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Optimizing Cloud Resources for Delivering IPTV Services Through Virtualization



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.