International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
Optimizing Streaming Server Selection for CDN-delivered Live StreamingZhenyun Zhuang
LNCS 2012
Content Delivery Networks (CDNs) have been widely used to deliver
web contents on today’s Internet. Gaining tremendous popularity, live streaming
is also increasingly being delivered by CDNs. Compared to conventional static
or dynamic web contents, the new application type of live streaming exposes
unique characteristics that pose challenges to the underlying CDN infrastructure.
Unlike traditional web-objects fetching, which allows Edge Servers to cache contents
and thus typically only involves Edge Servers for delivering contents, live
streaming requires real-time full CDN-streaming paths that span across Ingest
Servers, Origin Servers and Edge Servers.
DNS is the standard practice for enabling dynamic assignment of servers. GeoDNS,
a specialized DNS system, provides DNS resolution by taking into account the
geographical locations of end-users and CDN servers. Though GeoDNS effectively
redirects users to nearest CDN Edge Servers, it may not be able to select
the optimal Origin Server for relaying a live stream to Edge Servers due to the
unique characteristics of live streaming. In this work, we consider the requirements
of delivering live streaming with CDN, and propose advanced design for
selecting optimal Origin Streaming Servers in order to reduce network transit
cost and increase viewers’ experience. We further propose a live-streaming specific
GeoDNS design for selecting optimal Origin Servers to serve Edge Servers.
Survey paper on Virtualized cloud based IPTV Systemijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
Optimizing Streaming Server Selection for CDN-delivered Live StreamingZhenyun Zhuang
LNCS 2012
Content Delivery Networks (CDNs) have been widely used to deliver
web contents on today’s Internet. Gaining tremendous popularity, live streaming
is also increasingly being delivered by CDNs. Compared to conventional static
or dynamic web contents, the new application type of live streaming exposes
unique characteristics that pose challenges to the underlying CDN infrastructure.
Unlike traditional web-objects fetching, which allows Edge Servers to cache contents
and thus typically only involves Edge Servers for delivering contents, live
streaming requires real-time full CDN-streaming paths that span across Ingest
Servers, Origin Servers and Edge Servers.
DNS is the standard practice for enabling dynamic assignment of servers. GeoDNS,
a specialized DNS system, provides DNS resolution by taking into account the
geographical locations of end-users and CDN servers. Though GeoDNS effectively
redirects users to nearest CDN Edge Servers, it may not be able to select
the optimal Origin Server for relaying a live stream to Edge Servers due to the
unique characteristics of live streaming. In this work, we consider the requirements
of delivering live streaming with CDN, and propose advanced design for
selecting optimal Origin Streaming Servers in order to reduce network transit
cost and increase viewers’ experience. We further propose a live-streaming specific
GeoDNS design for selecting optimal Origin Servers to serve Edge Servers.
Survey paper on Virtualized cloud based IPTV Systemijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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.
Chaining Algorithm and Protocol for Peer-to-Peer Streaming Video on Demand Sy...ijwmn
As the various architectures and protocol have been implemented a true VoD system has great demand in the global users. The traditional VoD system does not provide the needs and demands of the global users. The major problem in the traditional VoD system is serving of video stream among clients is duplicated and streamed to the different clients, which consumes more server bandwidth and the client uplink bandwidth is not utilized and the performance of the system degrades. Our objective in this paper is to send one server stream sufficient to serve the many clients without duplicating the server stream. Hence we have proposed a protocol and algorithm that chains the proxy servers and subscribed clients utilize client’s uplink bandwidth such that the load on the server is reduced. We have also proved that less rejection ratio of the clients and better utilization of the buffer and bandwidth for the entire VoD system.
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...IEEEFINALYEARPROJECTS
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Reprinted with permission of NCTA, from the 2014 Cable Connection Spring Technical Forum Conference Proceedings. For more information on Cisco cloud solutions, visit: http://www.cisco.com/c/en/us/products/cloud-systems-management/index.html
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
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Virtualization Technology using Virtual Machines for Cloud ComputingIJMER
Cloud computing is the delivery of computing and storage capacity as a service to a community of end users. The name “cloud computing” comes from the use of a cloud-shaped symbol as an abstraction for the complex infrastructure it contains in system diagrams. Cloud computing entrusts services with a user's software, data and computation over a network. End users access cloud-based applications through a web browser or mobile application or a light-weight desktop while the business software and user's data are stored on servers at a remote location. Proponents claim that cloud computing environment allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables IT industry to more rapidly adjust resources to meet fluctuating and unpredictable business demand. In this paper, we present a system that uses virtualization technology to allocate the data center resources dynamically based on the application demands and support green computing by optimizing the number of servers in use. This method multiplexes virtual to physical resources adaptively based on the changing demand. We use the concept of skewness metric to combine virtual machines with different resource characteristics appropriately so that the capacities of servers are well utilized.
