Grid computing gained high popularity in the field of scientific computing through the idea of distributed resource sharing among institutions and scientists. Scientific computing is traditionally a high-utilization workload, with production Grids often running at over 80% utilization (generating high and often unpredictable latencies), and with smaller national Grids offering a rather limited amount of high-performance resources. Running large-scale simulations in such overloaded Grid environments often becomes latency bound or suffers from well-known Grid reliability problems. Today, a new research direction coined by the term Cloud computing proposes an alternative attractive to scientific computing scientists primarily because of four main advantages.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Improved Utilization of Infrastructure of Clouds by using Upgraded Functional...AM Publications
This paper discusses a propose cloud infrastructure that combines On-Demand allocation of resources with
improved utilization, opportunistic provisioning of cycles from idle cloud nodes to other processes. Because for cloud
computing to avail all the demanded services to the cloud consumers is very difficult. It is a major issue to meet cloud
consumer’s requirements. Hence On-Demand cloud infrastructure using Hadoop configuration with improved CPU
utilization and storage utilization is proposed using splitting algorithm by using Map-Reduce. Hence all cloud nodes which
remains idle are all in use and also improvement in security challenges and achieves load balancing and fast processing of
large data in less amount of time. Here we compare the FTP and HDFS for file uploading and file downloading; and
enhance the CPU utilization and storage utilization. Cloud computing moves the application software and databases to the
large data centres, where the management of the data and services may not be fully trustworthy. Therefore this security
problem is solve by encrypting the data using encryption/decryption algorithm and Map-Reducing algorithm which solve
the problem of utilization of all idle cloud nodes for larger data.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
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.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Improved Utilization of Infrastructure of Clouds by using Upgraded Functional...AM Publications
This paper discusses a propose cloud infrastructure that combines On-Demand allocation of resources with
improved utilization, opportunistic provisioning of cycles from idle cloud nodes to other processes. Because for cloud
computing to avail all the demanded services to the cloud consumers is very difficult. It is a major issue to meet cloud
consumer’s requirements. Hence On-Demand cloud infrastructure using Hadoop configuration with improved CPU
utilization and storage utilization is proposed using splitting algorithm by using Map-Reduce. Hence all cloud nodes which
remains idle are all in use and also improvement in security challenges and achieves load balancing and fast processing of
large data in less amount of time. Here we compare the FTP and HDFS for file uploading and file downloading; and
enhance the CPU utilization and storage utilization. Cloud computing moves the application software and databases to the
large data centres, where the management of the data and services may not be fully trustworthy. Therefore this security
problem is solve by encrypting the data using encryption/decryption algorithm and Map-Reducing algorithm which solve
the problem of utilization of all idle cloud nodes for larger data.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
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.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Allocation Strategies of Virtual Resources in Cloud-Computing NetworksIJERA Editor
In distributed computing, Cloud computing facilitates pay per model as per user demand and requirement.
Collection of virtual machines including both computational and storage resources will form the Cloud. In
Cloud computing, the main objective is to provide efficient access to remote and geographically distributed
resources. Cloud faces many challenges, one of them is scheduling/allocation problem. Scheduling refers to a
set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its
allocation strategy according to the changing environment and the type of task. In this paper we will see FCFS,
Round Robin scheduling in addition to Linear Integer Programming an approach of resource allocation.
Improving Utilization of Infrastructure CloudIJASCSE
A key advantage of Infrastructure-as-a-Service (IaaS) cloud is providing users on-demand access to resources. However, to provide on-demand access, cloud providers must either significantly overprovision their infrastructure (or pay a high price for operating resources with low utilization) or reject a large proportion of user requests (in which case the access is no longer on-demand). At the same time, not all users require truly on-demand access to resources. Many applications and workflows are designed for recoverable systems where interruptions in service are expected. For instance, many scientists utilize High Throughput Computing (HTC)-enabled resources, such as Condor, where jobs are dispatched to available resources and terminated when the resource is no longer available. We propose a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of cycles from idle cloud nodes to other processes by deploying backfill Virtual Machines (VMs).
