periodic transfers in mobile applications: network-wide origin, impact, and optimization feng qian...
DESCRIPTION
Background: Radio Resource Management in Cellular Networks RRC (Radio Resource Control) state machine [3GPP TS ] – State promotions have promotion delay – State demotions incur tail times Tail Time Delay: 1.5s Delay: 2s RRC StateChannel Radio Power IDLENot allocated Almost zero CELL_FACH Shared, Low Speed Low CELL_DCH Dedicated, High Speed High UMTS RRC State Machine for a large US 3G carrier Page 3TRANSCRIPT
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Periodic Transfers in Mobile Applications:Network-wide Origin, Impact, and Optimization
Feng Qian1, Zhaoguang Wang1, Yudong Gao1, Junxian Huang1 Alexandre Gerber2, Z. Morley Mao1, Subhabrata Sen2, Oliver
Spatscheck2
1University of Michigan 2AT&T Labs - ResearchApril 18 2012
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Introduction• Typical testing and optimization in cellular network
• Little focus has been put on their cross-layer interactionsMany mobile applications are not cellular-friendly.
• The key coupling factor: the RRC State Machine– Determine the resource management policy– Similar RRC state machines exist in 2G, 3G, and 4G networks
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RRCState
Machine?
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Background: Radio Resource Management in Cellular Networks
• RRC (Radio Resource Control) state machine [3GPP TS 25.331]
– State promotions have promotion delay– State demotions incur tail times
Tail Time
Tail Time
Delay: 1.5sDelay: 2s RRC State Channel Radio
Power
IDLE Not allocated Almost zero
CELL_FACH Shared, Low Speed Low
CELL_DCH Dedicated, High Speed High
UMTS RRC State Machine for a large US 3G carrier Page 3
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Background: Radio Resource Management in Cellular Networks
Promo Delay2 Sec
DCHTail
5 sec
FACHTail
12 sec
Tail TimeWaiting inactivity timers to expire
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Cellular Periodic Transfers
• What are periodic transfers?– A handset (mobile device) periodically exchanges some
data with a remote server every t seconds.• Why do they occur?
– Keep-alive, periodic polling, periodic measurements…– Easy to program: java.util.Timer.scheduleAtFixedRate()
• Why are they bad in cellular networks?– They are small and short– Each transfer incurs a long tail
We perform the first network-wide study of cellular periodic transfers to understand their• Prevalence• Resource impact• Application semantics• Potential for improving their inefficiency
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Talk Outline: Cellular Periodic Transfers…
• Why are they bad in cellular networks?• How prevalent are they?• Why do they occur in mobile applications?• What are their network-wide resource impact?• How to make them more resource-efficient?
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Talk Outline: Cellular Periodic Transfers…
• Why are they bad in cellular networks?• How prevalent are they?• Why do they occur in mobile applications?• What are their network-wide resource impact?• How to make them more resource-efficient?
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Small Data Transfers: 3G vs. Wi-Fi
• Perform controlled experiments– Download a small HTTP object in 3G and WiFi– Using a power monitor to measure energy consumption
• Radio energy breakdown
Promotion(~2 sec)
DataTransfer
DCH Tail(5 sec)
FACH Tail(12 sec)
3G:
DataTransfer
Wi-Fi Tail(250 ms)Wi-Fi:
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Measurement Results
Object A: 1 KBObject B: 9 KB
Wi-Fi is 140x more efficient than 3G, because• Wi-Fi has a much smaller RTT• Wi-Fi radio power is only half of 3G radio power• Wi-Fi has a much shorter tail time
For transferring A (B) using 3G, 97.0% (94.3%) of radio energy belongs to the tail.
• Small traffic bursts are extremely resource-inefficient in cellular networks
• Root cause: promotion delay the tail time• Transferring them periodically leads to even more
serious resource inefficiencies
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Talk Outline: Cellular Periodic Transfers…
• Why are they bad in cellular networks?• How prevalent are they?• Why do they occur in mobile applications?• What are their network-wide resource impact?• How to make them more resource-efficient?
