Optimizing Cloud Resources for Delivering IPTV Services

Through Virtualization

ABSTRACT:

Virtualized cloud-based services can take advantage of statistical multiplexing

across applications to yield significant cost savings. However, achieving similar

savings with real-time services can be a challenge. In this paper, we seek to lower a

provider’s costs for real-time IPTV services through a virtualized IPTV

architecture and through intelligent time-shifting of selected services. Using Live

TV and Video-on-Demand (VoD) as examples, we show that we can take

advantage of the different deadlines associated with each service 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

(e.g., maximum, convex and concave functions) reflecting the cost of providing the

service. 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 (compared to a possible as predicted by

the optimization framework).

EXISTING SYSTEM:

Servers in the VHO serve VoD using unicast, while Live TV is typically multicast

from servers using IP Multicast. When users change channels while watching live

TV, we need to provide additional functionality so that the channel change takes

effect quickly. For each channel change, the user has to join the multicast group

associated with the channel, and wait for enough data to be buffered before the

video is displayed; this can take some time. As a result, there have been many

attempts to support instant channel change by mitigating the user perceived

channel switching latency

DISADVANTAGES OF EXISTING SYSTEM:

More Waiting Time

More Switching latency

Not Cost effective

PROPOSED SYSTEM:

We propose a) To use a cloud computing infrastructure with virtualization to

handle the combined workload of multiple services flexibly and dynamically, b)

To either advance or delay one service when we anticipate a change in the

workload of another service, and c) To provide a general optimization framework

for computing the amount of resources to support multiple services without

missing the deadline for any service.

ADVANTAGES OF PROPOSED SYSTEM:

In this paper, we consider two potential strategies for serving VoD requests. The

first strategy is a postponement based strategy. In this strategy, we assume that

each chunk for VoD has a deadline seconds after the request for that chunk. This

would let the user play the content up to seconds after the request. The second

strategy is an advancement based strategy. In this strategy, we assume that requests

for all chunks in the VoD content are made when the user requests the content.

Since all chunks are requested at the start, the deadline for each chunk is different

with the first chunk having deadline of zero, the second chunk having deadline of

one and so on. With this request pattern, the server can potentially deliver huge

amount of content for the user in the same time instant violating downlink

bandwidth constraint

SYSTEM ARCHITECTURE:

SYSTEM CONFIGURATION:-

HARDWARE CONFIGURATION:-

Processor - Pentium –IV Speed - 1.1 Ghz

RAM - 256 MB(min)

Hard Disk - 20 GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

SOFTWARE CONFIGURATION:-

Operating System : Windows XP

Programming Language : JAVA/J2EE.

Java Version : JDK 1.6 & above.

Database : MYSQL

REFERENCE:

Vaneet Aggarwal, Member, IEEE, Vijay Gopalakrishnan, Member, IEEE, Rittwik

Jana, Member, IEEE, K. K. Ramakrishnan, Fellow, IEEE, and Vinay A.

Vaishampayan, Fellow, IEEE, “Optimizing Cloud Resources for Delivering IPTV

Services Through Virtualization”, IEEE TRANSACTIONS ON

MULTIMEDIA, VOL. 15, NO. 4, JUNE 2013.