodambakkam (Power House)
http://www.ingenioustech.in/
Research Inventy : International Journal of Engineering and Scienceinventy
This document discusses exploiting dynamic resource allocation in cloud environments for efficient parallel data processing. It presents Nephele, a new data processing framework designed for clouds. Nephele can dynamically allocate and deallocate different types of virtual machine instances from a cloud during job execution based on task requirements. This allows tasks to be executed on the optimal resource type, improving performance and reducing costs compared to static allocation. The document outlines Nephele's architecture, job description using directed acyclic graphs, and strategies for dynamic scheduling and resource management.
This document discusses dynamic resource management in the cloud using an algorithm called Nephele. Nephele is a data processing framework that can explicitly exploit dynamic resource allocation in IaaS clouds for task scheduling and execution. Tasks can be assigned to different types of virtual machines that are automatically instantiated and terminated during job execution. The document describes the Nephele architecture, which follows a master-worker pattern. It also discusses challenges around data locality in cloud environments and how Nephele addresses them. Modules of the Nephele system include a job manager, task managers, and a cloud controller for managing resource allocation. The algorithm aims to improve scheduling efficiency and reduce resource overloads.
This document presents a comparative study on parallel data processing for resource allocation in cloud computing. It discusses Nephele, an open source framework for parallel data processing in the cloud. The study analyzes Nephele's performance compared to Hadoop and how its ability to dynamically allocate virtual machine resources based on task requirements can improve efficiency. Experimental results show how Nephele can leverage heterogeneous cloud resources and automatic scaling to reduce processing costs compared to static allocation and Hadoop. The paper concludes Nephele is an efficient framework for parallel data processing in cloud computing.
Nephele efficient parallel data processing in the cloudArshams
Nephele is a data processing framework designed for cloud environments that allows dynamically allocating and deallocating different types of virtual machines from a cloud during job execution. It uses a master-worker architecture with a Job Manager coordinating the execution of tasks on Task Managers running on virtual machine instances. Nephele aims to improve efficiency over other frameworks by exploiting the on-demand nature of cloud resources through strategies like allocating instance types tailored to each job phase and releasing instances as soon as their tasks are complete.
Iaetsd effective fault toerant resource allocation with costIaetsd Iaetsd
1) The document proposes a fault-tolerant resource allocation method for cloud computing that aims to minimize user payment while meeting task deadlines.
2) It formulates a deadline-driven resource allocation problem based on virtual machine isolation technology and proposes an optimal solution with polynomial time complexity.
3) Experimental results show that the proposed work more efficiently schedules and allocates resources, improving utilization of cloud infrastructure resources.
This document discusses distributed data warehouses and online analytical processing (OLAP). It begins by describing different data warehouse architectures like enterprise data warehouses, data marts, and distributed enterprise data warehouses. It then outlines challenges for achieving performance in distributed OLAP systems, including dynamically managing aggregates, using partial aggregates, allocating data and balancing loads. The document proposes techniques like redundancy and patchworking queries across sites to optimize distributed querying.
Data Warehouses store integrated and consistent data in a subject-oriented data repository dedicated
especially to support business intelligence processes. However, keeping these repositories updated usually
involves complex and time-consuming processes, commonly denominated as Extract-Transform-Load tasks.
These data intensive tasks normally execute in a limited time window and their computational requirements
tend to grow in time as more data is dealt with. Therefore, we believe that a grid environment could suit
rather well as support for the backbone of the technical infrastructure with the clear financial advantage of
using already acquired desktop computers normally present in the organization. This article proposes a
different approach to deal with the distribution of ETL processes in a grid environment, taking into account
not only the processing performance of its nodes but also the existing bandwidth to estimate the grid
availability in a near future and therefore optimize workflow distribution.
Dear Students
Ingenious techno Solution offers an expertise guidance on you Final Year IEEE & Non- IEEE Projects on the following domain
JAVA
.NET
EMBEDDED SYSTEMS
ROBOTICS
MECHANICAL
MATLAB etc
For further details contact us:
enquiry@ingenioustech.in
044-42046028 or 8428302179.
Ingenious Techno Solution
#241/85, 4th floor
Rangarajapuram main road,
Kodambakkam (Power House)
http://www.ingenioustech.in/
Research Inventy : International Journal of Engineering and Scienceinventy
This document discusses exploiting dynamic resource allocation in cloud environments for efficient parallel data processing. It presents Nephele, a new data processing framework designed for clouds. Nephele can dynamically allocate and deallocate different types of virtual machine instances from a cloud during job execution based on task requirements. This allows tasks to be executed on the optimal resource type, improving performance and reducing costs compared to static allocation. The document outlines Nephele's architecture, job description using directed acyclic graphs, and strategies for dynamic scheduling and resource management.
This document discusses dynamic resource management in the cloud using an algorithm called Nephele. Nephele is a data processing framework that can explicitly exploit dynamic resource allocation in IaaS clouds for task scheduling and execution. Tasks can be assigned to different types of virtual machines that are automatically instantiated and terminated during job execution. The document describes the Nephele architecture, which follows a master-worker pattern. It also discusses challenges around data locality in cloud environments and how Nephele addresses them. Modules of the Nephele system include a job manager, task managers, and a cloud controller for managing resource allocation. The algorithm aims to improve scheduling efficiency and reduce resource overloads.
This document presents a comparative study on parallel data processing for resource allocation in cloud computing. It discusses Nephele, an open source framework for parallel data processing in the cloud. The study analyzes Nephele's performance compared to Hadoop and how its ability to dynamically allocate virtual machine resources based on task requirements can improve efficiency. Experimental results show how Nephele can leverage heterogeneous cloud resources and automatic scaling to reduce processing costs compared to static allocation and Hadoop. The paper concludes Nephele is an efficient framework for parallel data processing in cloud computing.