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
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.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
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.
Groupware is technology designed to be used by groups of people for sharing information. Groupware applications are becoming more and more popular now.
Groupware is an environment where all users can share their documents. It is a platform where they can perform daily task of communicating, collaborating and coordinating with others. It automates business processes by using workflow management and collaborated computing techniques.
Cloud computing comes into focus only when you think about what IT always needs: a way to increase capacity or add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software. Cloud computing encompasses any subscription-based or pay-per-use service that, in real time over the Internet, extends IT's existing capabilities.
Allocation Strategies of Virtual Resources in Cloud-Computing NetworksIJERA Editor
In distributed computing, Cloud computing facilitates pay per model as per user demand and requirement.
Collection of virtual machines including both computational and storage resources will form the Cloud. In
Cloud computing, the main objective is to provide efficient access to remote and geographically distributed
resources. Cloud faces many challenges, one of them is scheduling/allocation problem. Scheduling refers to a
set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its
allocation strategy according to the changing environment and the type of task. In this paper we will see FCFS,
Round Robin scheduling in addition to Linear Integer Programming an approach of resource allocation.
Improving Utilization of Infrastructure CloudIJASCSE
A key advantage of Infrastructure-as-a-Service (IaaS) cloud is providing users on-demand access to resources. However, to provide on-demand access, cloud providers must either significantly overprovision their infrastructure (or pay a high price for operating resources with low utilization) or reject a large proportion of user requests (in which case the access is no longer on-demand). At the same time, not all users require truly on-demand access to resources. Many applications and workflows are designed for recoverable systems where interruptions in service are expected. For instance, many scientists utilize High Throughput Computing (HTC)-enabled resources, such as Condor, where jobs are dispatched to available resources and terminated when the resource is no longer available. We propose a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of cycles from idle cloud nodes to other processes by deploying backfill Virtual Machines (VMs).
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
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.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
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.
Groupware is technology designed to be used by groups of people for sharing information. Groupware applications are becoming more and more popular now.
Groupware is an environment where all users can share their documents. It is a platform where they can perform daily task of communicating, collaborating and coordinating with others. It automates business processes by using workflow management and collaborated computing techniques.
Cloud computing comes into focus only when you think about what IT always needs: a way to increase capacity or add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software. Cloud computing encompasses any subscription-based or pay-per-use service that, in real time over the Internet, extends IT's existing capabilities.
OLAP Basics and Fundamentals by Bharat Kalia Bharat Kalia
OLAP is a category of software technology that enables analysts, managers, and executives to gain insight into the data through fast, consistent, interactive, access in a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.
Electronic commerce, commonly written as e-commerce or eCommerce, is the trading or facilitation of trading in products or services using computer networks, such as the Internet. Electronic commerce draws on technologies such as mobile commerce, electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection systems. Modern electronic commerce typically uses the World Wide Web for at least one part of the transaction's life cycle, although it may also use other technologies such as e-mail.
E-commerce businesses may employ some or all of the following:
PL/SQL is a combination of SQL along with the procedural features of programming languages.
It provides specific syntax for this purpose and supports exactly the same datatypes as SQL.
A Virtualization Model for Cloud ComputingSouvik Pal
Cloud Computing is now a very emerging field in the IT industry as well as research field. The advancement of Cloud Computing came up due to fast-growing usage of internet among the people. Cloud Computing is basically on-demand network access to a collection of physical resources which can be provisioned according to the need of cloud user under the supervision of Cloud Service provider interaction. From business prospective, the viable achievements of Cloud Computing and recent developments in Grid computing have brought the platform that has introduced virtualization technology into the era of high performance computing. Virtualization technology is widely applied to modern data center for cloud computing. Virtualization is used computer resources to imitate other computer resources or whole computers. This paper provides a Virtualization model for cloud computing that may lead to faster access and better performance. This model may help to combine self-service capabilities and ready-to-use facilities for computing resources.
