Optimizing Cloud Resources for Delivering IPTV Services through Virtualization(2012)

ABSTRACT

Virtualized cloud-based services can take advantage of statistical multiplexing across applications to yield significant cost savings to the operator. However, achieving similar benefits with real-time services can be a challenge. we seek to lower a provider’s costs of real-time IPTV services through a virtualized IPTV architecture and through intelligent time shifting of service delivery. We take advantage of the differences in the deadlines associated with Live TV versus Video-on-Demand to effectively multiplex these services. We provide a generalized framework for computing the amount of resources needed to support multiple services, without missing the deadline for any service. We construct the problem as an optimization formulation that uses a generic cost function. We consider multiple forms for the cost function to reflect the different pricing options. The solution to this formulation gives the number of servers needed at different time instants to support these services. We implement a simple mechanism for time-shifting scheduled jobs in a simulator and study the reduction in server load using real traces from an operational IPTV network. Our results show that we are able to reduce the load by ~24%. We also show that there are interesting open problems in designing mechanisms that allow time-shifting of load in such environments.

EXISTING SYSTEM

There exist various tools and technologies for cloud ,  such as cable and Digital television (DTV) is a telecommunication system for broadcasting and receiving moving pictures and sound by means of digital signals, in contrast to analog signals in analog (traditional) TV. It uses digital modulation data, which is digitally compressed and requires decoding by a specially designed television set or a standard receiver with a set-top box.

PROPOSED SYSTEM

Describes a system where a digital television service is delivered using the Internet Protocol over a network infrastructure, which may include delivery by a broadband connection. For residential users, IPTV is often provided in conjunction with Video on Demand and may be bundled with Internet services such as Web access and VoIP. The commercial bundling of IPTV, VoIP and Internet access is referred to as a Triple Play. Adding the mobile voice service leads to the Quadruple Play denomination. IPTV is typically supplied by a broadband operator using a closed network infrastructure. This closed network approach is in competition with the delivery of TV content over the public Internet. This type of delivery is widely called TV over Internet or Internet Television. In businesses, IPTV may be used to deliver television content over corporate LANs and business networks. Perhaps a simpler definition of IPTV would be television content that, instead of being delivered through traditional formats and cabling, is received by the viewer through the technologies used for networks.

Advantages

1.      User easily buys the channel using internet.

2.      User receives the signal from set-top box.

3.      No wires needed.

Disadvantages

1.      More cost and limited usage only.

2.      Some time make a signal problem in unconditional weather.

MODULE DESCRIPTION:

1.      Cloud Computing

2.      Deadline Constraints and Scheduling

3.      User Complaint

4.      Optimization

1. Cloud Computing

Cloud computing is the provision of dynamically scalable and often virtualized resources as a services over the internet Users need not have knowledge of, expertise in, or control over the technology infrastructure in the "cloud" that supports them. Cloud computing represents a major change in how we store information and run applications. Instead of hosting apps and data on an individual desktop computer, everything is hosted in the "cloud"—an assemblage of computers and servers accessed via the Internet.

Cloud computing exhibits the following key characteristics:

Agility improves with users' ability to re-provision technological infrastructure resources.

Cost is claimed to be reduced and in a public cloud delivery model capital expenditure is converted to operational expenditure. This is purported to lower barriers to entry, as infrastructure is typically provided by a third-party and does not need to be purchased for one-time or infrequent intensive computing tasks. Pricing on a utility computing basis is fine-grained with usage-based options and fewer IT skills are required for implementation. The e-FISCAL project's state of the art repository contains several articles looking into cost aspects in more detail, most of them concluding that costs savings depend on the type of activities supported and the type of infrastructure available in-house.

Virtualization technology allows servers and storage devices to be shared and utilization be increased. Applications can be easily migrated from one physical server to another.

Multi tenancy enables sharing of resources and costs across a large pool of users thus allowing for:

Centralization of infrastructure in locations with lower costs (such as real estate, electricity, etc.)

Utilization and efficiency improvements for systems that are often only 10–20% utilized.

Reliability is improved if multiple redundant sites are used, which makes well-designed cloud computing suitable for business continuity and disaster recovery.

Performance is monitored and consistent and loosely coupled architectures are constructed using web services as the system interface.

Security could improve due to centralization of data, increased security-focused resources, etc., but concerns can persist about loss of control over certain sensitive data, and the lack of security for stored kernels. Security is often as good as or better than other traditional systems, in part because providers are able to devote resources to solving security issues that many customers cannot afford. However, the complexity of security is greatly increased when data is distributed over a wider area or greater number of devices and in multi-tenant systems that are being shared by unrelated users. In addition, user access to security audit logs may be difficult or impossible. Private cloud installations are in part motivated by users' desire to retain control over the infrastructure and avoid losing control of information security.

Maintenance of cloud computing applications is easier, because they do not need to be installed on each user's computer and can be accessed from different places.

2. Deadline Constraints and Scheduling

Each channel pack has some deadline constraints and scheduling. The deadline Constraints Provide the limited period of time to the channels pack.  User using the Channels packs within Period. Suppose your channel period time is finished that time Automatically you loss the channel Pack. Also admin provided alert message to user two Days before in channel pack period using Deadline constraints.

There are three types of constraints:

1. Flexible constraints. This is a default type of constraint in project. It means that a task can start As Soon As Possible

2. Semi-flexible constraints. A task must begin or end no later than the defined  date

3. Inflexible constraint. A task must begin or end on a certain date.

3. User Complaint

In this module we give complaint to the complaint box and post the complaint. Then admin view the complaint then take the action to that complaint. Finally users view that complaint status.

4. Optimization

In the module user select the cheap and best channel pack. In this project optimization there are three methods.

Linear Cost Function

Cost function in which the graph of total costs versus a single cost driver forms a straight line within the relevant range

Piecewise Linear Convex Cost Function

Scheduling of the incoming requests which uses yi server

Resources a time i. Suppose that we only serve min (yi,K) of the requests and drop the remaining. The cost of using server’s yi at time i is given by total number of requests + c times the number of dropped requests. We know that the earliest deadline first strategy minimizes the number of dropped requests and hence the optimal strategy for the cost is as follows. Suppose that when we use earliest deadline first as the strategy with K as the number of servers, ˆsi be the number of requests served in time i and ˜si is the number of requests dropped (Note that ˜si = 0 if ˆsi < K.). Then si = ˆsi + ˜si is an optimal solution.

Exponential Cost Function

This is a convex optimization problem with integer constraints, and is thus NP hard problem in general. We here provide an achievable solution based on convex primal-dual method.

System Configuration:-

H/W System Configuration:-

Processor               -    Pentium –III

Speed                                -    1.1 Ghz

RAM                                 -    256  MB(min)

Hard Disk                          -   20 GB

Floppy Drive                     -    1.44 MB

Key Board                         -    Standard Windows Keyboard

Mouse                                -    Two or Three Button Mouse

Monitor                              -    SVGA

S/W System Configuration:-

Operating System            :Windows95/98/2000/XP

Application  Server          :   Tomcat5.0/6.X                                                  

Front End                          :   HTML, Java, Jsp

Scripts                                :   JavaScript.

Server side Script             :   Java Server Pages.

Database                            :   Mysql

Database Connectivity      :   JDBC.