Nephele efficient parallel data processing in the cloudArshams
Nephele is a data processing framework designed for cloud environments that allows dynamically allocating and deallocating different types of virtual machines from a cloud during job execution. It uses a master-worker architecture with a Job Manager coordinating the execution of tasks on Task Managers running on virtual machine instances. Nephele aims to improve efficiency over other frameworks by exploiting the on-demand nature of cloud resources through strategies like allocating instance types tailored to each job phase and releasing instances as soon as their tasks are complete.
Iaetsd effective fault toerant resource allocation with costIaetsd Iaetsd
1) The document proposes a fault-tolerant resource allocation method for cloud computing that aims to minimize user payment while meeting task deadlines.
2) It formulates a deadline-driven resource allocation problem based on virtual machine isolation technology and proposes an optimal solution with polynomial time complexity.
3) Experimental results show that the proposed work more efficiently schedules and allocates resources, improving utilization of cloud infrastructure resources.
This document discusses distributed data warehouses and online analytical processing (OLAP). It begins by describing different data warehouse architectures like enterprise data warehouses, data marts, and distributed enterprise data warehouses. It then outlines challenges for achieving performance in distributed OLAP systems, including dynamically managing aggregates, using partial aggregates, allocating data and balancing loads. The document proposes techniques like redundancy and patchworking queries across sites to optimize distributed querying.
Data Warehouses store integrated and consistent data in a subject-oriented data repository dedicated
especially to support business intelligence processes. However, keeping these repositories updated usually
involves complex and time-consuming processes, commonly denominated as Extract-Transform-Load tasks.
These data intensive tasks normally execute in a limited time window and their computational requirements
tend to grow in time as more data is dealt with. Therefore, we believe that a grid environment could suit
rather well as support for the backbone of the technical infrastructure with the clear financial advantage of
using already acquired desktop computers normally present in the organization. This article proposes a
different approach to deal with the distribution of ETL processes in a grid environment, taking into account
not only the processing performance of its nodes but also the existing bandwidth to estimate the grid
availability in a near future and therefore optimize workflow distribution.
Cisco and Greenplum Partner to Deliver High-Performance Hadoop Reference ...EMC
The document describes a partnership between Cisco and Greenplum to deliver optimized high-performance Hadoop reference configurations. Key elements include:
- Greenplum MR provides a high-performance distribution of Hadoop with features like direct data access, high availability, and advanced management.
- Cisco UCS is the exclusive hardware platform and provides a flexible, scalable computing platform optimized for Hadoop workloads.
- The Cisco Greenplum MR Reference Configuration combines these software and hardware components into an integrated solution for running Hadoop and big data analytics workloads.
LARGE-SCALE DATA PROCESSING USING MAPREDUCE IN CLOUD COMPUTING ENVIRONMENTijwscjournal
The computer industry is being challenged to develop methods and techniques for affordable data processing on large datasets at optimum response times. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. One of the recent processing models with a more efficient and intuitive solution to rapidly process large amount of data in parallel is called MapReduce. It is a framework defining a template approach of programming to perform large-scale data computation on clusters of machines in a cloud computing environment. MapReduce provides automatic parallelization and distribution of computation based on several processors. It hides the complexity of writing parallel and distributed programming code. This paper provides a comprehensive systematic review and analysis of large-scale dataset processing and dataset handling challenges and
requirements in a cloud computing environment by using the MapReduce framework and its open-source implementation Hadoop. We defined requirements for MapReduce systems to perform large-scale data processing. We also proposed the MapReduce framework and one implementation of this framework on Amazon Web Services. At the end of the paper, we presented an experimentation of running MapReduce
system in a cloud environment. This paper outlines one of the best techniques to process large datasets is MapReduce; it also can help developers to do parallel and distributed computation in a cloud environment.
Scheduling in Virtual Infrastructure for High-Throughput Computing IJCSEA Journal
This document summarizes a study on improving the efficiency of resource utilization in virtual infrastructure for high-throughput computing. The study proposes a pre-staging model where virtual machine images are pre-loaded on execution nodes and jobs are directly submitted to the virtual machines. Experimental results show that the pre-staging model improves job execution times by 10-15 times compared to using Condor's virtual universe, with greater improvements for non-HPC jobs. The overhead of virtualization also reduces performance gains for HPC jobs like MPI applications.
IRJET-Framework for Dynamic Resource Allocation and Efficient Scheduling Stra...IRJET Journal
This document discusses a framework for dynamic resource allocation and efficient scheduling strategies in cloud computing platforms for high-performance computing (HPC). It proposes using a parallel genetic algorithm to find optimal allocation of virtual machines to physical resources in order to maximize resource utilization. The algorithm represents the resource allocation problem as an unbalanced job scheduling problem. It uses genetic operators like mutation and crossover to efficiently allocate requests for resources to idle nodes. Compared to a traditional genetic algorithm, the parallel genetic algorithm improves the speed of finding the best allocation and increases resource utilization. Future work could explore implementing dynamic load balancing and using big data concepts on the cloud.
