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Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybri...1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
35 content distribution with dynamic migration of services for minimum cost u...INFOGAIN PUBLICATION
Content Delivery Networks are the key for today’s internet content delivery. Users are knowingly or unknowingly accessing the CDN via internet. No matter how much the data retrieved by the user it may contain the CDN hand behind every character of text and every pixel of image. CDN came into existence to solve the delay problem. The moment when a user requests for a web page and the response delivered to the corresponding users web browser facing a huge delay. The main goal of this paper is content distribution of web services to multiple data centers placed in different geographical locations and providing security. A content distribution service is a major part of popular Internet applications. In proposed system hybrid clouds are used i.e., both private cloud as well as public cloud. One data center is allocated to each region. Providing security to the data is always an important issue because of the critical nature of the cloud and very large amount of complicated data it carries. To provide security cipher text policy algorithm is used. Authentication technique is used to verify the user authentication. If the user is authorized to access services then and only he receives configuration key to use.
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
Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybri...1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
35 content distribution with dynamic migration of services for minimum cost u...INFOGAIN PUBLICATION
Content Delivery Networks are the key for today’s internet content delivery. Users are knowingly or unknowingly accessing the CDN via internet. No matter how much the data retrieved by the user it may contain the CDN hand behind every character of text and every pixel of image. CDN came into existence to solve the delay problem. The moment when a user requests for a web page and the response delivered to the corresponding users web browser facing a huge delay. The main goal of this paper is content distribution of web services to multiple data centers placed in different geographical locations and providing security. A content distribution service is a major part of popular Internet applications. In proposed system hybrid clouds are used i.e., both private cloud as well as public cloud. One data center is allocated to each region. Providing security to the data is always an important issue because of the critical nature of the cloud and very large amount of complicated data it carries. To provide security cipher text policy algorithm is used. Authentication technique is used to verify the user authentication. If the user is authorized to access services then and only he receives configuration key to use.
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
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.
MCCVA: A NEW APPROACH USING SVM AND KMEANS FOR LOAD BALANCING ON CLOUDijccsa
Nowadays, the demand of using resources, using services via the intranet system or on the Internet is rapidly growing. The respective problem coming is how to use these resources effectively in terms of time and quality. Therefore, the network QoS and its economy are people concerns, cloud computing was born in an inevitable trend. However, managing resources and scheduling tasks in virtualized data centres on the cloud are challenging tasks. Currently, there are a lot of Load Balancing algorithms applied in clouds and proposed by many authors, scholars, and experts. These existing methods are more about natural and heuristic, but the application of AI, or modern datamining technologies, in load balancing is not too popular due to the different characteristics of cloud. In this paper, we propose an algorithm to reduce the processing time (makespan) on cloud computing, helping the load balancing work more efficiency. Here, we use the SVM algorithm to classify the coming Requests, K - Mean to cluster the VMs in cloud, then the LB will allocate the requests into the VMs in the most reasonable way. In this way, request with the least processing time will be allocated to the VMs with the lowest usage. We name this new proposal as MCCVA - Makespan Classification & Clustering VM Algorithm. We have experimented and evaluated this algorithm in CloudSim, a cloud simulation environment, we obtained better results than some other wellknown algorithms. With this MCCVA, we can see the big potential of AI and datamining in Load Balancing, we can further develop LB with AI to achieve better and better results of QoS.
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...SaikiranReddy Sama
In Dynamic Resource Allocation, WE PRESENT A SYSTEM THAT USES VIRTUALIZATION TECHNOLOGY TO ALLOCATE DATA CENTER RESOURCES DYNAMICALLY.
WE INTRODUCE THE CONCEPT OF “SKEWNESS”.
And BY MINIMIZING SKEWNESS, WE CAN COMBINE DIFFERENT TYPES 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.
Dynamic resource Allocation using Virtual Machines For Cloud Computing
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.
LOAD BALANCING ALGORITHM ON CLOUD COMPUTING FOR OPTIMIZE RESPONE TIMEijccsa
To improve the performance of cloud computing, there are many parameters and issues that we should consider, including resource allocation, resource responsiveness, connectivity to resources, unused resources exploration, corresponding resource mapping and planning for resource. The planning for the use of resources can be based on many kinds of parameters, and the service response time is one of them.