A Secure Cloud Storage System with Data Forwarding using Proxy Re-encryption ...IJTET Journal
Cloud computing provides the facility to access shared resources and common support which contributes services on demand over the network to perform operations that meet changing business needs. A cloud storage system, consisting of a collection of storage servers, affords long-term storage services over the internet. Storing the data in a third party cloud system cause serious concern over data confidentiality, without considering the local infrastructure limitations, the cloud services allow the user to enjoy the cloud applications. As the different users may be working in the collaborative relationship, the data sharing becomes significant to achieve productive benefit during the data accessing. The existing security system only focuses on the authentication; it shows that user’s private data cannot be accessed by the fake users. To address the above cloud storage privacy issue shared authority based privacy-preserving authentication protocol is used. In the SAPA, the shared access authority is achieved by anonymous access request and privacy consideration, attribute based access control allows the user to access their own data fields. To provide the data sharing among the multiple users proxy re-encryption scheme is applied by the cloud server. The privacy-preserving data access authority sharing is attractive for multi-user collaborative cloud applications.
A Secure Cloud Storage System with Data Forwarding using Proxy Re-encryption ...IJTET Journal
Cloud computing provides the facility to access shared resources and common support which contributes services on
demand over the network to perform operations that meet changing business needs. A cloud storage system, consisting of a collection
of storage servers, affords long-term storage services over the internet. Storing the data in a third party cloud system cause serious
concern over data confidentiality, without considering the local infrastructure limitations, the cloud services allow the user to enjoy the
cloud applications. As the different users may be working in the collaborative relationship, the data sharing becomes significant to
achieve productive benefit during the data accessing. The existing security system only focuses on the authentication; it shows that
user’s private data cannot be accessed by the fake users. To address the above cloud storage privacy issue shared authority based
privacy-preserving authentication protocol is used. In the SAPA, the shared access authority is achieved by anonymous access request
and privacy consideration, attribute based access control allows the user to access their own data fields. To provide the data sharing
among the multiple users proxy re-encryption scheme is applied by the cloud server. The privacy-preserving data access authority
sharing is attractive for multi-user collaborative cloud applications.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
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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
SURVEY ON KEY AGGREGATE CRYPTOSYSTEM FOR SCALABLE DATA SHARINGEditor IJMTER
Public-key cryptosystems produce constant-size cipher texts with efficient delegation
of decryption rights for any set of cipher texts. One can aggregate any set of secret keys and make
them as compact as a single key. The secret key holder can release a constant-size aggregate key for
flexible choices of cipher text set in cloud storage. In KAC, users encrypt a message not only under a
public-key, but also under an identifier of cipher text called class. That means the cipher texts are
further categorized into different classes. The key owner holds a master-secret called master-secret
key, which can be used to extract secret keys for different classes. More importantly, the extracted
key have can be an aggregate key which is as compact as a secret key for a single class, but
aggregates the power of many such keys, i.e., the decryption power for any subset of cipher text
classes. The key aggregate cryptosystem is enhanced with boundary less cipher text classes. The
system is improved with device independent key distribution mechanism. The key distribution
process is enhanced with security features to protect key leakage. The key parameter transmission
process is integrated with the cipher text download process.
Virtual Machine Migration and Allocation in Cloud Computing: A Reviewijtsrd
Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessed through internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre copy and post copy approach. The process to move running applications or VMs from one physical machine to another is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the service is unavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. Khushbu Singh Chandel | Dr. Avinash Sharma "Virtual Machine Migration and Allocation in Cloud Computing: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29556.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29556/virtual-machine-migration-and-allocation-in-cloud-computing-a-review/khushbu-singh-chandel
Multi-objective load balancing in cloud infrastructure through fuzzy based de...IAESIJAI
Cloud computing became a popular technology which influence not only
product development but also made technology business easy. The services
like infrastructure, platform and software can reduce the complexity of
technology requirement for any ecosystem. As the users of cloud-based
services increases the complexity of back-end technologies also increased.