ABOUT THE SUITABILITY OF CLOUDS IN HIGH-PERFORMANCE COMPUTINGcsandit
Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT
infrastructure, but can outsource it to a Cloud Service Provider of their choice. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or for Big Data analysis, things are getting more intricate, because HPC codes must stay highly
efficient, even when executed by many virtual cores (vCPUs). Older clouds or new standard clouds can fulfil this only under special precautions, which are given in this article. The results
can be extrapolated to other cloud OSes than OpenStack and to other codes than OpenFOAM,which were used as examples
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Hybrid Based Resource Provisioning in CloudEditor IJCATR
The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
The document discusses elastic data warehousing in the cloud. It begins with an introduction to data warehousing and cloud computing. Cloud computing offers benefits like reduced costs, expertise, and elasticity. However, challenges include data import/export performance, low-end cloud nodes, latency, and loss of control. The goal is an elastic data warehousing system that can automatically scale resources based on usage, saving money. It will provide overviews of traditional data warehousing and current cloud offerings to analyze the potential for elastic data warehousing in the cloud.
1) Accelerated computing is a framework that uses specialized processors or accelerators alongside general-purpose CPUs to improve application performance, energy efficiency, and cost.
2) AMD recognizes accelerated computing as a paradigm shift and is committed to developing hardware accelerators, software tools, and technology to advance this framework.
3) Historically, improving CPU performance focused on increasing frequency and optimizing architecture, but limitations in these approaches drove the industry toward multi-core CPUs and heterogeneous computing using accelerators.
An Energy Efficient Data Transmission and Aggregation of WSN using Data Proce...IRJET Journal
The document proposes a system for efficient data transmission and aggregation in wireless sensor networks (WSNs) using MapReduce processing. Sensors are grouped into three clusters, with a cluster head elected in each based on distance, memory, and battery to reduce energy consumption. Sensor data is encrypted and sent to cluster heads, which aggregate the data and append a signature before sending to the base station. The signature is verified and data is stored in Hadoop and processed using MapReduce. The system aims to provide data integrity and privacy during concealed data aggregation to reduce overhead in heterogeneous WSNs.
This white paper discusses the need for differentiated architectures in today's data centers. It outlines Juniper's vision of evolving data centers to a simplified, cloud-ready state. This involves consolidating resources, simplifying networks through a 3-2-1 architecture, and making networks more scalable and efficient for modern applications through techniques like Virtual Chassis technology and a unified fabric. The paper contrasts needs for cost-effective IT data centers versus high-performance production data centers.
Access security on cloud computing implemented in hadoop systemJoão Gabriel Lima
This document summarizes a research paper on implementing access security on a Hadoop cloud computing system using fingerprint identification and face recognition. The researchers built a Hadoop cloud computing system connected to mobile and thin client devices over wired and wireless networks. They used fingerprint and face recognition for rapid user identification and verification on the cloud system within 2.2 seconds. The goal was to evaluate the effectiveness and efficiency of access security on the Hadoop cloud computing platform.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
This document discusses scheduling in cloud computing. It proposes a priority-based scheduling protocol to improve resource utilization, server performance, and minimize makespan. The protocol assigns priorities to jobs, allocates jobs to processors based on completion time, and processes jobs in parallel queues to efficiently schedule jobs in cloud computing. Future work includes analyzing time complexity and completion times through simulation to validate the protocol's efficiency.
A REVIEW ON LOAD BALANCING IN CLOUD USING ENHANCED GENETIC ALGORITHM IAEME Publication
“Cloud computing” is a term, which involves virtualization, distributed
computing, networking, software and net services. A cloudconsists of several partssuch as shoppers, datacenter and distributed servers. It includes fault tolerance, high
availability, scalability, flexibility, reduced overhead for users, reduced cost of
possession, on demand services etc. Central to these issues lies the institution of a
good load reconciliation algorithmic rule. The load can be CPU load, memory
capacity, delay or network load. Load balancing is the method of distributing the load
among varied nodes of a distributed system to boost each resource utilization and job
interval whereas additionally avoiding a state of affairs wherever a number of the
nodes area unit heavily loaded whereas different nodes area unit idle or doing little
work. Load balancing ensures that all the processors within the system or each node
within the network will require the equal quantity of labor at any instant of your time.
This technique will be sender initiated, receiver initiated or symmetric sort
(combination of sender initiated and receiver initiated types). Our objective is to
develop an effective load reconciliation algorithmic rule mistreatment divisible load
programming theorem to maximize or minimize completely different performance
parameters (throughput, latency for example) for the clouds of different sizes (virtualtopology de-pending on the appliance requirement).
BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONSijccsa
Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The
orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits
resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the
scientists unprecedented flexibility for research and development. With the proper incentive model,
resource efficiency will be automatically maximized. In this context, there are three new challenges. The
first is the virtualization overheads. The second is the administrative complexity for scientists to manage
the virtual clusters. The third is the programming model. The existing HPC programming models were
designed for dedicated homogeneous parallel processors. The HPC cloud is typically heterogeneous and
shared. This paper reports on the practice and experiences in building a private HPC cloud using a subset
of a traditional HPC cluster. We report our evaluation criteria using Open Source software, and
performance studies for compute-intensive and data-intensive applications. We also report the design and
implementation of a Puppet-based virtual cluster administration tool called HPCFY. In addition, we show
that even if the overhead of virtualization is present, efficient scalability for virtual clusters can be achieved
by understanding the effects of virtualization overheads on various types of HPC and Big Data workloads.
We aim at providing a detailed experience report to the HPC community, to ease the process of building a
private HPC cloud using Open Source software.
This document describes Dremel, an interactive query system for analyzing large nested datasets. Dremel uses a multi-level execution tree to parallelize queries across thousands of CPUs. It stores nested data in a novel columnar format that improves performance by only reading relevant columns from storage. Dremel has been in production at Google since 2006 and is used by thousands of users to interactively analyze datasets containing trillions of records.
