IPTS

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. In this paper, we seek to lower a provider’s costs of real-time IPTV services through a virtualized IPTV architecture and through intelligent timeshifting of service delivery. We take advantage of the differences in the deadlines associated with Live TV versus Video-on-Demand (VoD) 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) 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% (compared to a possible _ 31%). We also show that there are interesting open problems in designing mechanisms that allow time-shifting of load in such environments.

Existing System:

In IPTV, Live TV is typically multicast from servers using IP Multicast, with one group per TV channel. Video-on-Demand (VoD) is also supported by the service provider, with

each request being served by a server using a unicast stream.When users change channels while watching live TV, we need to provide additional functionality to 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. With the typical ICC implemented on IPTV systems, the content is delivered at an accelerated rate using a unicast stream from the server. The playout buffer is filled quickly, and thus keeps switching latency small. Once the playout buffer is filled upto the playout point, the set top box reverts back to receiving the multicast stream. ICC adds a demand that is proportional to the number of users concurrently initiating a channel change event. Operational data shows that there is a dramatic burst load placed on servers by correlated channel change requests from consumers. This results in large peaks occurring on every half-hour and hour boundaries and is often significant in terms of both bandwidth and server I/O capacity. Currently, this demand is served by a large number of servers grouped in a data center for serving individual channels, and are scaled up as the number of subscribers increases. However this demand is transient and typically only lasts several seconds, possibly upto a couple of minutes. As a result, majority of the servers dedicated to live TV sit idle outside the burst period

Proposed System:

In this paper, we aim a) to use a cloud computing infrastructure with virtualization to dynamically shift the resources

in real time to handle the ICC workload, b) to be able to anticipate the change in the workload ahead of time and

preload VoD content on STBs, thereby facilitate the shifting of resources from VoD to ICC during the bursts and c) solve a general cost optimization problem formulation without having to meticulously model each and every parameter setting in a data center to facilitate this resource shift. In a virtualized environment, ICC is managed by a set of VMs (typically, a few VMs will be used to serve a popular channel). Other VMs would be created to handle VoD requests. With the ability to spawn VMs quickly [3], we believe we can shift servers (VMs) from VoD to handle the ICC demand in a matter of a few seconds. Note that by being able to predictthe ICC bursts (channel change behavior can be predicted from historic logs as a result of LiveTV show timings.