Wireless Cache Invalidation Schemes with Link Adaptation and Downlink Traffic

Presented by Ying Jin

Outline• Background• System model• Three proposed strategies• Simulation Result• Conclusion

Background• Cache invalidation strategy -- IR (invalidation report)

– Server periodically broadcast IR, IR ={(Ti,<dx,tx>|tx>Ti - ωL}– If cache miss, client send uplink request to server– Server collect all requests and broadcast replies once every IR peri

od– 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 t

o ensure consistency.

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.

Background• IR-UIR :

– UIR ={(Ti,<dx,tx>|tx>Ti}, updated IR– Client use UIRs to invalidate cache data– Reduce the long query delay– Little more broadcast overhead

Background• IR+UIR :

– UIR ={(Ti,<dx,tx>|tx>Ti}– 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

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)

System model• Uplink:

– Request– Information– Pilot

• Downlink:– Acknowledgement– Polling– Information– Announcement

Frame duration: 2.5ms

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.

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: rspeech Kbps• Data: rfile Kbps, exponentially distributed request mean arrival tim

e Tq

• Tu: mean data update time, exponential distribution • Pu: probability of updating hot data iterm• Each server has consistent view of DB, broadcast same set of I

R+UIR• Broadcast scheduler: determine transmission rate for broadcast

Proposed methods 1• Reducing the Probability of Corruption i

n IR

• Time interval: L seconds

• # of UIR: m-1

• IR => {IRi, i= 1,2,… ω}, – IRi = {(dx,tx)| Ti- j*L < tx≤ (Ti- (j-1)*L}– each segment IRi separately transmitted– For example, IR at Ti <= IR at Ti-3, IR1, I

R2 at Ti

• Reduce both the corruption probability and power consumption

– (1-Pe)(SωL +x) < (1-Pe)(SL+x) , (Pe bit error rate, SL size of an IR segment, Sω

L size of IR)

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 p

rotection)

• Two type users: – Active user: latest IR segments– long time disconnected user: old IR segments

• Broadcast scheduler: – using average data rate (by collecting CSIs)

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

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

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 )

Tu= 100s; Tq = 100s

Simulation results• Effect of Query Ge

neration Time– Tq increase => query delay in

crease– Divide-IR not very effective– UIR-IR perform better

– Tq increase => uplink request increase

– Ideal IR request fewer uplink request

– Tq increase => delay in other downlink traffic decrease

# of client= 50; Tu= 100s

Simulation results• Effect of Update Ar

rival Time– Larger Tu => small delay– Divide-IR improve significantl

y for aggressive broadcast– UIR-reply outperform Divide-I

R at high update rate– Uplink request decreases wit

h increasing update time

# of client= 50; Tq = 100s

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; Tu= 100s; Tq = 100s

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; Tu= 100s; Tq = 100s

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; Tu= 100s; Tq = 100s

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

Thank you