Cloud computing is a realized wonder. It delights its users by providing applications, platforms and infrastructure without any initial investment. The “pay as you use” strategy comforts the users. The usage can be increased by adding infrastructure, tools or applications to the existing application. The realistic beauty of cloud computing is that there is no need for any sophisticated tool for access, web browser or even smartphone will do. Cloud computing is a windfall for small organizations having less sensitive information. But for large organizations, the risks related to security may be daunting. Necessary steps have to be taken for managing the issues like confidentiality, integrity, privacy, availability and so on. In this paper availability is taken and studied in a multi-dimensional perspective. Availability is taken a key issue and the mechanisms that enable enhancement are analyzed.
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
The document discusses the design and implementation of a virtual client honeypot to collect internet malware. A client honeypot is an active security system that simulates client-side software to detect attacks against clients. The proposed virtual client honeypot collects URLs from a database, launches them in a virtual machine, and monitors for malware downloads and changes to the file system and network activity. The honeypot was able to successfully collect malware samples and network packet captures from malicious websites exploiting client-side vulnerabilities.
This document discusses the estimation of very fast transient overvoltages (VFTOs) in a 3-phase 132kV gas insulated substation (GIS). It presents the modeling of a GIS system in MATLAB to analyze VFTOs generated during switching operations and 3-phase faults. Each re-strike of the disconnector switch during opening/closing generates high frequency transient overvoltages that can damage equipment. The paper develops an accurate electrical model of the GIS components using lumped parameters to simulate transient behavior and calculate overvoltage waveforms.
The document describes a proposed model for representing user profiles using ontologies for personalized web information gathering. The model uses both a world knowledge base (encoded from the Library of Congress Subject Headings) and a user's local instance repository to construct personalized ontologies representing the user's concept models and background knowledge. The proposed model is compared against existing benchmark models through experiments using a large standard dataset, and results show the proposed model improves web information gathering performance.
Cisco and Greenplum Partner to Deliver High-Performance Hadoop Reference ...EMC
The document describes a partnership between Cisco and Greenplum to deliver optimized high-performance Hadoop reference configurations. Key elements include:
- Greenplum MR provides a high-performance distribution of Hadoop with features like direct data access, high availability, and advanced management.
- Cisco UCS is the exclusive hardware platform and provides a flexible, scalable computing platform optimized for Hadoop workloads.
- The Cisco Greenplum MR Reference Configuration combines these software and hardware components into an integrated solution for running Hadoop and big data analytics workloads.
LARGE-SCALE DATA PROCESSING USING MAPREDUCE IN CLOUD COMPUTING ENVIRONMENTijwscjournal
The computer industry is being challenged to develop methods and techniques for affordable data processing on large datasets at optimum response times. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. One of the recent processing models with a more efficient and intuitive solution to rapidly process large amount of data in parallel is called MapReduce. It is a framework defining a template approach of programming to perform large-scale data computation on clusters of machines in a cloud computing environment. MapReduce provides automatic parallelization and distribution of computation based on several processors. It hides the complexity of writing parallel and distributed programming code. This paper provides a comprehensive systematic review and analysis of large-scale dataset processing and dataset handling challenges and
requirements in a cloud computing environment by using the MapReduce framework and its open-source implementation Hadoop. We defined requirements for MapReduce systems to perform large-scale data processing. We also proposed the MapReduce framework and one implementation of this framework on Amazon Web Services. At the end of the paper, we presented an experimentation of running MapReduce
system in a cloud environment. This paper outlines one of the best techniques to process large datasets is MapReduce; it also can help developers to do parallel and distributed computation in a cloud environment.
Scheduling in Virtual Infrastructure for High-Throughput Computing IJCSEA Journal
This document summarizes a study on improving the efficiency of resource utilization in virtual infrastructure for high-throughput computing. The study proposes a pre-staging model where virtual machine images are pre-loaded on execution nodes and jobs are directly submitted to the virtual machines. Experimental results show that the pre-staging model improves job execution times by 10-15 times compared to using Condor's virtual universe, with greater improvements for non-HPC jobs. The overhead of virtualization also reduces performance gains for HPC jobs like MPI applications.
IRJET-Framework for Dynamic Resource Allocation and Efficient Scheduling Stra...IRJET Journal
This document discusses a framework for dynamic resource allocation and efficient scheduling strategies in cloud computing platforms for high-performance computing (HPC). It proposes using a parallel genetic algorithm to find optimal allocation of virtual machines to physical resources in order to maximize resource utilization. The algorithm represents the resource allocation problem as an unbalanced job scheduling problem. It uses genetic operators like mutation and crossover to efficiently allocate requests for resources to idle nodes. Compared to a traditional genetic algorithm, the parallel genetic algorithm improves the speed of finding the best allocation and increases resource utilization. Future work could explore implementing dynamic load balancing and using big data concepts on the cloud.
ABOUT THE SUITABILITY OF CLOUDS IN HIGH-PERFORMANCE COMPUTINGcsandit
Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT
infrastructure, but can outsource it to a Cloud Service Provider of their choice. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or for Big Data analysis, things are getting more intricate, because HPC codes must stay highly
efficient, even when executed by many virtual cores (vCPUs). Older clouds or new standard clouds can fulfil this only under special precautions, which are given in this article. The results
can be extrapolated to other cloud OSes than OpenStack and to other codes than OpenFOAM,which were used as examples
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Hybrid Based Resource Provisioning in CloudEditor IJCATR
The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
The document discusses elastic data warehousing in the cloud. It begins with an introduction to data warehousing and cloud computing. Cloud computing offers benefits like reduced costs, expertise, and elasticity. However, challenges include data import/export performance, low-end cloud nodes, latency, and loss of control. The goal is an elastic data warehousing system that can automatically scale resources based on usage, saving money. It will provide overviews of traditional data warehousing and current cloud offerings to analyze the potential for elastic data warehousing in the cloud.