The users can easily figure out the response time of their requests, and it becomes one of the important QoSs. When we discover and explore more on this, response time can provide solutions for the distribution, the load balancing of resources with better efficiency. This is one of the most promising
research directions for improving the cloud technology. Therefore, this paper proposes a load balancing algorithm based on response time of requests on cloud with the name APRA (ARIMA Prediction of Response Time Algorithm), the main idea is to use ARIMA algorithms to predict the coming response time, thus giving a better way of effectively resolving resource allocation with threshold value. The experiment
result outcomes are potential and valuable for load balancing with predicted response time, it shows that prediction is a great direction for load balancing.
Linked List Implementation of Discount Pricing in Cloudpaperpublications3
Abstract: In the cloud computing environment computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. In order to enjoy these discounts, the customers must be ready to adjust the time limits. By strategically scheduling multiple customers’ resource request, a cloud broker takes the responsibility of distributing the discounts offered by cloud service providers. Here the focus is on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. A randomized online stack-centric scheduling algorithm (ROSA) is implemented with linked list in order to maintain the status of the resource and to allocate resources without time constrains.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Performance and Cost Evaluation of an Adaptive Encryption Architecture for Cl...Editor IJLRES
The cloud database as a service is a novel paradigm that can support several Internet-based applications, but its adoption requires the solution of information confidentiality problems. We propose a novel architecture for adaptive encryption of public cloud databases that offers an interesting alternative to the tradeoff between the required data confidentiality level and the flexibility of the cloud database structures at design time. We demonstrate the feasibility and performance of the proposed solution through a software prototype. Moreover, we propose an original cost model that is oriented to the evaluation of cloud database services in plain and encrypted instances and that takes into account the variability of cloud prices and tenant workloads during a medium-term period.
Adaptive Offloading in Mobile Cloud Computing by automatic partitioning approach of tasks is the idea to augment execution through migrating heavy computation from mobile devices to resourceful cloud servers and then receive the results from them via wireless networks. Offloading is an effective way to
overcome the resources and functionalities constraints
of the mobile devices since it can release them from
intensive processing and increase performance of the
mobile applications, in terms of response time.
Offloading brings many potential benefits, such as
energy saving, performance improvement, reliability
improvement, ease for the software developers and
better exploitation of contextual information.
Parameters about method transitions, response times,
cost and energy consumptions are dynamically reestimated
at runtime during application executions.
Profit Maximization for Service Providers using Hybrid Pricing in Cloud Compu...Editor IJCATR
Cloud computing has recently emerged as one of the buzzwords in the IT industry. Several IT vendors are promising to offer computation, data/storage, and application hosting services, offering Service-Level Agreements (SLA) backed performance and uptime promises for their services. While these „clouds‟ are the natural evolution of traditional clusters and data centers, they are distinguished by following a pricing model where customers are charged based on their utilization of computational resources, storage and transfer of data. They offer subscription-based access to infrastructure, platforms, and applications that are popularly termed as IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). In order to improve the profit of service providers we implement a technique called hybrid pricing , where this hybrid pricing model is a pooled with fixed and spot pricing techniques.
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.
MCCVA: A NEW APPROACH USING SVM AND KMEANS FOR LOAD BALANCING ON CLOUDijccsa
Nowadays, the demand of using resources, using services via the intranet system or on the Internet is rapidly growing. The respective problem coming is how to use these resources effectively in terms of time and quality. Therefore, the network QoS and its economy are people concerns, cloud computing was born in an inevitable trend. However, managing resources and scheduling tasks in virtualized data centres on the cloud are challenging tasks. Currently, there are a lot of Load Balancing algorithms applied in clouds and proposed by many authors, scholars, and experts. These existing methods are more about natural and heuristic, but the application of AI, or modern datamining technologies, in load balancing is not too popular due to the different characteristics of cloud. In this paper, we propose an algorithm to reduce the processing time (makespan) on cloud computing, helping the load balancing work more efficiency. Here, we use the SVM algorithm to classify the coming Requests, K - Mean to cluster the VMs in cloud, then the LB will allocate the requests into the VMs in the most reasonable way. In this way, request with the least processing time will be allocated to the VMs with the lowest usage. We name this new proposal as MCCVA - Makespan Classification & Clustering VM Algorithm. We have experimented and evaluated this algorithm in CloudSim, a cloud simulation environment, we obtained better results than some other wellknown algorithms. With this MCCVA, we can see the big potential of AI and datamining in Load Balancing, we can further develop LB with AI to achieve better and better results of QoS.
