1. Measurement-Based Adaptive Statistical Admission Control Scheme for Video-on-Demand Servers In-Hwan Kim, Jeong-Won Kim, Seung-Won Lee, Ki-Dong Chung Pusan Nat’1 Univ.

2. Why we need Admission Control? zPerformed by an accurate grasp of the condition of surplus resources. zProblems yThe disk has irregular response time yAlso it has high deviation of amount demanded zObjectives yMaximum resource utilization yQoS Guarantee

3. Introduction to Admission Control zParameter-based approach yDeterministic admission control yStatistical admission control zMeasurement-based yConsider with network bandwidth

4. Proposed Scheme zAdaptive statistical admission control scheme yoff-line xdemand blocks per round xaverage demand block yon-line x# of blocks can be a adequately serviced xnot to exceed this the of blocks, it limit the user

5. Off-line process zSize of each frame zConvert size to # of blocks per round z30 frames by 4KB zEstimated statistic max and min # of demanded blocks yuser arrive: poisson distribution yuser access: Zipf distribution zDisk sub-system ability (# of blocks per round)

6. On-Line Process zL >= M + E xL: Set by QoS, max # of demanded blocks corespondent to overflow rate xM: average # of demanded blocks xE: tolerated bulk block size

7. The demand blocks per round and the probability of block requests according to the users zService round is 1 second,

8. Overflow probability evaluator

9. Cumulated overflow probability graph according to demand blocks

10. The determination of the E n average demand block peak blocks zA value between the average demand block and the peak blocks. Large E n makes less overflows. QoS guarantees to users an efficient usage of resources. zTradeoff between QoS guarantees to users and an efficient usage of resources. zAdaptive admission control for the unpredictable disk response time through the variable E n.

11. Overall admission control algo. zSet QoS (L) zAllowed_Overflow = 1-QoS zIs L>=M+E ? zIs Allowed_Overflow >= Overflow_Rate_After_Latest_Admit ?

12. Experiment Environment

13. The parameters of video data

14. Fluctuation of demand blocks and the average block per round QoS level 60%, overflow probability not to exceed 0.4 is 2525 blocks

15. Fluctuation of E n on demand blocks per round

16. Control of overflow rate

17. Conclusions zProposed scheme could make the video server guarantee the server-initiated QoS and admit users within a level of making best use of the system resources. zFuture work ySeek the optimal E n yIntegrate the disk resource and the network one.