1) Accelerated computing is a framework that uses specialized processors or accelerators alongside general-purpose CPUs to improve application performance, energy efficiency, and cost.
2) AMD recognizes accelerated computing as a paradigm shift and is committed to developing hardware accelerators, software tools, and technology to advance this framework.
3) Historically, improving CPU performance focused on increasing frequency and optimizing architecture, but limitations in these approaches drove the industry toward multi-core CPUs and heterogeneous computing using accelerators.
An Energy Efficient Data Transmission and Aggregation of WSN using Data Proce...IRJET Journal
The document proposes a system for efficient data transmission and aggregation in wireless sensor networks (WSNs) using MapReduce processing. Sensors are grouped into three clusters, with a cluster head elected in each based on distance, memory, and battery to reduce energy consumption. Sensor data is encrypted and sent to cluster heads, which aggregate the data and append a signature before sending to the base station. The signature is verified and data is stored in Hadoop and processed using MapReduce. The system aims to provide data integrity and privacy during concealed data aggregation to reduce overhead in heterogeneous WSNs.
This white paper discusses the need for differentiated architectures in today's data centers. It outlines Juniper's vision of evolving data centers to a simplified, cloud-ready state. This involves consolidating resources, simplifying networks through a 3-2-1 architecture, and making networks more scalable and efficient for modern applications through techniques like Virtual Chassis technology and a unified fabric. The paper contrasts needs for cost-effective IT data centers versus high-performance production data centers.
Access security on cloud computing implemented in hadoop systemJoão Gabriel Lima
This document summarizes a research paper on implementing access security on a Hadoop cloud computing system using fingerprint identification and face recognition. The researchers built a Hadoop cloud computing system connected to mobile and thin client devices over wired and wireless networks. They used fingerprint and face recognition for rapid user identification and verification on the cloud system within 2.2 seconds. The goal was to evaluate the effectiveness and efficiency of access security on the Hadoop cloud computing platform.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
This document discusses scheduling in cloud computing. It proposes a priority-based scheduling protocol to improve resource utilization, server performance, and minimize makespan. The protocol assigns priorities to jobs, allocates jobs to processors based on completion time, and processes jobs in parallel queues to efficiently schedule jobs in cloud computing. Future work includes analyzing time complexity and completion times through simulation to validate the protocol's efficiency.
A REVIEW ON LOAD BALANCING IN CLOUD USING ENHANCED GENETIC ALGORITHM IAEME Publication
“Cloud computing” is a term, which involves virtualization, distributed
computing, networking, software and net services. A cloudconsists of several partssuch as shoppers, datacenter and distributed servers. It includes fault tolerance, high
availability, scalability, flexibility, reduced overhead for users, reduced cost of
possession, on demand services etc. Central to these issues lies the institution of a
good load reconciliation algorithmic rule. The load can be CPU load, memory
capacity, delay or network load. Load balancing is the method of distributing the load
among varied nodes of a distributed system to boost each resource utilization and job
interval whereas additionally avoiding a state of affairs wherever a number of the
nodes area unit heavily loaded whereas different nodes area unit idle or doing little
work. Load balancing ensures that all the processors within the system or each node
within the network will require the equal quantity of labor at any instant of your time.
This technique will be sender initiated, receiver initiated or symmetric sort
(combination of sender initiated and receiver initiated types). Our objective is to
develop an effective load reconciliation algorithmic rule mistreatment divisible load
programming theorem to maximize or minimize completely different performance
parameters (throughput, latency for example) for the clouds of different sizes (virtualtopology de-pending on the appliance requirement).
BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONSijccsa
Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The
orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits
resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the
scientists unprecedented flexibility for research and development. With the proper incentive model,
resource efficiency will be automatically maximized. In this context, there are three new challenges. The
first is the virtualization overheads. The second is the administrative complexity for scientists to manage
the virtual clusters. The third is the programming model. The existing HPC programming models were
designed for dedicated homogeneous parallel processors. The HPC cloud is typically heterogeneous and
shared. This paper reports on the practice and experiences in building a private HPC cloud using a subset
of a traditional HPC cluster. We report our evaluation criteria using Open Source software, and
performance studies for compute-intensive and data-intensive applications. We also report the design and
implementation of a Puppet-based virtual cluster administration tool called HPCFY. In addition, we show
that even if the overhead of virtualization is present, efficient scalability for virtual clusters can be achieved
by understanding the effects of virtualization overheads on various types of HPC and Big Data workloads.
We aim at providing a detailed experience report to the HPC community, to ease the process of building a
private HPC cloud using Open Source software.
This document describes Dremel, an interactive query system for analyzing large nested datasets. Dremel uses a multi-level execution tree to parallelize queries across thousands of CPUs. It stores nested data in a novel columnar format that improves performance by only reading relevant columns from storage. Dremel has been in production at Google since 2006 and is used by thousands of users to interactively analyze datasets containing trillions of records.
Cloud computing is a realized wonder. It delights its users by providing applications, platforms and infrastructure without any initial investment. The “pay as you use” strategy comforts the users. The usage can be increased by adding infrastructure, tools or applications to the existing application. The realistic beauty of cloud computing is that there is no need for any sophisticated tool for access, web browser or even smartphone will do. Cloud computing is a windfall for small organizations having less sensitive information. But for large organizations, the risks related to security may be daunting. Necessary steps have to be taken for managing the issues like confidentiality, integrity, privacy, availability and so on. In this paper availability is taken and studied in a multi-dimensional perspective. Availability is taken a key issue and the mechanisms that enable enhancement are analyzed.