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...SaikiranReddy Sama
In Dynamic Resource Allocation, WE PRESENT A SYSTEM THAT USES VIRTUALIZATION TECHNOLOGY TO ALLOCATE DATA CENTER RESOURCES DYNAMICALLY.
WE INTRODUCE THE CONCEPT OF “SKEWNESS”.
And BY MINIMIZING SKEWNESS, WE CAN COMBINE DIFFERENT TYPES 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.
Dynamic resource Allocation using Virtual Machines For Cloud Computing
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.
LOAD BALANCING ALGORITHM ON CLOUD COMPUTING FOR OPTIMIZE RESPONE TIMEijccsa
To improve the performance of cloud computing, there are many parameters and issues that we should consider, including resource allocation, resource responsiveness, connectivity to resources, unused resources exploration, corresponding resource mapping and planning for resource. The planning for the use of resources can be based on many kinds of parameters, and the service response time is one of them.
The users can easily figure out the response time of their requests, and it becomes one of the important QoSs. When we discover and explore more on this, response time can provide solutions for the distribution, the load balancing of resources with better efficiency. This is one of the most promising
research directions for improving the cloud technology. Therefore, this paper proposes a load balancing algorithm based on response time of requests on cloud with the name APRA (ARIMA Prediction of Response Time Algorithm), the main idea is to use ARIMA algorithms to predict the coming response time, thus giving a better way of effectively resolving resource allocation with threshold value. The experiment
result outcomes are potential and valuable for load balancing with predicted response time, it shows that prediction is a great direction for load balancing.
Linked List Implementation of Discount Pricing in Cloudpaperpublications3
Abstract: In the cloud computing environment computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. In order to enjoy these discounts, the customers must be ready to adjust the time limits. By strategically scheduling multiple customers’ resource request, a cloud broker takes the responsibility of distributing the discounts offered by cloud service providers. Here the focus is on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. A randomized online stack-centric scheduling algorithm (ROSA) is implemented with linked list in order to maintain the status of the resource and to allocate resources without time constrains.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Performance and Cost Evaluation of an Adaptive Encryption Architecture for Cl...Editor IJLRES
The cloud database as a service is a novel paradigm that can support several Internet-based applications, but its adoption requires the solution of information confidentiality problems. We propose a novel architecture for adaptive encryption of public cloud databases that offers an interesting alternative to the tradeoff between the required data confidentiality level and the flexibility of the cloud database structures at design time. We demonstrate the feasibility and performance of the proposed solution through a software prototype. Moreover, we propose an original cost model that is oriented to the evaluation of cloud database services in plain and encrypted instances and that takes into account the variability of cloud prices and tenant workloads during a medium-term period.
Adaptive Offloading in Mobile Cloud Computing by automatic partitioning approach of tasks is the idea to augment execution through migrating heavy computation from mobile devices to resourceful cloud servers and then receive the results from them via wireless networks. Offloading is an effective way to
overcome the resources and functionalities constraints
of the mobile devices since it can release them from
intensive processing and increase performance of the
mobile applications, in terms of response time.
Offloading brings many potential benefits, such as
energy saving, performance improvement, reliability
improvement, ease for the software developers and
better exploitation of contextual information.
Parameters about method transitions, response times,
cost and energy consumptions are dynamically reestimated
at runtime during application executions.
Profit Maximization for Service Providers using Hybrid Pricing in Cloud Compu...Editor IJCATR
Cloud computing has recently emerged as one of the buzzwords in the IT industry. Several IT vendors are promising to offer computation, data/storage, and application hosting services, offering Service-Level Agreements (SLA) backed performance and uptime promises for their services. While these „clouds‟ are the natural evolution of traditional clusters and data centers, they are distinguished by following a pricing model where customers are charged based on their utilization of computational resources, storage and transfer of data. They offer subscription-based access to infrastructure, platforms, and applications that are popularly termed as IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). In order to improve the profit of service providers we implement a technique called hybrid pricing , where this hybrid pricing model is a pooled with fixed and spot pricing techniques.
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,
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Understanding the cloud computing stackSatish Chavan
Understanding the cloud computing stack
Introduction
Key characteristics
At Glance
Standardization, Migration &Adaptation
Service models
Deployment models
Network as a Service
Software as a Service (SaaS).