The heterogeneous requirement of users in terms for various configurations
creates different unbalancing issues related to load. Hence effective load
balancing in a cloud system with reference to time and space become crucial
as it adversely affect system performance. Since the user requirement and
expected performance is multi-objective use of decision-making tools like
fuzzy logic will yield good results as it uses human procedure knowledge in
decision making. The overall system performance can be further improved by
dynamic resource scheduling using optimization technique like genetic
algorithm.
Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Cloud computing is a hot topic all over the world nowadays, through which customers can access information and computer power via a web browser. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Cloud computing means saving and accessing the data over the internet instead of local storage. In this paper, we have provided a short review on the types, models and architecture of the cloud environment.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Halogenation process of chemical process industries
Extending Grids with Cloud Resource Management for Scientific Computing
1. Extending Grids with Cloud Resource Management for Scientific Computing
1. INTRODUCTION
In the last decade, Grid computing gained high popularity in the field of scientific
computing through the idea of distributed resource sharing among institutions and scientists.
Scientific computing is traditionally a high-utilization workload, with production Grids often
running at over 80% utilization (generating high and often unpredictable latencies), and with
smaller national Grids offering a rather limited amount of high-performance resources. Running
large-scale simulations in such overloaded Grid environments often becomes latency bound or
suffers from well-known Grid reliability problems. Today, a new research direction coined by the
term Cloud computing proposes an alternative attractive to scientific computing scientists
primarily because of four main advantages.
First, Clouds promote the concept of leasing remote resources rather than buying own
hardware, which frees institutions from permanent maintenance costs and eliminates the burden
of hardware deprecation following Moore’s law.
Second, Clouds eliminate the physical overhead cost of adding new hardware such as
compute nodes to clusters or supercomputers and the financial burden of permanent over-provisioning
of occasionally needed resources. Through a new concept of “scaling-by credit-card”,
Clouds promise to immediately scale up/down an infrastructure according to the temporal
needs in a cost effective fashion.
Third, the concept of hardware virtualization can represent a significant breakthrough for
the automatic and scalable deployment of complex scientific software and can also significantly
improve the shared resource utilization.
Fourth, the provisioning of resources through business relationships constrains
specialized data centre companies in offering reliable services which existing Grid infrastructures
fail to deliver. Despite the existence of several integrated environments for transparent
programming and high-performance use of Grid infrastructures for scientific applications , there
are no results yet published in the community that report on extending them to enjoy the benefits
offered by Cloud computing. While there are several early efforts that investigate the
appropriateness of Clouds for scientific computing, they are either limited to simulations, do not
address the highly successful workflow paradigm, and do not attempt to extend Grids with
Clouds as a hybrid combined platform for scientific computing.
By: Bharat Kalia 1
2. Extending Grids with Cloud Resource Management for Scientific Computing
In this paper we extend a Grid workflow application development and computing
environment to harness resources leased by Cloud computing providers. Our goal is to provide an
infrastructure that allows the execution of workflows on conventional Grid resources which can
be supplemented on demand with additional Cloud resources, if necessary. We concentrate our
presentation on the extensions we brought to the resource management service to consider Cloud
resources, comprising new Cloud management, software (image) deployment, and security
components. We present experimental results using a real-world application in the Austrian Grid
environment, extended with an own academic Cloud constructed using the Eucalyptus
middleware and Xen virtualization technology.
By: Bharat Kalia 2
3. Extending Grids with Cloud Resource Management for Scientific Computing
2. BACKGROUND CONCEPTS
While there are several workflow execution middlewares for Grid computing, none is
known to support the new type of Cloud infrastructure.
