Wireless Cache Invalidation Schemes with Link Adaptation and Downlink Traffic
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Transcript of Wireless Cache Invalidation Schemes with Link Adaptation and Downlink Traffic
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 ti
me 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 transmitte
d– 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,
SωL 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
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)
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 Generation Time
– Tq increase => query delay increase
– 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 Arrival Time
– Larger Tu => 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; 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