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Cellular Measurement Dataset
• Packet traces collected from a large U.S. cellular carrier
• Collected at the cellular core network without sampling
• 1.5 billion packets for 1.25 hours in December 2010
• Recorded IP and TCP(UDP) headers and timestamps
• Extracted 2.8 million user sessions
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The Periodicity Detection Algorithm
• Existing approaches: DFT and auto-correlation– Used to estimate RTT (the “clock” of TCP flows)– Not work well in our scenario: periodic transfers have much fewer
samples than RTTs• We proposed a simple heuristics-based approach
– Input: a session; output: periodicities and periodic transfers– Key idea: exhaustively search for repetitions of data transfers spaced by
a fixed time period
Page 12t seconds t seconds
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Measurement Results
• Key algorithm parameter:– θ (minimal repetitions to be observed before declaring a
periodicity)– We use θ = 4 (more details in paper)
• What is the distribution of periodicities?
• A particular value of one-minute dominates the periodicity.
• Likely to be set by developers in an ad-hoc manner.
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Measurement Results (cont.)
• Prevalence of periodic transfers?– A long session: at least one minute– They occur in about 20% of long sessions
• Typical size and duration of a periodic transfer?
They are Small:• 25th / 50th / 75th percentiles:
0.2 KB, 1.1 KB, 1.8KB• 97% of periodic transfers < 10 KB
They are short:• 90% are less than 7 seconds
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Cellular periodic transfers are:• Prevalent: they occur in 20% of long sessions• Small: median size is 1.1 KB• Short: 90% are less than 7 seconds• At least 70% of periodicities are one minute
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Talk Outline: Cellular Periodic Transfers…
• Why are they bad in cellular networks?• How prevalent are they?• Why do they occur in mobile applications?• What are their network-wide resource impact?• How to make them more resource-efficient?
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Understanding Origins ofCellular Periodic Transfers
• Leverage a database generated by the same carrier– Contains IP address content provider mappings– Based on the data collected on the same day / same
location as the packet trace was collected• IPs of 46% of detected periodic transfers have
meaningful content provider names in the database
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IP content provider database184.73.206.70 lt.andomedia.com170.149.173.1 www.nytimes.com212.58.244.69 www.bbc.co.uk
…
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Origins of Cellular Periodic TransfersContent Provider /
Applications% Periodic transfers Remarks
facebook.com 48.4% Keep connection alive for pushing
andomedia.com 15.5% Pandora’s audience measurement
medialytics.com 4.9% User behavior monitoring
DNS 14.1% DNS lookups
Advertisements 3.3% Advertisement update
gmail.com 1.4% Checking emails
pinger.com 1.4% Polling to fetch updates for SMS
(Other) 11.1% e.g., periodically check the weather
(within 46% of all detected periodic transfer instances)
Many periodic transfers are either unnecessary or overly aggressive
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Case Studies
• Facebook– Periodic transfers (60s) used as keep-alive messages– Prevent a TCP connection from being closed by the NAT– For the four largest U.S. cellular carriers, the timeout of
cellular NAT is at least 4.25 minutes [Wang et al, SIGCOMM 2011]
• Pandora (music streaming)– Periodic audience measurement uploaded to andomedia.com every one minute
– Even when Pandora is running in the background
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Talk Outline: Cellular Periodic Transfers…
• Why are they bad in cellular networks?• How prevalent are they?• Why do they occur in mobile applications?• What are their network-wide resource impact?• How to make them more resource-efficient?