2.1 ASKALON
ASKALON is a Grid application development and computing environment developed at
the University of Innsbruck with the goal of simplifying the development and optimization of
applications that can harness the power of Grid computing (see Figure 2.1). In ASKALON, the
user composes workflow applications at a high level of abstraction using a UML graphical
modeling tool. Workflows are specified as a directed graph of activity types representing an
abstract semantic description of the computation such as a Gaussian elimination algorithm, a Fast
Fourier Transform, or an N-body simulation. The activity types are interconnected in a workflow
through control flow and data flow dependencies. The abstract workflow representation is given
in an XML form to the ASKALON middleware services for transparent execution onto the Grid.
This task is mainly accomplished by a fault tolerant enactment engine, together with a
scheduling service in charge of computing optimized mappings of workflow activities onto the
available Grid resources. To achieve this task, the scheduler employs a resource management
service that consists of two main components: GridARM for discovery and brokerage of
hardware resources by interfacing with a Grid information service, and GLARE for registration
and provisioning of software resources. An important functionality component of GLARE is the
By: Bharat Kalia 3
4. Extending Grids with Cloud Resource Management for Scientific Computing
automatic provisioning of activity deployments on remote Grid sites, which are properly
configured installations of the legacy software and services implementing the activity types.
Once an activity deployment has been installed, we say that the remote resource has been
provisioned and can be used by the scheduler and enactment engine for the workflow execution.
This execution can be monitored using graphical tools or via the engines event system.
2.2 Cloud Computing
The buzzword Cloud computing is recently being increasingly used for the provisioning
various services through the Internet which are billed like utilities. From a scientific point of
view, the most popular interpretation of Cloud computing is Infrastructure as a Service (IaaS),
which provides generic means for hosting and provisioning of access to raw computing
infrastructure and its operating software. IaaS are typically provided by data a center renting
modern hardware facilities to customers that only pay for what hey effectively use, which frees
them from the burden of hardware maintenance and deprecation. IaaS is characterized by the
concept of resource virtualization which allows a customer to deploy and run his own guest
operating system on top of the virtualization software offered by the provider.
Virtualization in IaaS is also a key step towards distributed, automatic, and scalable
deployment, installation, and maintenance of software. To deploy a guest operating system
showing to the user another abstract and higher-level emulated platform, the user creates a virtual
machine image, in short image. In order to use a Cloud resource, the user needs to copy and boot
an image on top, called virtual machine instance, in short instance. After an instance has been
started on a Cloud resource , we say that the resource has been provisioned and can be used. If a
resource is no longer necessary, it must be released such that the user no longer pays for its use.
Commercial Cloud providers typically provide to customers a selection of resource classes or
instance types with different characteristics including CPU type, number of cores, memory, hard
disk, and I/O performance.
By: Bharat Kalia 4
5. Extending Grids with Cloud Resource Management for Scientific Computing
3. RESOURCE MANAGEMENT ARCHITECTURE
To enable the ASKALON Grid environment use Cloud resources from different
providers, we extended the resource management service three new components: Cloud
management, image catalogue, and security mechanisms. Whenever the high-performance Grid
resources are exhausted, the ASKALON scheduler has the option of supplementing them with
additional ones leased from Cloud providers to faster complete the workflow. A limit for the
maximum number of leased resources that are requested is set for each cloud in their credential
properties. This limit helps to save money and stay within the resource limits given by the cloud
provider. EC2 allows the users to request up to 20 instances on a normal account while bigger
resource requests require to contact Amazon manually. The used dps.cloud offers 12 cores and
any further requests could not be served so the limit for resource requests was set to 12. When a
deployment request for a new Cloud resource arrives from the scheduler, the resource manager
arranges its provisioning by performing the following steps(see fig 3.1).