Platform as a Service (PaaS).
Infrastructure as a Service (IaaS).
Communications as a Service (CaaS)
Data as a Service - DaaS
Benefits & Challenges
Security Risks & Challenges
Cloud Vendors
Hire some ii towards privacy-aware cross-cloud service composition for big da...ieeepondy
Hire some ii towards privacy-aware cross-cloud service composition for big data applications
+91-9994232214,8144199666, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2015-2016
-----------------------------------
Contact:+91-9994232214,+91-8144199666
Email:ieeeprojectchennai@gmail.com
ieee projects chennai, ieee projects bangalore
Load Balancing in Cloud Computing Environment: A Comparative Study of Service...Eswar Publications
Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The
load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
In this paper we are study-ing about cloud computing, their types, need to use cloud computing. We also study the architecture of the mobile cloud computing. So we included new techniques for backup and restoring data from mobile to cloud. Here we proposed to apply some compres-sion technique while backup and restore data from Smartphone to cloud and cloud to the Smartphone.
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.
ESTIMATING CLOUD COMPUTING ROUND-TRIP TIME (RTT) USING FUZZY LOGIC FOR INTERR...IJCI JOURNAL
Cloud computing is widely considered a transformative force in the computing world and is poised to
replace the traditional office setup as an industry standard. However, given the relative novelty of these
services and challenges such as the impact of physical distance on round-trip time (rtt), questions have
arisen regarding system performance and associated billing structures. The primary objective of this study
is to address these concerns. We aim to alleviate doubts by leveraging a fuzzy logic system to classify
distances between regions that support computing services and compare them with the conventional web
hosting format. To achieve this, we analyse the responses of one of these services, like amazon web
services, across different distance categories (near, medium, and far) between regions and strive to
conclude overall system performance. Our tests reveal that significant data is consistently lost during
customer transmission despite exhibiting superior round-trip times. We delve into this issue and present
our findings, which may illuminate the observed anomalous behaviour.
Similar to COST-MINIMIZING DYNAMIC MIGRATION OF CONTENT DISTRIBUTION SERVICES INTO HYBRID CLOUDS (20)
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
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Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
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The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
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• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
COST-MINIMIZING DYNAMIC MIGRATION OF CONTENT DISTRIBUTION SERVICES INTO HYBRID CLOUDS
1. COST-MINIMIZING DYNAMIC MIGRATION OF CONTENT
DISTRIBUTION SERVICES INTO HYBRID CLOUDS
Abstract—With the recent advent of cloud computing technologies, a growing
number of content distribution applications are contemplating a switch to cloud-
based services, for better scalability and lower cost. Two key tasks are involved for
such a move: to migrate the contents to cloud storage, and to distribute the web
service load to cloud-based web services. The main issue is to best utilize the cloud
as well as the application provider’s existing private cloud, to serve volatile
requests with service response time guarantee at all times, while incurring the
minimum operational cost. While it may not be too difficult to design a simple
heuristic, proposing one with guaranteed cost optimality over a long run of the
system constitutes an intimidating challenge. Employing Lyapunov
optimization techniques, we design a dynamic control algorithm to optimally place
contents and dispatch requests in a hybrid cloud infrastructure spanning geo-
distributed data centers, which minimizes overall operational cost over time,
subject to service response time constraints. Rigorous analysis shows that the
algorithm nicely bounds the response times within the preset QoS target, and
guarantees that the overall cost is within a small constant gap from the optimum
achieved by a T-slot look ahead mechanism with known future information. We
verify the performance of our dynamic algorithm with prototype-based evaluation.
EXISTING SYSTEM:
2. Migration of applications into clouds: A number of research projects have emerged
in recent years that explore the migration of services into a cloud platform. develop
an optimization model for migrating enterprise IT applications onto a hybrid cloud.
Their model takes into account enterprise-specific constraints, such as transaction
delays and security policies. Onetime optimal service deployment is considered,
while our work investigates optimal dynamic migration over time, to achieve the
long-term optimality. In epropose an intelligent algorithm to factor workload and
dynamically determine the service placement across the public cloud and the
private cloud. Their focus is on designing an algorithm for distinguishing base
workload and trespassing workload. Migration of content delivery services into
clouds: Some research efforts have been put into migrating generic content
delivery services onto clouds. MetaCDN by Pathan et al. a proof-of-concept
testbed, experiments on which show that deploying content delivery based on
storage clouds can improve utility, based on primitive content placement and
request routing mechanisms. Chen propose to build CDNs in the cloud in order to
minimize cost under the constraints of QoS requirement, but they only propose
greedy-strategy based heuristics without provable properties. In contrast, we target
an optimization framework which renders optimal migration solutions for long run
of the system.