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
The document discusses the design and implementation of a virtual client honeypot to collect internet malware. A client honeypot is an active security system that simulates client-side software to detect attacks against clients. The proposed virtual client honeypot collects URLs from a database, launches them in a virtual machine, and monitors for malware downloads and changes to the file system and network activity. The honeypot was able to successfully collect malware samples and network packet captures from malicious websites exploiting client-side vulnerabilities.
This document discusses the estimation of very fast transient overvoltages (VFTOs) in a 3-phase 132kV gas insulated substation (GIS). It presents the modeling of a GIS system in MATLAB to analyze VFTOs generated during switching operations and 3-phase faults. Each re-strike of the disconnector switch during opening/closing generates high frequency transient overvoltages that can damage equipment. The paper develops an accurate electrical model of the GIS components using lumped parameters to simulate transient behavior and calculate overvoltage waveforms.
The document describes a proposed model for representing user profiles using ontologies for personalized web information gathering. The model uses both a world knowledge base (encoded from the Library of Congress Subject Headings) and a user's local instance repository to construct personalized ontologies representing the user's concept models and background knowledge. The proposed model is compared against existing benchmark models through experiments using a large standard dataset, and results show the proposed model improves web information gathering performance.
This document describes a study that developed an integrated biomedical ontology for extracting information from Medline abstracts about Alzheimer's disease. The ontology integrated the Gene Ontology and Medical Subject Headings by mapping gene names, GO terms, and MeSH keywords related to Alzheimer's. The integrated ontology was validated structurally, syntactically, and semantically. It was then used to discover significant associations between proteins, genes, and Alzheimer's disease extracted from Medline abstracts.
The document discusses how bioinformatics can be used to identify new cancer drug targets. It describes analyzing gene sequences to find homologs of known cancer genes. Microarray data can be mined to find genes that are differentially expressed in cancer versus normal tissues. Digital expression data from EST and SAGE tags provides another method to analyze gene expression levels in cancers. Integrating these diverse genomic and expression datasets through bioinformatics allows detection of cancer-causing mutations, gene amplifications and differentially expressed genes to identify potential new drug targets.
3.[18 22]hybrid association rule mining using ac treeAlexander Decker
This document proposes a new hybrid algorithm called AC Tree (AprioriCOFI tree) for efficiently mining association rules from large datasets at multiple concept levels. The AC Tree algorithm combines aspects of the Apriori, FP-Tree, and COFI Tree algorithms. It first uses Apriori to identify frequent 1-itemsets, then constructs an FP Tree header table and builds smaller trees for each frequent item to mine patterns at different levels. Experimental results on a 20 Newsgroups dataset show that AC Tree outperforms Apriori, FP-Tree, and APFT algorithms by discovering more interesting patterns faster.
Java fue creado en la década de 1990 por Sun Microsystems para mejorar los lenguajes de programación existentes y hacerlos más simples, modernos y potentes. Fue diseñado para ser multiplataforma y seguro, y se adaptó rápidamente a Internet. Presentado públicamente en 1995, Java ofrece ventajas como ser orientado a objetos, potente y seguro, además de no poder ejecutar virus.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
This document summarizes a research paper about efficient parallel data processing in cloud environments. It discusses Nephele, a data processing framework that can exploit the dynamic resource provisioning of Infrastructure-as-a-Service clouds. Nephele's architecture allows tasks to be assigned to different types of virtual machines that are automatically instantiated and terminated during job execution. Evaluations show Nephele can improve resource utilization and reduce costs compared to Hadoop by assigning specific VM types to tasks and allocating/deallocating VMs during job execution. The paper presents opportunities for future work to further optimize Nephele's ability to adapt resource usage based on overload or underutilization.
This document summarizes the Nephele framework for efficient parallel data processing using Hadoop. Nephele is designed for dynamic and heterogeneous cluster environments. It allows tasks of a processing job to be assigned to different types of virtual machines instantiated and terminated automatically during job execution. This improves resource utilization and reduces processing time and costs compared to other frameworks like Hadoop that are not suitable for dynamic resource allocation in cloud environments. The document discusses related frameworks like SCOPE, SWIFT, Falkon and Hadoop, and explains how Nephele addresses their limitations for parallel processing in clouds.
This document summarizes the Nephele framework for efficient parallel data processing using Hadoop. Nephele is designed for dynamic and heterogeneous cloud environments. It allows tasks of a job to be assigned to different types of virtual machines that are automatically instantiated and terminated during job execution. The document compares Nephele to other frameworks like Hadoop, SCOPE, SWIFT, and Falkon. Evaluation results show Nephele improves resource utilization and reduces processing time and cost compared to Hadoop by dynamically allocating resources based on workload. Future work aims to further improve Nephele's ability to adapt to resource overload or underutilization during job execution.
This document summarizes the Nephele framework, which is a parallel data processing framework designed for dynamic and heterogeneous cloud environments. The Nephele framework offers efficient parallel data processing in clouds by allowing particular tasks of a processing job to be assigned to different types of virtual machines, which are automatically instantiated and terminated during job execution. This dynamic resource allocation is more efficient than other frameworks as it can monitor for resource overload or underutilization and adjust resource allocation accordingly to optimize processing time and costs.
IRJET- Cost Effective Workflow Scheduling in BigdataIRJET Journal
This document summarizes research on cost-effective workflow scheduling in big data environments. It discusses a Pointer Gossip Content Addressable Network Montage Framework that allows automatic selection of target clouds, uniform access to clouds, and workflow data management while meeting service level agreements at lowest cost. The framework is evaluated using a real scientific workflow application in different deployment scenarios. Results show it can execute workflows with expected performance and quality at lowest cost.