Fig 3.1 The cloud-enhanced resource management architecture
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6. Extending Grids with Cloud Resource Management for Scientific Computing
1) Retrieves a signed request for a certain number of activity deployments needed to
complete the workflow;
2) The security component checks the credential of the request and which Clouds are
available for the requesting user;
3) The image catalogue component retrieves the predefined registered images for the
accessible Clouds;
4) The images are checked if they include the requested activity deployment or if they have
the capability to auto-deploy;
5) The instances are started using the Cloud management component and the image boot
process is monitored until a (SSH) control connection is possible to the new instance. If
the instance does not contain the requested activity deployment, an optional auto-deployment
process using GLARE takes place;
6) A new entry is created in GridARM with all information required by the new instance
such as identifier, IP address, and number of CPUs;
7) All the activity deployments contained in the booted image are registered in GLARE;
8) The resource manager replies to the scheduler with the new deployments for the
requested activity types.
3.1 Cloud management
In terms of functionality, the Cloud-enabled resource manager extends the old Grid
resource manager with two new runtime functions: the request for new deployments for a
specific activity type and the release of a resource after its use ended. The Cloud management
component is responsible for provisioning, releasing, and checking the status of an instance.
Figure 3.2 shows a generic instance state transition diagram which we constructed by analyzing
the instance states in different Cloud implementations.
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7. Extending Grids with Cloud Resource Management for Scientific Computing
Upon a request for an additional resources, the Cloud management component selects the
resources (instance types) with the best price/performance ratio to which it transfers a image
containing the required activity deployments, or enabled withauto-deployment functionality
(state starting). In the running state the image is booted, while in the accessible the instance is
ready to be used. In the resizing phase the underlying hardware is reconfigured, e.g. by adding
more cores or memory , while in the restarting phase the image is rebooted, for example upon a
kernel change. The release of an image upon shut down is signaled by the terminated
state.Thefailed state indicates an error of any kind that automatically releases the resource. Upon
a resource release, the instance and all the deployments registered are removed from GridARM
and GLARE. However, if there are pending requests for an existing instance containing the
required deployments, the resource manager can optimize the provisioning by reusing the same
instance for the next user if they share same Cloud credential.
The Cloud manager also maintains a registry of the available resources classes (or
instance types) offered by different Cloud providers containing the number of cores, the amount
of memory and hard disk, I/O performance, and cost per unit of computation. For example, Table
3.1 contains the resource class information offered by four Cloud providers, which need to be
manually entered by the resource manager administrator in the Cloud management registry due
to the lack of a corresponding API.Today, different commercial and academic Clouds provide
different interfaces to their services, as no official standard has been defined yet.
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8. Extending Grids with Cloud Resource Management for Scientific Computing
We are using in the Cloud management component the Amazon API defined by EC2,
which is also implemented by Eucalyptus and Nimbus middlewares used for building “academic
Clouds”. To support more Clouds, plugins to other interfaces or using a metacloud software are
required. Table 3.2 shows an overview of the Cloud providers those are currently offering API
access to provision and release their resources, and which could therefore be integrated into an
automatic resource management system. This overview also shows the difference in available
hardware configurations of the selected five providers. There is a also wide range of Cloud
providers that do not offer an API to control the instances and therefore are not listed.
3.2 Image catalogue
Each Cloud infrastructure provides a different set of images offered by the provider or
defined by the users themselves, which need to be organized in order to be of effective use. For
example, the Amazon EC2 API provides built-in functionality to retrieve the list of available
images, while other providers only offer plain text HTML pages listing their offers, while some
providers have the lists of possible images hidden in their instance start API documentation. The
information about the images provided by different Cloud providers is in all cases limited to
simple string name and lacks additional semantic descriptions of image characteristics such the
supported architecture, operating system type, embedded software deployments, or support for
auto-deployment functionality. The task of the image catalogue is to systematically organize this
missing information, which is registered manually by the resource manager administrator.