3. PROPOSED SYSTEM:
The contribution of this work can be summarized as follows:
We propose a generic optimization framework for dynamic, optimal
migration of a content distribution service to a hybrid cloud consisting of a
private cloud and public geo-distributed cloud services.
We design a joint content placement and load distribution algorithm for
dynamic content distribution service deployment in the hybrid cloud.
Providers of content distribution services can practically apply it to guide
their service migration, with confidence in cost minimization and
performance guarantee, regardless of the request arrival pattern.
We demonstrate optimality of our algorithm with rigorous theoretical
analysis and prototype-based evaluation. The algorithm nicely bounds the
response times (including queueing and round-trip delays) within the preset
QoS target in cases of arbitrary request arrivals, and guarantees that the
overall cost is within a small constant gap from the optimum achieved by a
T-slot lookahead mechanism with information into the future.
Module 1
Hybrid Cloud
A hybrid cloud is a combination of a private cloud combined with the use of public
cloud services where one or several touch points exist between the environments.
The goal is to combine services and data from a variety of cloud models to create a
4. unified, automated, and well-managed computing environment. Combining public
services with private clouds and the data center as a hybrid is the new definition
of corporate computing. Not all companies that use some public and some private
cloud services have a hybrid cloud. Rather, a hybrid cloud is an environment where
the private and public services are used together to create value.
A cloud is hybrid
If a company uses a public development platform that sends data to a private
cloud or a data center–based application.
When a company leverages a number of SaaS (Software as a Service)
applications and moves data between private or data center resources.
When a business process is designed as a service so that it can connect with
environments as though they were a single environment.
Module 2
Dynamic Migration
Currently, many Web services have been deployed by different organizations that
are widely distributed over the Internet. These are mostly software services
running on fixed hardware resources. When composing multiple services for a
system, it is likely that some selected software services are hosted at widely
5. distributed sites. This brings potential performance problems. Sending a service
request along with a large quantity of input data across the
wide area network can be costly. It increases the network traffic and raises the
potential of unexpected delays due to network congestions. This can be a major
barrier for applications that have real-time requirements. For example, a
commander may dynamically assemble a command and control application that
involves a large number of web services, such as many data services based on
continuous input from the remote sensors, image processing services, information
fusion services, etc. to assist her/his decision making. Communication among two
data processing services may involve a large amount of data and may result in
delays due to network congestions. Such delays can affect the timeliness of the
decision and cause costly consequences. However, if there are a limited number
of services to choose from, it may be difficult to significantly reduce the
communication latency. In cloud environment, this problem can be solved by
considering service migration. One of major advances in cloud environment is that
computing hardware resources and their management utilities are all provided as
services. The widely distributed computing resources can be used to host migrated
services to potentially minimize the communication cost. However, not all services
can be migrated. Services based on hardware resources are less flexible and cannot
be igrated (not in the cyber world). When the services involve common hardware
devices, the devices, even though non-migratable, are likely to be all over the
place. Thus, it is possible to select one that can result in minimized communication
cost. When a service involves specialized hardware, then it cannot be migrated.
6. Services can potentially be migrated, but the migration costs and gains have to be
evaluated to ensure net performance gains.
Module 3
The service migration problem
System Model We consider a typical content distribution application, which
provides a collection of contents (files), denoted as set M, to users spreading over
multiple geographical regions. There is a private cloud owned by the provider of
the content distribution application, which stores the original copies of all the
contents. The private cloud has an overall upload bandwidth of b units for serving
contents to users. There is a public cloud consisting of data centers located in
multiple geographical regions, denoted as set N. One data center resides in each
region. There are two types of inter-connected servers in each data center: storage
servers for data storage, and computing servers that support the running and
provisioning of virtual machines (VMs). Servers inside the same data center can
access each other via a certain DCN (Data Center Network). The provider of the
content distribution application (application provider) wishes to provision its
service by exploiting a hybrid cloud architecture, which includes the geo-
distributed public cloud and its private cloud. The major components of the content
distribution application include: (i) back-end storage of the contents and (ii) front-
end web service that serves users’ requests for contents. The application provider
may migrate both service components into the public cloud: contents an be
7. replicated in storage servers in the cloud, while requests can be dispatched to web
services installed on VMs on the computing servers.
