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:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard Disk -
20 GB
ü Key Board -
Standard Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System : Windows XP
ü Programming Language :
JAVA/J2EE.
ü Java Version :
JDK 1.6 & above.
ü 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.