This document discusses performance analysis of cloud computing services. It begins by defining cloud computing and describing its key characteristics like on-demand access to computing resources and pay-per-use models. It then reviews several studies on using virtualization technologies and frameworks for evaluating cloud performance and workload generation. The document concludes that tools are needed for comprehensive performance analysis of large scientific clouds to evaluate metrics like response time, cost and scalability across different cloud vendors.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal1
Currently cloud computing has turned into a promising technology and has become a great key for
satisfying a flexible service oriented , online provision and storage of computing resources and user’s
information in lesser expense with dynamism framework on pay per use basis.In this technology Job
Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources,
administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic
Scheduling Approach to schedule resources, by taking account of computation time and makespan with two
detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We
believe that proposed hyper-heuristic achieve better results than other individual heuristics
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal
The document proposes a hyper-heuristic method for scheduling jobs in a cloud environment. It combines two low-level heuristics - Ant Colony Optimization and Particle Swarm Optimization - and uses two operators, intensification and diversity revealing, to select the heuristics. It also uses a conditional revealing operator to identify job failures while allocating resources. The hyper-heuristic aims to achieve better results than individual heuristics in terms of lower makespan time.
Energy-Efficient Task Scheduling in Cloud EnvironmentIRJET Journal
1. The document discusses developing an energy-efficient task scheduling approach for cloud data centers using deep reinforcement learning.
2. It aims to minimize computational costs and cooling costs by optimizing task assignment to servers based on factors like temperature, CPU, and memory.
3. The proposed approach uses a greedy algorithm to schedule tasks to servers maintaining the lowest temperature, thus reducing energy consumption and improving data center performance.
Cloud Computing: A Perspective on Next Basic Utility in IT World IRJET Journal
This document discusses cloud computing and its architecture. It begins with an introduction to cloud computing, defining it as a model that provides infrastructure, platforms, and software as services. The key characteristics and service models of cloud computing are described.
The document then discusses the architecture of cloud computing, including the layers of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It also describes the deployment models of private cloud, public cloud, community cloud, and hybrid cloud.
The document outlines several challenges of cloud computing, such as resource allocation and scheduling, cost optimization, processing time and speed, memory management, load balancing, security issues, fault
Task Scheduling methodology in cloud computing Qutub-ud- Din
This document outlines a proposed methodology for developing efficient task scheduling strategies in cloud computing. It begins with introductions to cloud computing and task scheduling. It then reviews several relevant existing task scheduling algorithms from literature that focus on objectives like reducing costs, minimizing completion time, and maximizing resource utilization. The problem statement indicates the goals are to reduce costs, minimize completion time, and maximize resource allocation. An overview of the proposed methodology's flow is then provided, followed by references.
This document proposes improving infrastructure as a service (IaaS) cloud computing by overlapping on-demand resource allocation with opportunistic provisioning of cycles from idle cloud nodes. It extends the Nimbus cloud computing toolkit to deploy backfill virtual machines on idle nodes for high throughput computing workloads. Evaluation shows backfill VMs can increase cloud infrastructure utilization from 38.5% to 100% with only 6.39% overhead for processing workloads, benefiting both cloud providers and users.
This document proposes a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of idle cloud nodes. It uses Hadoop configuration with MapReduce algorithms to split large files into smaller parts and distribute the work across nodes to improve CPU and storage utilization. Encryption is also used to securely transmit data and address security challenges. The system aims to make better use of idle resources, process large datasets faster, and enhance security in cloud computing environments.
A Novel Approach for Workload Optimization and Improving Security in Cloud Co...IOSR Journals
This document proposes a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of idle cloud nodes to improve utilization. It discusses using Hadoop configuration with a Map-Reduce file splitting algorithm to distribute large files across nodes for processing. Encryption is also used to secure data during transmission and storage using an RSA algorithm. The goal is to improve CPU and storage utilization, handle large data faster by using idle nodes, and maintain security of data and services in the cloud.
IRJET- Performing Load Balancing between Namenodes in HDFSIRJET Journal
This document proposes a new architecture for HDFS to address the single point of failure issue of the NameNode. The current HDFS architecture uses a single NameNode that manages file system metadata. If it fails, the entire system fails. The proposed architecture uses multiple interconnected NameNodes that maintain mirrors of each other's metadata using the Chord system. This allows load balancing between NameNodes and prevents failure if one NameNode goes down, as other NameNodes can handle the load and client requests/responses. The goal is to improve scalability, availability and reduce downtime of the NameNode in HDFS through this new distributed architecture.
Efficient and reliable hybrid cloud architecture for big databaseijccsa
The objective of our paper is to propose a Cloud computing framework which is feasible and necessary for
handling huge data. In our prototype system we considered national ID database structure of Bangladesh
which is prepared by election commission of Bangladesh. Using this database we propose an interactive
graphical user interface for Bangladeshi People Search (BDPS) that use a hybrid structure of cloud
computing handled by apache Hadoop where database is implemented by HiveQL. The infrastructure
divides into two parts: locally hosted cloud which is based on “Eucalyptus” and the remote cloud which is
implemented on well-known Amazon Web Service (AWS). Some common problems of Bangladesh aspect
which includes data traffic congestion, server time out and server down issue is also discussed.