A Strategic Evaluation of Energy-Consumption and Total Execution Time for Clo...idescitation
Cloud computing is a very budding area in the
research field and as well as in the IT enterprises. 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. In this era of rapid usage
of Internet all over the world, Cloud computing has become
the center of Internet-oriented business place. For enterprises,
cloud computing is the worthy of consideration and they try to
build business systems with minimal costs, higher profits and
more choice; for large-scale industry, energy consumption
and total execution tome are the two important aspects of
cloud computing. In the current scenario, IT Enterprises are
trying to minimize the energy-consumption which, in turn,
maximizes the profit of the industry. And they are also trying
to reduce total execution time which, in turn, is concerned
with providing better Quality of Service (QoS). Therefore, in
this paper we have made an attempt to evaluate energy-
consumption and total execution time using CloudSim
simulator which helps to make evaluation performance of
energy consumption and total execution time of user
application.
Dynamic Chunks Distribution Scheme for Multiservice Load Balancing Using Fibo...Editor IJCATR
Cloud computing is collection of distributed hosts which allows services on demand to user. The Centralized cloud based
multimedia system CMS[4], materialized because huge number of users demand for various multimedia services through the Internet
at the same time and it is hard to design effective load balancing algorithm. Load Balancing is the process which are used to distribute
workloads across aggregate computing resources that maximize throughput, minimize latency. In this paper videos are split up into no
of chunks and stored at hosts in a distributed manner, The chunk size increased to reduce time lag and improve performance. The
cluster heads will monitor all the distribution host loads and client request which could not allow the direct communication between
Client and host .Fibonacci-based breaking scheme is introduced to split a video file into number of chunks that allows to reduce the
provisioning delay received by users and to optimize the resource utilization by reducing the idle time. The proposed scheme will able
to view the whole video by the end user without any delay.
Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybri...nexgentechnology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
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
Chaining Algorithm and Protocol for Peer-to-Peer Streaming Video on Demand Sy...ijwmn
As the various architectures and protocol have been implemented a true VoD system has great demand in the global users. The traditional VoD system does not provide the needs and demands of the global users. The major problem in the traditional VoD system is serving of video stream among clients is duplicated and streamed to the different clients, which consumes more server bandwidth and the client uplink bandwidth is not utilized and the performance of the system degrades. Our objective in this paper is to send one server stream sufficient to serve the many clients without duplicating the server stream. Hence we have proposed a protocol and algorithm that chains the proxy servers and subscribed clients utilize client’s uplink bandwidth such that the load on the server is reduced. We have also proved that less rejection ratio of the clients and better utilization of the buffer and bandwidth for the entire VoD system.
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...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
Reprinted with permission of NCTA, from the 2014 Cable Connection Spring Technical Forum Conference Proceedings. For more information on Cisco cloud solutions, visit: http://www.cisco.com/c/en/us/products/cloud-systems-management/index.html
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
Virtualization Technology using Virtual Machines for Cloud ComputingIJMER
Cloud computing is the delivery of computing and storage capacity as a service to a community of end users. The name “cloud computing” comes from the use of a cloud-shaped symbol as an abstraction for the complex infrastructure it contains in system diagrams. Cloud computing entrusts services with a user's software, data and computation over a network. End users access cloud-based applications through a web browser or mobile application or a light-weight desktop while the business software and user's data are stored on servers at a remote location. Proponents claim that cloud computing environment allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables IT industry to more rapidly adjust resources to meet fluctuating and unpredictable business demand. In this paper, we present a system that uses virtualization technology to allocate the data center resources dynamically based on the application demands and support green computing by optimizing the number of servers in use. This method multiplexes virtual to physical resources adaptively based on the changing demand. We use the concept of skewness metric to combine virtual machines with different resource characteristics appropriately so that the capacities of servers are well utilized.
