Distributed Servers Architecture for Networked Video Services S. H. Gary Chan, Member IEEE, and Fouad Tobagi, Fellow IEEE

Contents  Introduction  System Architecture  Caching schemes for video-on-demand services  Analytical Model  Results  Conclusion

Introduction (1)  In a VoD system, the central server has limited streaming capacities  Distributed servers architecture  Hierarchy of servers  Local servers cache the videos  Some servers are placed close to the user cluster  Determine which movie and how much video data should be stored to minimize the total cost

Introduction (2)  Three models of distributed servers architecture  Uncooperative - unicast  Uncooperative - multicast  Cooperative - communicating servers  This paper studied a number of caching schemes, all employing circular buffers and partial caching  All requests arriving during the cache window duration are served from the local cache  Claim that using partial caching on temporary storage can lower the system cost by an order of magnitude

System Cost (1)  Cost associated with storing a movie in a local server  How much and how long the storage is used  For example, 1-GB disk costing about $200  Disk to be amortized one year  Cost = $200/(365 X 6) = 0.091/h per GB  Streaming rate = 5 Mb/s  One minute of video data ( GB) costs:  0.091/h X = $5.71 X /min

System Cost (2)  Cost associated with streaming a movie from the central server  Range from low (Internet) to quite high (satellite channels)  γ = ratio of storage cost to network  small γ -> relatively cheap storage  Tradeoff network cost versus storage cost 100-min movie cost $3

Video popularity  Movies have notion of skewness  High demand movies should be cached locally  Low demand serviced directly  Intermediate class should be partially cached  Geometric video popularity  r/m mean r% requests ask for m% of the movies  500 movies  4000 requests/h

Caching Scheme (1)  Unicast Delivery  A new arrival for a video opens a network channel of T h minutes to stream the video from the central server  Local server caches W minutes of data with circular buffer  All requests within a window size (W) form a group  Arrivals more than W minutes start a new stream

Caching Scheme (2)  Multicast Delivery  Pre-storing: local server stores permanently the leading portion of each movie  Leader size: W minutes  W minutes of video data served by the local server  T h -W multicast from the central server

Caching Scheme (3)  Multicast Delivery  Pre-caching: local server decides if it should cache a multicast video or not  Two schemes depending on the multicast schedule  Periodic multicasting with pre-caching  Request-driven pre-caching

Caching Scheme (4)  Movie is multicast at regular intervals of W minutes  Local server pre-caches W minutes’ video data  Multicast stream will be held for T h minutes or aborted at the end of W minutes  The start of multicast streams is driven by request arrival  Request arriving within W minutes can be served by the local server Periodic multicasting with pre-caching Request-driven pre-caching

Caching Scheme (5)  Communicating Servers (“server chain”)  Video data can be transferred from one server to another using the server network  LS1 receives data from the central server using unicast stream  LS1 buffers W minutes’ video data  Requests at LS2 arriving within W are served by LS1  Chain is broken when two buffer allocations are separated by more than W minutes

Scheme Analysis  Movie length T h min  Streaming rate b 0 MB/min  Request process is Poisson  Interested in  Average number of network channels,  Average buffer size,  Total system cost:

Analysis - Unicast  Interarrival time = W + 1/λ  By Little’s formula:  Average number of buffers allocated: 1/(W+1/λ))T h which yields  System Cost:  To minimize, either cache or don’t  λ<γ B = W = 0  λ>γ B = T h

Analysis - Multicast  Pre-storing  Buffer size depends on whether any request arrives within a multicast interval Probability of an arrival within W

Analysis - Multicast  Pre-caching (Periodic multicasting with pre- caching)  Pre-caching (Request-driven precaching) Probability of stream not started by the server

Analysis – Communicating Servers  Communicating Servers  If there are many local servers, a subsequent request likely comes from a different server  Average number of concurrent requests is λT h Average interval between successive channel allocation Average time data is kept in the server network Average requests’ inter-arrival time given that the successive requests are within W

Results - Unicast  For unicast, tradeoff between S and B given λ is linear with slope (-λ)  Optimal caching strategy is all or nothing  Determining factors for caching a movie SkewnessSkewness Cheapness of storageCheapness of storage

Results - Multicast with Pre-storing  There is an optimal W to minimize cost  The storage component of this curve becomes steeper as γ increases = 5 req/min, N s = 20,  =0.002/min, T h = 90 min

Results - Multicast N s = 20,  =0.002/min, T h = 90 min Pre-storing Pre-caching

Results – Communicating Servers  Low arrival rate, large W to form a chain  The higher the request rate, the easier it is to chain  =0.002/min, T h = 90 min

Results  Multicast achieves cost reduction within a certain window of arrival rate  Chaining shouldn’t be higher cost than other systems unless local communication costs are very high  =0.002/min, T h = 90 min

System Using Batching and Multicast  Requests arriving within a period of time are grouped together  Batching allows fewer multicast streams to be used, thus lowering the associated cost  Users will tolerate some delay, D max

Results – Request Batching  N s  is low enough, distributed servers based on unicast delivery can achieve lower cost  Combined system:  batching  > ’ => local server D max = 6 min, T h = 90 min ’

Results  =0.002/min, T h = 90 min  Unicast and Combined scheme require similar cost  Multicast can further reduce the cost but not significant  Chaining achieves the greatest saving in cost

Conclusion  Distributed servers architecture to provide video- on-demand service  Different local caching for video streaming  Given certain cost function, determine  which video and how much video data should be cached  More skewed the video popularity is, the more saving a distributed servers architecture can achieve