IRJET- Comparatively Analysis on K-Means++ and Mini Batch K-Means Clustering ...IRJET Journal
This document provides an overview and comparative analysis of the K-Means++ and Mini Batch K-Means clustering algorithms in cloud computing using MapReduce. It first introduces cloud computing and its advantages for processing big data using Hadoop MapReduce. It then discusses the K-Means++ algorithm as an improved version of the standard K-Means algorithm that initializes cluster centroids more intelligently. Finally, it compares the performance of K-Means++ and Mini Batch K-Means when implemented using MapReduce for large-scale clustering in cloud environments.
A STUDY ON JOB SCHEDULING IN CLOUD ENVIRONMENTpharmaindexing
This document discusses job scheduling algorithms in cloud computing environments. It begins with an introduction to cloud computing and job scheduling challenges. It then reviews several existing job scheduling algorithms that aim to minimize completion time and costs while improving performance and quality of service. These algorithms use approaches like genetic algorithms, priority queues, and workload prediction. The document also discusses issues like priority-based scheduling and balancing mixed workloads. Overall, the document analyzes the problem of job scheduling in clouds and surveys different proposed scheduling algorithms and their objectives.
IRJET - Evaluating and Comparing the Two Variation with Current Scheduling Al...IRJET Journal
This document presents two variations of a job-driven scheduling scheme called JOSS for efficiently executing MapReduce jobs on remote outsourced data across multiple data centers. The goal of JOSS is to improve data locality for map and reduce tasks, avoid job starvation, and improve job performance. Extensive experiments show that the two JOSS variations, called JOSS-T and JOSS-J, outperform other scheduling algorithms in terms of data locality and network overhead without significant overhead. JOSS-T performs best for workloads of small jobs, while JOSS-J provides the shortest workload time for jobs of varying sizes distributed across data centers.
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.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
This document describes a vehicle theft detection system that uses radio frequency identification (RFID) technology. The system involves embedding an RFID chip in each vehicle that continuously transmits a unique identification signal. When a vehicle is stolen, the owner reports it to the police, who upload the vehicle's information to a central database. Police vehicles are equipped with RFID receivers. If a stolen vehicle passes within range of a receiver, the receiver detects the vehicle's ID signal and displays its details on a tablet. This allows police to quickly identify and recover stolen vehicles. The system aims to make it difficult for thieves to hide a vehicle's identity and allows vehicles to be tracked globally wherever the detection system is implemented.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document compares the k-means and grid density clustering algorithms. It summarizes that grid density clustering determines dense grids based on the densities of neighboring grids, and is able to handle different shaped clusters in multi-density environments. The grid density algorithm does not require distance computation and is not dependent on the number of clusters being known in advance like k-means. The document concludes that grid density clustering is better than k-means clustering as it can handle noise and outliers, find arbitrary shaped clusters, and has lower time complexity.
This document proposes a method for detecting, localizing, and extracting text from videos with complex backgrounds. It involves three main steps:
1. Text detection uses corner metric and Laplacian filtering techniques independently to detect text regions. Corner metric identifies regions with high curvature, while Laplacian filtering highlights intensity discontinuities. The results are combined through multiplication to reduce noise.
2. Text localization then determines the accurate boundaries of detected text strings.
3. Text binarization filters background pixels to extract text pixels for recognition. Thresholding techniques are used to convert localized text regions to binary images.
The method exploits different text properties to detect text using corner metric and Laplacian filtering. Combining the results improves
This document describes the design and implementation of a low power 16-bit arithmetic logic unit (ALU) using clock gating techniques. A variable block length carry skip adder is used in the arithmetic unit to reduce power consumption and improve performance. The ALU uses a clock gating circuit to selectively clock only the active arithmetic or logic unit, reducing dynamic power dissipation from unnecessary clock charging/discharging. The ALU was simulated in VHDL and synthesized for a Xilinx Spartan 3E FPGA, achieving a maximum frequency of 65.19MHz at 1.98mW power dissipation, demonstrating improved performance over a conventional ALU design.
This document describes using particle swarm optimization (PSO) and genetic algorithms (GA) to tune the parameters of a proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. PSO and GA are used to minimize the objective function by adjusting the PID parameters to achieve optimal step response with minimal overshoot, settling time, and rise time. The results show that PSO provides high-quality solutions within a shorter calculation time than other stochastic methods.
This document discusses implementing trust negotiations in multisession transactions. It proposes a framework that supports voluntary and unexpected interruptions, allowing negotiating parties to complete negotiations despite temporary unavailability of resources. The Trust-x protocol addresses issues related to validity, temporary loss of data, and extended unavailability of one negotiator. It allows a peer to suspend an ongoing negotiation and resume it with another authenticated peer. Negotiation portions and intermediate states can be safely and privately passed among peers to guarantee stability for continued suspended negotiations. An ontology is also proposed to provide formal specification of concepts and relationships, which is essential in complex web service environments for sharing credential information needed to establish trust.
This document discusses and compares various nature-inspired optimization algorithms for resolving the mixed pixel problem in remote sensing imagery, including Biogeography-Based Optimization (BBO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). It provides an overview of each algorithm, explaining key concepts like migration and mutation in BBO. The document aims to prove that BBO is the best algorithm for resolving the mixed pixel problem by comparing it to other evolutionary algorithms. It also includes figures illustrating concepts like the species model and habitat in BBO.
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
This document summarizes research on using wireless sensor networks to detect mobile targets. It discusses two optimization problems: 1) maximizing the exposure of the least exposed path within a sensor budget, and 2) minimizing sensor installation costs while ensuring all paths have exposure above a threshold. It proposes using tabu search heuristics to provide near-optimal solutions. The research also addresses extending the models to consider wireless connectivity, heterogeneous sensors, and intrusion detection using a game theory approach. Experimental results show the proposed mobile replica detection scheme can rapidly detect replicas with no false positives or negatives.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.