A Strategic Evaluation of Energy-Consumption and Total Execution Time for Clo...idescitation
Cloud computing is a very budding area in the
research field and as well as in the IT enterprises. 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. In this era of rapid usage
of Internet all over the world, Cloud computing has become
the center of Internet-oriented business place. For enterprises,
cloud computing is the worthy of consideration and they try to
build business systems with minimal costs, higher profits and
more choice; for large-scale industry, energy consumption
and total execution tome are the two important aspects of
cloud computing. In the current scenario, IT Enterprises are
trying to minimize the energy-consumption which, in turn,
maximizes the profit of the industry. And they are also trying
to reduce total execution time which, in turn, is concerned
with providing better Quality of Service (QoS). Therefore, in
this paper we have made an attempt to evaluate energy-
consumption and total execution time using CloudSim
simulator which helps to make evaluation performance of
energy consumption and total execution time of user
application.
Dynamic Chunks Distribution Scheme for Multiservice Load Balancing Using Fibo...Editor IJCATR
Cloud computing is collection of distributed hosts which allows services on demand to user. The Centralized cloud based
multimedia system CMS[4], materialized because huge number of users demand for various multimedia services through the Internet
at the same time and it is hard to design effective load balancing algorithm. Load Balancing is the process which are used to distribute
workloads across aggregate computing resources that maximize throughput, minimize latency. In this paper videos are split up into no
of chunks and stored at hosts in a distributed manner, The chunk size increased to reduce time lag and improve performance. The
cluster heads will monitor all the distribution host loads and client request which could not allow the direct communication between
Client and host .Fibonacci-based breaking scheme is introduced to split a video file into number of chunks that allows to reduce the
provisioning delay received by users and to optimize the resource utilization by reducing the idle time. The proposed scheme will able
to view the whole video by the end user without any delay.
Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybri...nexgentechnology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
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 HYBR...Nexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
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 contentnexgentech15
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
The main problem is to avoid the complexity of retrieving the video content without streaming problem in multi network clients. The proposed work is to improve Collaboration among streaming contents on server resources in order to improve the network performance. Implementing network collaboration on a content delivery scenario, with a strong reduction of data transferred via servers. Audio and video files are transmitted in blocks to clients through the peer using the Network Coding Equivalent Content Distribution scheme. The objective of the system is to tolerate out-of-order arrival of blocks in the stream and is resilient to transmission losses of an arbitrary number of intermediate blocks, without affecting the verifiability of remaining blocks in the stream. To formulate the joint rate control and packet scheduling problem as an integer program where the objective is to minimize a cost function of the expected video distortion. Suggestions of cost functions are proposed in order to provide service differentiation and address fairness among users.
Mobile-Based Video Caching Architecture Based on Billboard Manager csandit
Video streaming services are very popular today. Increasingly, users can now access multimedia applications and video playback wirelessly on their mobile devices. However, a significant challenge remains in ensuring smooth and uninterrupted transmission of almost any
size of video file over a 3G network, and as quickly as possible in order to optimize bandwidth consumption. In this paper, we propose to position our Billboard Manager to provide an optimal transmission rate to enable smooth video playback to a mobile device user connected to
a 3G network. Our work focuses on serving user requests by mobile operators from cached resource managed by Billboard Manager, and transmitting the video files from this pool. The
aim is to reduce the load placed on bandwidth resources of a mobile operator by routing away as much user requests away from the internet for having to search a video and, subsequently, if located, have it transferred back to the user.
An Efficient Distributed Control Law for Load Balancing in Content Delivery N...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Efficient criticality oriented service brokering policy in cloud datacentersIJECEIAES
Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.
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VIRTUAL CACHE & VIRTUAL WAN ACCELERATOR FUNCTION PLACEMENT FOR COST-EFFECTIVE...IJCNCJournal
The algorithm to determine the place where network functions are located and how much capacities of network function flexibly are required is essential for economical NFV (Network Functions Virtualization)based network design. The authors proposed a placement algorithm of virtual routing function and virtual firewall function in the NFV-based network for minimizing the total network cost and developed the effective allocation guidelines for these virtual functions.
