Characterizing Radio Resource Allocation for 3G Networks Feng Qian* Zhaoguang Wang* Alexandre Gerber...

Characterizing Radio Resource Allocation for 3G Networks

Feng Qian* Zhaoguang Wang*Alexandre Gerber† Z. Morley Mao*Subhabrata Sen† Oliver Spatscheck†

* University of Michigan † AT&T Labs Research

Internet Measurement Conference, Nov 1 2010

2 University of MichiganElectrical Engineering and Computer ScienceRobustNet

Introduction Limited Radio resources in cellular networks need to be efficiently

managed/allocated – uses RRC state machine Our work focuses on UMTS (Universal Mobile Telecommunications

System) 3G cellular networks Release of resources controlled by inactivity timers

Timeout value, called Tail Time, causes inefficiencies of energy and radio resources

Tail time can last up to 15 seconds

Allocation of resources triggered by user data transmission activity Resource allocation takes up to several seconds

due to control msg exchange


The RRC State Machine for UMTS Network

State promotions have promotion delay State demotions incur tail times (waste radio resource & energy)

The key tradeoff

Tail Time

Tail Time

Delay: ~1.5sDelay: ~2s

Increase Inactivity Timers

Decrease Inactivity Timers

Decrease promotion delaysLower RAN OverheadBetter user experience

Increase promotion delaysHigher RAN overheadWorse user experience

Increase tail timesWaste radio resourcesWaste handset energy

Decrease tail timesSave radio resourcesSave handset energy


Example: Web Browsing Traffic on HTC TyTn II Smartphone

DCH Tail and FACH tail waste significant radio and energy• 34% of total radio energy• 33% of DCH/FACH channel occupation time

FACH DCH FACH and DCH

Wasted Radio Energy 27% 67% 34%

Wasted Channel Occupation Time 27% 67% 33%


Our Contributions Accurate inference of the RRC state machine

For both state transitions and inactivity timers Characterization of state machine behaviors

Using real UMTS traces Explore the optimal state machine inactivity timer settings

Analysis of multimedia streaming strategies Pandora audio and Youtube Video Traffic pattern impose significant impact on the radio resource

and energy consumption. Data preprocessing scheme for cellular data traces

See paper for details


Outline Accurate inference of the RRC state machine







State Machine Inference State Promotion Inference

Determine one of the two promotion procedures P1: IDLEFACHDCH; P2:IDLEDCH

State demotion and inactivity timer inference See paper for details

A packet of min bytes never triggers FACHDCH promotion (we use 28B)A packet of max bytes always triggers FACHDCH promotion (we use 1KB)

P1: IDLEFACH, P2:IDLEDCHP1: FACHDCH, P2:Keep on DCH

Normal RTT < 300msRTT w/ Promo > 1500ms


RRC State Machines of Two Commercial UMTS Carriers

Carrier 1 Carrier 2Timer Carrier 1 Carrier 2

DCHFACH (α timer) 5 sec 6 sec

FACHIDLE (β timer) 12 sec 4 sec

What are the optimal inactivity timer values?

PromotionInference

Reports P2IDLEDCH

PromotionInference

Reports P1IDLEFACHDCH


State Machine Inference Validation using a power meter

Carrier 1

Promo Delay: 2 SecDCH Tail: 5 secFACH Tail: 12 sec

RRC State Avg Radio Power

IDLE 0

FACH 460 mW

DCH 800 mW

FACHDCH 700 mW

IDLEDCH 550 mW


Cellular Measurement Data

A large TCP header packet trace collected from a large UMTS carrier (Carrier 1) in Jan 2010

278 million TCP packets, 162 GB data Preprocessing: extract Sessions

A session consists of all packets transferred by the same user device (handset).

One handset may have multiple sessions: use a threshold of 60 sec to decide a session has terminated.

A session may consist of multiple TCP flows. Replay the 1 million sessions to an RRC state machine

simulator


State Occupation Time and Tail Times

• DCH Tail Ratio: DCH Tail Time / Total DCH Time• The overall tail ratio for DCH and FACH are 45% and 86% respectively.• More than 70% of the sessions have DCH tail ratios higher than 50%.• The efficiency of FACH is extremely low.

Distribution of DCH and FACH Tail Ratios across all sessions

State Occupation / Transition Time

DCH 44.5% (99.7% user data)

FACH 48.0% (0.3% user data)

IDLEDCH 6.8%

FACHDCH 0.7%

Tail Ratios

DCH Tail 45.3%

FACH Tail 86.1%


State Promotion Overhead

Cumulative promotion overhead

• Promotion Overhead: Promotion Time / Total Session Duration• Usually shorter sessions have higher promotion overhead• Cumulative promotion overhead function y = CP(x)

The overall promotion overhead for all sessions short than x is y

Within the dataset, 57% of the sessions are at most 10 sec, and their average promotion overhead is 57%.