Module 4
Cost-Minimizing Service MigrationProblem
We suppose that the system runs in a time-slotted fashion. Each time slot is a unit
time which is enough for uploading any file m 2 M with size v(m) (bytes) at the
unit bandwidth. In time slot t, a(m) j (t) requests are generated for downloading file
m 2 M, from users in region j. We assume that the request arrival is an arbitrary
process over time, and the number of requests arising from one region for a file in
each time slot is upper-bounded by Amax. The cost of uploading a byte from the
private cloud is h. The charge for storage at data center i is pi per byte per unit
time. gi and oi per byte are charged for uploading from and downloading into data
center i, respectively. The cost for renting a VM instance in data center i is fi per
unit time. These charges follow the charging model of leading commercial cloud
providers, such as Amazon EC2 and S3. We assume that the storage capacity in
each data center is sufficient for storing contents from this content distribution
application. We also assume that each request is served at one unit bandwidth, and
the number of requests that a VM in data center i can serve per unit time.
Module 5
8. Dynamic migration algorithm
In this section, we design a dynamic control algorithm using Lyapunov
optimization techniques, which solves the optimal migration problem in and
bounds the time-averaged round-trip delays and queueing delays for each request.
We also discuss its practical implementation. Bounding Delays The optimization
problem includes a constraint on time-averaged variable values, i.e., inequality.
Our dynamic algorithm will only be able to adjust variables in each time slot. How
can we guarantee this inequality by controlling the variable values over time?
To satisfy constraint , we resort to the virtual queue techniques in Lyapunov
optimization.
CONCLUSION
This paper investigates optimal migration of a content distribution service to a
hybrid cloud consisting of a private cloud and public geo-distributed cloud
services. We propose a generic optimization framework based on Lyapunov
optimization theory, and design a dynamic, joint content placement and request
distribution algorithm, which minimizes the operational cost of the application
with QoS guarantees. We theoretically show that our algorithm approaches the
optimality achieved by a mechanism with known information in the future T time
slots by a small gap, no matter what the request arrival pattern is. Our prototype-
based evaluation verifies our theoretical findings. We intend to extend the
framework to specific content distribution services with detailed requirements,
9. such as video-on-demand services or social media applications, in our ongoing
work.
REFERENCES
[1] Amazon CloudFront, http://aws.amazon.com/cloudfront/.
[2] Microsoft Azure, http://www.microsoft.com/windowsazure/.
[3] Google App Engine, http://code.google.com/appengine/.
[4] Dropbox, http://www.dropbox.com/.
[5] Microsoft Office Web Apps, http://office.microsoft.com/enus/ web-apps/.
[6] Google docs, http://docs.google.com/.
[7] M. Hajjat, X. Sun, Y. E. Sung, D. Maltz, and S. Rao, “Cloudward Bound:
Planning for Beneficial Migration of Enterprise Applications to the Cloud,” in
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[8] H. Zhang, G. Jiang, K. Yoshihira, H. Chen, and A. Saxena, “Intelligent
Workload Factoring for a Hybrid Cloud Computing Model,” in Proc. of the
International Workshop on Cloud Services (IWCS 2009), June 2009.
[9] H. Li, L. Zhong, J. Liu, B. Li, and K. Xu, “Cost-effective Partial Migration of
VoD Services to Content Clouds,”in Proc. ofIEEE CLOUD, July 2011.
[10] X. Cheng and J. Liu, “Load-Balanced Migration of Social Media to Content
Clouds,” in Proc. of NOSSDAV, June 2011.
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layer control in wireless networks,” Foundations and Trends in Networking, vol. 1,
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[12] M. J. Neely, Stochastic Network Optimization with Application to
Communication and Queueing Systems. Morgan & Claypool, 2010.
[13] “Energy optimal control for time varying wireless networks,” IEEE Tran. on
Information Theory, no. 7, pp. 2915–2934, July 2006.
[14] M. M. Amble, P. Parag, S. Shakkottai, and L. Ying, “Content- Aware Caching
and Traffic Management in Content Distribution Networks,” in Proc. of IEEE
INFOCOM, April 2011.