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

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?

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

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

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

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

…

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

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

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

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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