Cloud computing is changing the way that video services are offered, enabling elastic and efficient
resource allocation through auto-scaling. In this system, a new framework of cloud workload management for
multimedia delivery services, demand forecast, predictive resource allocation and quality assurance as interdependent
components. Based on the trace analysis of a production VoD system, propose time-series techniques
to predict media resource demand from online monitoring, and determine resource reservations from multiple
data centres. Prediction-Based Resource Allocation algorithm (PBRA) that maximize utilization offered in the
tariffs, while ensuring that required resources are reserved in the cloud. Demand forecasting module, which
predicts the user demand of streaming capacity for each media channel during future time period. Cloud broker is
responsible from the side of the media content provider for both allocating the proper amount of resources in the
cloud, and reserving the time period over which the required resources are allocated. This system has focused on
improving the QoS and maximizing the utilization of resources for a particular time, such as Internet VoD
systems, especially in the context of emerging cloud based services.
Similar to International Journal of Engineering and Science Invention (IJESI) (20)
[IJET-V2I2P11] Authors:Pradeep Landge, Ashish Naware , Pooja Mete, Saif Maniy...
International Journal of Engineering and Science Invention (IJESI)
1. International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org Volume 3 Issue 1ǁ January 2014 ǁ PP.69-71
Optimal Distribution of Video in Cloud
T.Archana1, Mrs. S. Roselin Mary2
1
(Computer Science Department, Anand Institute of Higher Technology, Chennai, Tamil Nadu)
2
(Associate Professor, Computer Science Department, Anand Institute of Higher Technology,
Chennai, Tamil Nadu)
ABSTRACT: Content delivery networks (CDN) use multiple servers in many geographic locations that improve
deliveries of static and streaming content. The high fluctuation of user demands in geographically distributed
regions results in low resource utilizations of CDN. It also adds complexity to the deployment procedures.
Hence in proposed, multiple cloud server data is registered with Video Service Provider (VSP) as they are costeffective, highly scalable and reliable. The Cloud Service Providers (CSP) is registered with Video Service
Providers. A region head is chosen for each location. The user sends request for video, which is received by
their region head. Now the region head chooses the best Cloud server (CS) with minimum load to make it cost
effective and for faster response time. The proposed algorithm is, Dynamic Load Balancing (DDN) algorithm
for faster response time and for handling the changing user demands. It balances the load in the cloud servers
using a data structure, which assigns the job from heavily utilized cloud servers to least utilized cloud servers.
KEYWORDS : Cloud Service Provider, Content distribution Network, Video Service Provider,
I.
INTODUCTION
Traditional Content Distribution Networks (CDNs) such as Akamai and Mirror Image have deployed
tens of thousands of data centers and edge servers to deliver content across the globe. It has become financially
prohibitive for smaller providers to compete on a large scale following the traditional model by building new
data centers. The recent emergence of storage cloud providers such as Amazon S3, Nirvanix and Rackspace has
opened up new opportunities to provision cost-effective CDNs. CDN that is based on storage clouds as a “cloud
CDN”, as opposed to a “traditional CDN” is used. Storage cloud providers charge their customers by their
storage and bandwidth usage following the utility computing model. Storage cost is measured per GB per unit
time and Bandwidth cost is measured per GB transferred. Cloud CDN may take advantage of the competitive
prices offered by different cloud providers and reduce its own expense. However, as the only cost for cloud
CDNs is the bandwidth and storage cost, they are very sensitive to the usage variations.Internet video providers
(e.g., YouTube, Hulu) generally resort to CDN to conduct large-scale video distribution. However, CDN
solutions are inadequate for the emerging video traffic growth. First, due to their semi static resource
provisioning mechanisms, the resource utilization of existing CDNs is extremely low (normally ranging
between 5%-10%), which directly translates into high operational cost. Second, the emerging user generated
video contents are long tail nature, defying the operational principle of serving the popular contents of CDNs.
The emergence of cloud computing opens a new door for designing the next-generation video distribution
platform. Cloud-based services are more cost-effective, highly scalable and reliable. The popularities of videos
to be distributed are dynamic and evolutionary over time. Thus, the deployment of cloud resources is also a
dynamic process. It means that a video service provider should adjust resource provisioning at different regions
proactively and place video contents according to the changes of user demands. It investigates the optimal
deployment of cloud-assisted video distribution services. Therefore, an algorithm is proposed to dynamically
balance the load and to ensure the optimum utilization of cloud services.
II.