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Quantifying the ResourceImpact of Periodic Transfers
• Use our trace-driven RRC state machine simulator with a handset radio power model [Qian etal, Mobisys 11]
• Three metrics of resource consumption– D: radio resource consumption
Quantified by the CELL_DCH occupation time– S: signaling load, quantified by the total promotion delay– E: handset radio energy consumption
Computed using a handset radio power model
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Quantifying the ResourceImpact of Periodic Transfers
• Compute the impact
• Quantify the impact at four study scopes– All sessions in the dataset– All sessions contain periodic transfers– All Facebook sessions– All Pandora sessions
ΔE = (ER – E0) / E0
E0: Radio energy consumption
in original sessions
ER: Radio energy consumption in modified
sessions with periodic transfers removed
ΔE: Radio energy impact of periodic
transfers (the value is negative)
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The Resource Impact of Periodic Transfers
Study ScopeΔV
Traffic Volume
ΔERadio Energy
ΔSSignalingOverhead
ΔDRadio Energy
All sessions 0.4% -7.9% -8.9% -6.5%
Periodic sessions 0.7% -20.4% -25.3% -15.6%
Facebook sessions 1.7% -30.5% -30.4% -30.5%
Pandora sessions 0.5% -28.7% -35.0% -20.5%
• Huge disparity between traffic volume and resource consumption of periodic transfers
• All sessions: ΔE is 20 times of the ΔV• Pandora: ΔE, ΔS, and ΔD are 40 to 70 times higher than ΔV
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• The state-of-art cellular periodic transfers are extremely resource inefficient
• The root cause: tail time and promotion overhead
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Talk Outline: Cellular Periodic Transfers…
• Why are they bad in cellular networks?• How prevalent are they?• Why do they occur in mobile applications?• What are their network-wide resource impact?• How to make them more resource-efficient?
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Optimizing Periodic Transfers
• Periodic transfers are delay-tolerant– Not initiated by user inputs– Applications usually have the flexibility over a time
window in scheduling each transfer• Can existing optimization approaches effectively
reduce their resource impact? – Perform “what-if” analysis for existing opt. techniques– Resource reduction computed as
– Focus on Pandora and Facebook sessions
ΔE = (Eafter_opt – Ebefore_opt) / Ebefore_opt
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What-if Analysis: Increasing Periodicity
• Increasing the periodicity to 5 min: reduce the resource consumption by 22%~ 28%
• Tradeoff between resource saving and application semantics
StudyScope
ΔERadio Energy
ΔSSignalingOverhead
Facebook -30.5% -30.4%
Pandora -28.7% -35.0%
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What-if Analysis: Fast Dormancy
• Fast dormancy [3GPP Release 7 R2-075251]– Handset actively requests a state demotion after data transfer– Controlled by an independent fast dormancy timer
----- Without FD----- With FD
The Fast Dormancy Timer
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What-if Analysis: Fast Dormancy
Fast dormancy on only periodic transfers Fast dormancy on all transfers
• Blindly applying fast dormancy on all traffic is not recommended• Doing that only at the end of periodic transfers is acceptable• Tradeoff between resource saving and signaling overhead
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Summary: Cellular Periodic Transfers…
• Why are they bad in cellular networks? – They are small and short– The tail effect
• How prevalent are they?– 20% of long sessions (> 1min)
• What are their key characteristics?– Small and short– 60 seconds dominates the periodicity
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Summary: Cellular Periodic Transfers…
• Why do they occur in mobile applications?– Keep alive, measurement, polling, advertisement…– Many are too aggressive or even unnecessary
• What are their network-wide resource impact?– Up to 30% for popular apps– Resource impact is 20~70x of bandwidth usage impact
• How to make them more resource-efficient?– Existing techniques are effective, incurring various
tradeoffs
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Research Impact
• Our finding reached Pandora and Facebook
• The ARO (mobile Application Resource Optimizer) tool for profiling smartphone apps
• http://web.eecs.umich.edu/~fengqian/
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Backup Slides
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The Periodicity Detection Algorithm
• Existing approaches: DFT and auto-correlation– Used to estimate RTT (the “clock” of TCP flows)– Not work well in our scenario: periodic transfers have much
fewer samples than RTTs• We proposed a simple heuristics-based approach
– Input: a user session– Output: periodicities and periodic transfers– Assume each periodicity is associated with the same server– Allow multiple periodicities appear in a session
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The Periodicity Detection Algorithm
• For packets of each server IP address1. Discretize timestamps using a slot length of ω2. Search for periodicity of tmin <= t <= tmax slots (2 conditions)3. Identify packets associated with each periodic transfer
• Evaluate by performing manual inspection
Slot lenω sec
A marked slot contains at leastone packet of the target IP
t: the detected periodicity
Cond1: Observe at least θ marked slots spaced by a fixed number of t slots (e.g., θ = 3)
Cond2: no marked slots between periodic seeds
PeriodicSeed 1
PeriodicSeed 2
PeriodicSeed 3
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Optimization Techniques
----- Without FD----- With FD
The Fast Dormancy Timer
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