1 / 17

Mobility Aware Server Selection for Mobile Streaming Multimedia CDN

Mobility Aware Server Selection for Mobile Streaming Multimedia CDN. Muhammad Mukarram Bin Tariq , Ravi Jain, Toshiro Kawahara {tariq , jain, kawahara}@docomolabs-usa.com DoCoMo USA Labs. September 29, 2003. Summary.

flower
Download Presentation

Mobility Aware Server Selection for Mobile Streaming Multimedia CDN

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Mobility Aware Server Selection for Mobile Streaming Multimedia CDN Muhammad Mukarram Bin Tariq, Ravi Jain, Toshiro Kawahara {tariq, jain, kawahara}@docomolabs-usa.com DoCoMo USA Labs. September 29, 2003

  2. Summary • We present a mobility-aware server selection scheme for content distribution networks. • Our target is CDN with high density of servers, each server having a small coverage area. Mobile users can move out of such service areas in the duration of streaming media sessions, resulting potentially degraded QoS. • Server Handoff can be performed to revive QoS, but it is expensive. • We use user’s mobility along with traditional criteria such as proximity, server load etc., and assigns a server such that the probability of user moving out of coverage area of the assigned server is reduced, while meeting QoS criteria. • Simulation results show up to 18 % reduction in number of server handoffs.

  3. Outline • Summary • Introduction • Overview • Problem Statement • Mobility Aware Server Selection • Assumed CDN topology • Gathering user mobility information and estimating residence time with servers. • Server Selection. • Simulation • Mobility, Server Selection, Content Distribution. • Results • Conclusions

  4. Introduction • Multimedia has increasing share in overall traffic • Fixed broadband has not harnessed Multimedia, how will mobile broadband? • Mobile phones are there all the time. • Usage scenarios: movies, songs, news, playing video games etc, while traveling • CDN must meet the challenge of mobility, wireless and streaming media trio. • Our focus is the (Mobility + Streaming). Market Size (traffic) Multimedia 70-80% Voice 20-30% 2005 2010 Year Expected Future mobile communication market [Yumiba01]

  5. Streaming Media In Mobile Networks • In previous work [Tariq02] we showed that server handoff is helpful for streaming content to mobile users. • Localizes traffic, reduces delay, jitter, and load on the network. Server Handoff R Server Handoff R R R Server Server Server R R R R R R Logically Non Adjacent Subnets, (hot spots) Subnets in a Mobile Network

  6. Naïve server handoff scheme has problems • If the users move too fast, there would be too many server handoffs, which are expensive for the network. • Signaling, Content Placement • Our mobility-aware server selection assigns right users to right servers, reducing the need for handoffs. • Reduce Number of Handoffs while meeting QoS criteria.

  7. CDN Topology Tier 3 Server Coverage Area More Coverage Area • Allows: • Maximization of traffic localization • Obtain desired QoS ↔ Number of Handoffs tradeoff by choosing appropriate server tier. Better QoS Servers Tier 2 Server Coverage Area Tier 1 Server Coverage Area aka. server-zoneEach has a RR Access Network Subnets

  8. Server Selection Process • Move to higher tier if • Server Capacity Available • User is Moving Fast • QoS Diff is maintained • Move to lower tier if • Server Capacity Available • Won’t increase handoffs • QoS Diff is maintained Server Tiers Server Capacity Information We introduce a Lazy Mode where we do not move users to lower tiers unless higher tiers are saturated!!! RR Mobility Information

  9. Mobility Information Client maintains its average subnet residence time over k recent moves Trajectory of the client A subnet Mean residence time of all n clients in RR’s server-zone Residence Time Client i Mean Server Residence-time for each tier t RR uses the information to estimate a future residence time of client i with tier t We can make a high granularity estimate using subnet specific information, at cost of higher overhead.

  10. Simulation • Simulate realistic user movement in a large geographical area, collect movement events – we wrote a custom simulator for this. • Simulate different server selection algorithms • Baseline, clients always assigned to default tier • Eager mode with both Low and High Granularity Mobility Information • Lazy Mode with both Low and High Granularity Mobility Information. • See how we did in terms of delay and jitter experience by the users. Mobility Simulation Server Selection Simulation Content Distribution Simulation

  11. Mobility Simulation • Custom simulator to simulate realistic urban area user movement. • Cars, Trains, Streets, Freeways, Public Transport, Congestion, etc. • San Francisco Bay area, 3575 sq. miles. • Over laid with 189 base-stations, 59 subnets

  12. Simulation Parameters • CDN topology • 34 servers arranged in 3 tiers, 21, 8 and 4 in tiers 1, 2, and 3 respectively. • The 3 tiers at 80ms, 160ms and 240ms respectively, from the edge. • Server Capacity, variable {50, 75, 100, 200, 300} simultaneous sessions • Users • 2500 users with three QoS class, {1, 2, 3}, users distributed across the three QoS classes proportionately to the number of servers at corresponding tier. • Session Durations, variable {50, 100, 200, 1000, 1500} seconds • Data rate per user: 64kbps, 20pps • Selection Criteria • Desired QoS Separation between adjacent classes: 20 ms. • Server Overload threshold for Lazy mode. 10% of the maximum reported load allowance.

  13. Simulation Results (1/2) Eager Mode Lazy Mode Low Granularity High Granularity Results for server capacity = 300 Results for server capacity = 100 More results in the paper…

  14. Simulation Results (2/2) • Desired Separation is maintained in all scenarios • Eager mode is achievesbetter convergence and at lower overall value. • Higher server capacityallows us to do more. • Accuracy of estimationhas little impact. QoS Class 1 QoS Class 2 QoS Class 3 Lazy Mode Eager Mode

  15. Conclusions • We have presented a mobility aware server selection scheme. • Up to 18% reduction in number of server handoffs. • Simple, Largely stateless • Relies on simple and manageable information – much of which is already available in the network. • If you are eager, you better be sure – with eager mode, higher accuracy is crucial. • Has applications beyond streaming media. • Anywhere that you want to make tradeoff with mobility by switching to wider-area systems. • Open issues: • Improving while maintaining simplicity. • Manageability. • Bundling with other technologies.

  16. Algorithm Details Task: Assign server to a client i of QoS class q and current/default server tier t selectedTier := t Find load allowance of next higher tier Lt+1 If the client is in fastest Lt+1 – true if is less than Lt+1 here Uj number of sessions of a client j If the delay separation will be maintained – true if . similarly for q,q-1 Assign Server Tier t+1. End-If Else-If Eager Mode or (Lazy Mode with Lt+1 too low) – checking to see if we can move it to lower tier instead Make sure client won’t increase the number of handoffs i.e., Assign Server Tier t-1. End-If

  17. IWCW 2003Conference Report Muhammad Mukarram Bin Tariq DoCoMo USA Labs. October 8, 2003

More Related