The Impact of Session Size

Session Size vs. State Occupation Time Session Size vs. Tail Ratios

Statistically, large sessions utilize radio resource more efficiently• Fraction of DCH occupation time is higher• Tail Ratio is lower


What-if Analysis for Inactivity Timers Inactivity timers are the most crucial parameters

affecting UE energy consumption State promotion overhead Radio resource utilization (i.e., DCH occupation time)

What is the impact of changing inactivity timers Perform what-if analysis by replaying traces to the simulator

with different inactivity timer values.


What-if Analysis for Inactivity Timers

• The α (DCHFACH) timer imposes much higher impact on the three metrics than the β(FACHIDLE) timer does.

• Very small α timer values (< 2 sec) cause significant increase of state promotion overhead.

• It is difficult to well balance the tradeoff. The fundamental reason is that timers are globally and statically set to constant values.

Fix the α (DCHFACH) timer Change the β(FACHIDLE) timer

Fix the β(FACHIDLE) timer Change the α (DCHFACH) timer

Relative Change of…

ΔE Radio Energy

Δ S Promotion Overhead

Δ D DCH Occupation Time





Analysis of multimedia streaming strategies Pandora audio and Youtube Video Traffic pattern impose significant impact on the radio

resource and energy consumption. Data preprocessing scheme for cellular data traces



Streaming Traffic Pattern

Pandora audio streaming Collect a 30-min trace by listening to

seven tracks (songs) using an Android G2 phone of Carrier 2

Bursty traffic Pattern Pro: each burst utilizes maximal bandwidth Con: each short burst incurs tail time

High overhead incurred by tails50% of DCH time and 59% of radio energy are wasted on tails


Streaming Traffic Pattern YouTube video streaming

Collect a 10-min YouTube trace using Android G2 of Carrier 2.

Traffic patternFirst 10 sec: maximal bw is utilizedNext 30 sec: constant bitrate of 400kbpsRemaining: transmit intermittently withthe inter-burst time between 3~5 s.

Under-utilization ofnetwork bandwidth causes itslong DCH occupation time. Energy/radio resource efficiency is

much worse than Pandora


Improve Resource Efficiency of YouTube Proposed traffic pattern: Chunk Mode

The video content is split into n chunks C1, …, Cn

Each transmitted at the highest bit rate. n should not be too small as users often do not watch the

entire video Para Meaning

M Maximal BW

L Content size

TSSSlow start duration

LSSBytes transferred in slow start

How to eliminate the Tail for each chunk?Using Fast Dormancy


Fast Dormancy A new feature added in 3GPP Release 7 When finishing transferring the data, a handset sends a

special RRC message to RAN The RAN immediately releases the RRC connection and lets

the handset go to IDLE Fast Dormancy dramatically

reduces the tail time, saving radio resources and battery life

Fast Dormancy has been supported in some devices (e.g., Google Nexus One) in application-agnostic manner

----- Without FD----- With FD (Illustration)


Use Fast Dormancy to Enhance Chunk Mode Invoke fast dormancy at the end of each chunk

To immediately release radio resources (assuming no concurrent network activity exists)

In general, however, aggressively invoking fast dormancy may increase the state promotion overhead

Relative Change of…

ΔE Radio Energy

Δ D DCH Occupation Time

Chunk Mode: Save 80% of DCH occupation time and radio energy for YouTubeFast Dormancy: Keep ΔD and ΔE almost constant regardless of # of chunks.


Related Work Tune inactivity timers to balance the tradeoff [Lee04] [Yeh09]

Using analytical model instead of real cellular traffic Optimize radio energy utilization by shaping traffic

pattern [Balasubramanian09][Aaron10] For delay-tolerant apps (e.g., email, RSS), delay their transfers

Leverage fast dormancy to reduce tail time [Qian10] Apps predict the inter-transfer time (ITT), the idle period after

each data transfer Invoke fast dormancy if ITT is long enough Use a robust coordination algorithm to handle concurrent

network activities


Conclusion The RRC state machine guides the radio resource allocation

policy in 3G UMTS network State promotions may cause considerable performance

inefficiency due to their long delays Tail times may cause significant resource utilization

inefficiency due to the tail effects The above two factors form the key tradeoff.

The tradeoff is hard to balance, as timers are globally and statically set, hard to adapt to the diversity of traffic patterns.

Two approaches to address the problem Apps alter traffic patterns based on the state machine behavior Apps cooperate with network in allocating radio resource

Thank You!

Q&A


State Promotion Tens of control messages are exchanged during a state promotion.

RRC connection setup: ~ 1sec Radio Bearer Setup: ~ 1 sec+Figure source: HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications. John Wiley and Sons, Inc., 2006.

Characterizing Radio Resource Allocation for 3G Networks Feng Qian* Zhaoguang Wang* Alexandre Gerber...

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