1. Wireless Cache Invalidation Schemes with Link Adaptation and Downlink Traffic Presented by Ying Jin

2. Outline Background System model Three proposed strategies Simulation Result Conclusion

3. Background Cache invalidation strategy -- IR (invalidation report) –Server periodically broadcast IR, IR ={(T i, |t x >T i - ωL} –If cache miss, client send uplink request to server –Server collect all requests and broadcast replies once every IR period –To answer a particular query, a client is required to wait for the next IR to determine whether its cache is valid or not. Advantages: –high scalability –energy efficiency Drawbacks: –clients must flush their entire caches after long disconnection (ωL), even if some of the cached items may still be valid; –clients must at least wait for the next IR before answering a query to ensure consistency.

4. Background Cache invalidation strategy -- IR (invalidation report) –To answer a particular query, a client is required to wait for the next IR to determine whether its cache is valid or not.

5. Background IR-UIR : –UIR ={(T i, |t x >T i }, updated IR –Client use UIRs to invalidate cache data –Reduce the long query delay –Little more broadcast overhead

6. Background IR+UIR : –UIR ={(T i, |t x >T i } –Reduce the long query delay –Little more broadcast overhead Assumptions: –broadcast channel is error-free, –no other downlink traffic. Objective –Study performance of IR, IR-UIR on realistic system model –Effect of broadcast overhead on other downlink traffic –Three new schemes

7. Some concepts Fast fading: fluctuating in a very fast manner (caused by multi-path signals interfering with each other) Long-term fading: fluctuating in relatively slower manner (due to distance and terrain effects) Coherence time: time duration of the radiation maintains a near-constant phase relationship Channel State Information (CSI) : channel condition (fading attenuation)

8. System model Uplink: –Request –Information –Pilot Downlink: –Acknowledgement –Polling –Information –Announcement Frame duration: 2.5ms

9. System model System model with an adaptive physical layer Two signal propagation components: –fast fading component –long-term shadowing component Transmission mode –mode 0 to mode 5 (Low rate to high rate) Assumption: –mobility of the users < 5km/hr (pedestrian speed) –channel fading experienced by each mobile device is independent of one another.

10. Proposed methods Targets: –reducing the probability of corruption in IRs, –improving the broadcast channel utilization, –reducing the average delay in other downlink traffic. Notation –User: Voice: r speech Kbps Data: r file Kbps, exponentially distributed request mean arrival time T q T u : mean data update time, exponential distribution P u : probability of updating hot data iterm Each server has consistent view of DB, broadcast same set of IR+UIR Broadcast scheduler: determine transmission rate for broadcast

11. Proposed methods 1 Reducing the Probability of Corruption in IR Time interval: L seconds # of UIR: m-1 IR => {IR i, i= 1,2,… ω}, –IR i = {(dx,tx)| T i - j*L < tx≤ (T i - (j-1)*L} –each segment IR i separately transmitted –For example, IR at T i <= IR at T i-3, IR 1, IR 2 at T i Reduce both the corruption probability and power consumption –(1-P e ) (SωL +x) < (1-P e ) (SL+x), (P e bit error rate, S L size of an IR segment, S ωL size of IR)

12. Proposed methods 2 Improving Channel Utilization Optimal transmission rate –current channel status of all clients –importance of the information being delivered –more important information: low-rate broadcast (higher level of error protection) –less important information: high-rate broadcast (lower level of error protection) Two type users: –Active user: latest IR segments –long time disconnected user: old IR segments Broadcast scheduler: –using average data rate (by collecting CSIs)

13. Proposed methods 3 Reducing the Average Delay in Other Downlink Traffic IR based scheme => block other downlink traffic –Server collect all requests over the IR time period, and broadcast after IR –size of each IR is very large –long list of reply Server broadcasts query replies after both IRs and UIRs –Reduce block in other downlink traffic –Reduce query delay Tradeoff between aggregate effect

14. Simulation results Model Parameters Transmission mode –0-5 low-> high Three Metrics –Average query delay –# of uplink request per successful query –Average delay of other downlink traffic

15. Simulation results Effect of number of clients –# of client increase => query delay decrease –IR-UIR worse than IR on aggressive broadcast –Divide-IR outperforms significantly on aggressive broadcast –UIR-reply on normal broadcast better than ideal IR-UIR –More cache hits => decrease uplink request –IR better than IR+UIR in uplink request? –Conservative broadcast achieves the least transmission error, its impact on other traffic is largest. (because long broadcast time ) T u = 100s; T q = 100s

16. Simulation results Effect of Query Generation Time –T q increase => query delay increase –Divide-IR not very effective –UIR-IR perform better –T q increase => uplink request increase –Ideal IR request fewer uplink request –T q increase => delay in other downlink traffic decrease # of client= 50; T u = 100s

17. Simulation results Effect of Update Arrival Time –Larger T u => small delay –Divide-IR improve significantly for aggressive broadcast –UIR-reply outperform Divide- IR at high update rate –Uplink request decreases with increasing update time # of client= 50; T q = 100s

18. Simulation results Effect of Number of UIR –More UIR =>smaller delay, larger overhead –Optimal UIR=5 –Divide-IR improves with UIR –Uplink cost start to converge from UIR=5 –UIR overheads => increase delay in other downlink traffic # of client= 50; T u = 100s; T q = 100s

19. Simulation results Effect of Access Skew –Hot data access probability –Divide-IR shows large improvement on aggressive broadcast –Cache hit => Access skew largely affect uplink cost –Delay in other downlink is comparatively not affected? # of client= 50; T u = 100s; T q = 100s

20. Simulation Results Effect of Disconnection Time –Short Disconnection period, no big difference (fig. a) –Flush the whole cache => Significant increase in the number of uplink request (fig. d) –Little improvement in UIR-reply (fig. b) –Long disconnection => decrease query rate (fig. e) # of client= 50; T u = 100s; T q = 100s

21. Conclusion Assumptions on IR-based cache invalidation strategies –Error-free broadcast –No other downlink traffic Three new schemes –Divide-IR –Adaptive broadcast transmission –UIR-reply Simulation result Contributions –Estimate the performance of IR, IR-UIR on a realistic environment –Take into account the transmission error and other downlink traffic

22. Thank you