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9. Extending Grids with Cloud Resource Management for Scientific Computing
Figure 3.3 shows the hierarchical image catalogue structure were each provider has an
assigned set of images, and for each image there is a list of embedded activity deployments, or
which can be automatically deployed. Custom images with embedded deployments have reduced
the provisioning overhead, as the deployment part is skipped. Images are currently not
interoperable between Cloud providers which generate a large image catalogue that needs to be
managed. As Table 3.2 demonstrates, the variety of the offers between different providers is
high. For example, Amazon EC2 has by far the most images available, also due to the fact that
users can upload their custom or modified images and make them available to the community. At
the other extreme, AppNexus only provides one standard instance for its users.
The bus size of the different images may create additional problems with the activity
deployments on the started instances, e.g. Amazon EC2 only offers 32 bit architectures on their
two cheapest instance types, while the others are 64 bit.
3.3 Security
Security is a critical topic in Cloud computing with applications running and producing
confidential data on remote unknown resources that need to pe protected. Several issues need to
be addressed such as authentication to the Cloud services and to the started instances, as well as
securing user credit card information. Authentication is supported by existing providers either
through a key pair and certificate mechanism, or by using login and password combinations (see
Table 3.2).
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10. Extending Grids with Cloud Resource Management for Scientific Computing
One can distinguish between two types of credentials in Cloud environments:
user credential is a persistent credential associated with a credit card number used
for provisioning and releasing Cloud resources;
instance credential is a temporary credential used for manipulating an instance
through the SSH protocol.
Since these credentials are issued separately by the providers, users will have different
credentials for each Cloud infrastructure, in addition to their Grid Security Infrastructure (GSI)
certificate. The resource manager needs to manage these credentials in a safe manner, while
granting to the other services and to the application secure access to the deployed Cloud
resources. The security mechanism of the resource manager is based on GSI proxy delegation
credentials, which we extended with two secured repositories for Cloud access:
A MyCloud repository which, similar to a MyProxy repository, stores copies of
the user credentials which can only be accessed by authenticating with a correct
GSI credential associated to it;
A MyInstance repository for storing temporary instance credentials generated for
each started instance.The detailed security procedure upon an image
deploymentrequest is as follows (see Figure 3.4):
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11. Extending Grids with Cloud Resource Management for Scientific Computing
1) A GSI-authenticated request for a new image deployment is received.
2) The security component checks in the MyCloud repository for the Clouds for
which the user has valid credentials;
3) A new credential is generated for the new instance that needs to be started. In case
multiple images need to be started, the same instance credential can be used to
reduce the credential generation overhead (i.e. about 6-10 seconds in our
experiments, including the communication overhead);
4) The new instance credentials are stored in the MyImage repository, which will
only be accessible to the enactment engine service for job execution after a proper
GSI authentication;
5) A start instance request is sent to the Cloud using the newly generated instance
credential;
6) When an instance is released, the resource manager deletes the corresponding
credential from the MyInstance repository.
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12. Extending Grids with Cloud Resource Management for Scientific Computing
4. CONCLUSION
In this paper we extended a Grid workflow development and computing environment to
use on-demand Cloud resources in Grid environments offering a limited amount of high-performance
resources. We presented the extensions to the resource management architecture to
consider Cloud resources comprising three new components: Cloud management for automatic
image management, image catalogue for management of software deployments, and security for
authenticating with multiple Cloud providers. We presented experimental results of using a real-world
application in the Austrian Grid environment, extended with an own academic Cloud. Our
results demonstrate that workflows with large problem sizes can significantly benefit from being
executed in a combined Grid and Cloud environment.
Similarly, the cost of using Cloud resources is more convenient for large workflows due
to the hourly billing increment policies applied. Our environment currently supports providers
offering Amazon EC2-compliant interfaces, which we plan to extend for other Cloud providers.
We also plan to investigate more sophisticated multi-criteria scheduling strategies such as the
effect of the resource class granularity (i.e. number of underlying cores) on the execution time,
resource allocation efficiency, and the overall cost. We also intend to use the Cloud simulation
framework presented in for validating various scheduling and optimization strategies at a larger
scale.
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