RELATED WORKS
[1] The Opportunistic Load Balancing algorithm (OLB) intends to keep each node busy regardless of
the current workload of each node. OLB assigns tasks to available nodes in random order. The Minimum
Completion Time algorithm (MCT) assigns a task to the node that has the expected minimum completion time
of this task over other nodes. The Min-Min scheduling algorithm (MM) adopts the same scheduling approach as
the Minimum Completion Time algorithm (MCT) to assign a task the node that can finish this task with
minimum completion time over other nodes. LB3M achieved minimum completion time and provides better
load balancing compared to LBMM and MM. [2] CMS is used for optimizing the dynamic multi-service load
balancing in cloud based multimedia systems. It further solves the problem by an efficient genetic algorithm
with immigrant scheme, which has been shown to be suitable for dynamic problems. [3] Content Distribution
Distribution (CDNs) uses storage clouds.
www.ijesi.org
69 | Page
2. Optimal Distribution Of Video In Cloud…
The storage cloud providers operate on data centers that can offer Internet-based storage. These
providers charge their customers by their storage and bandwidth usuage following the utility computing model.
Storage cost is measured per GB per unit time and bandwidth cost is measured per GB transferred. It placed web
sever replicas in cloud to minimize the cost and also satisfies the QoS requirements for user requests.
[6] CMLB makes each multimedia service task to obtain the required resources in the shortest time
through load balancing. This paper facilitates the execution of complicated multimedia tasks, as well as supports
specific and stringent multimedia QoS provisioning. It uses effective load balancing approach for cloud-based
multimedia systems (CMLB). [7] used NBS algorithm for resource allocation but it focuses on reducing the cost
more than time. This paper investigates the optimal deployment problem of cloud assisted Video distribution
services and explores the best tradeoff between the operational cost and the user experience. It aims at paving
the way for building the next-generation video cloud. [8] aimed to avoid deadlocks in cloud by the use of virtual
machines (Vm). If a Vm is 100%, then it is fully utilized. It comprises of users requesting for the services of
diverse applications from various distributed virtual servers. [9] ACO uses pheromone table and pheromone
update mechanism is proved as a efficient and effective tool to balance the load but it does not consider the
fault tolerance issues. This paper deals with the main concern of load balancing in cloud computing. The load
can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load
among various nodes of a distributed system to improve both resource utilization and job response time.
III.
PROPOSED WORK
Design
The following is the block diagram of the overall system design. The user sends the request for the
video. The request is received by their region head which is forwarded to the CSP based on the availability of
the resource and load. The proposed algorithm helps in balancing the load. Additionally CSP uses the cache.
The cache is a buffer which is used to keep the recently retrieved videos. The CSP initially checks its cache for
the requested video. In case of absence, the request is forwarded to the VSP from where the video is retrieved
and sent back to the requested user.
Fig. 1. Overall Design
IV.
ADVANTAGES
Maximum utilization of resources.
Maximum Speed.
No wastage of Resources.
Low network traffic.
V.
PROPOSED ALGORITHM
DDN (Dynamic Distributed Non-pre emptive) Load Balancing Algorithm
[1] STEP : The CSP has sub servers known as cloud server (CS). Initially the status of CS will be zero as all
the CS are available.
[2] STEP : The region head maintains the data structure comprising of Job ID, CS ID, CS status.
[3] STEP : When there is a queue of requests, the region head parses the data structure for allocation to identify
the least utilized CS.
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3. Optimal Distribution Of Video In Cloud…
[4] STEP: If availability of CS is more, then the CS with least hop time is considered.
[5] STEP : The region head updates the data structure automatically after allocation.
VI.
IMPLEMENTATION
Allocation of Job
The user registers with the cloud service provider and the information are stored in the database. These
cloud service providers are connected to the video service providers to provide the requested videos. When the
user requests, it is forwarded to the region head which chooses the suitable cloud server.
Load Balancing
The region head maintains a data structure comprising of Job ID, CS ID and CS status. When the
requests arrive, the region head parses the data structure for allocation to identify the least utilized CS. If an
overloaded CS is found, the region head assigns the load of overloaded CS to the under-utilized CS. The region
head updates the data structure by modifying the entries accordingly on a time basis.
Cache management
When another user requests the same video to the cloud service provider in short duration of time, then
it does not communicate directly to the video service provider. Rather it first checks its cache for the requested
video. If present, fetches from its cache, otherwise it requests the VSP.
VII.
CONCLUSION
The new user can register with CSP and request the video. During the allocation of job, the log is
monitored and maintained which helps in load balancing. It also aims in reducing the response time for retrieval
of video using cache management and effectively balancing the load based on the CS status.
REFERENCES
[1]
[2]
[3]
[4]
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Balancing in a Three-level
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