This document provides an overview of the syllabus for a course on cellular networks and mobile computing. The key topics covered include: - Mobile application development for iOS and Android platforms. - System support for optimizing mobile apps, including power models, energy profiling, and operating system features like virtualization and storage. - Interactions between mobile apps and cellular networks, including profiling radio resource usage and characterizing cellular network traffic. - Interactions between mobile apps and cloud services like push notifications, storage, and messaging platforms. - Security and privacy issues for mobile platforms, including malware detection, data/location privacy attacks and defenses.

1. Cellular Networks and Mobile
Computing
COMS 6998-11, Fall 2012
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2. Syllabus
• Mobile App Development (lecture 2,3)
– Mobile operating systems: iOS and Android
– Development environments: Xcode, Eclipse with Android SDK
– Programming: Objective-C and android programming
• System Support for Mobile App Optimization (lecture 4,7)
– Mobile device power models, energy profiling and ebug debugging
– Core OS topics: virtualization, storage and OS support for power and context management
• Interaction with Cellular Networks (lecture 1,5, 8)
– Basics of 3G/LTE cellular networks
– Mobile application cellular radio resource usage profiling
– Measurement-based cellular network and traffic characterization
• Interaction with the Cloud (lecture 6,9)
– Mobile cloud computing platform services: push notification, iCloud and Google Cloud Messaging
– Mobile cloud computing architecture and programming models
• Mobile Platform Security and Privacy (lecture 10,11,12)
– Mobile platform security: malware detection and characterization, attacks and defenses
– Mobile data and location privacy: attacks, monitoring tools and defenses
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3. Mobile App Development: iOS
• iOS Overview
• Objective C
• Xcode
• Model-View-Controller
• Blocks and Multithreading
• Core Data and Location
• iCloud
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4. Mobile App Development:
Android
• Android OS Overview
• Eclipse and Android SDK
• Application Framework
– Activity, content provider, broadcast receiver,
intent
• Networking
• Google Cloud Messaging (GCM)
4

5. System Support for Mobile App
Optimization
• Mobile device power models, energy profiler
and ebug debugging
• Core OS topics:
– Virtualization
– Storage
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6. System Calls As Power Triggers
Key observation: System call is the interface through which an
application communicates with the underlying system
(hardware) and outside world (Internet, GPS, etc.)
Key Idea: Use System Calls as triggers in power modeling
Advantages:
– Encapsulates utilization based triggers
• Parameters of system calls
– Captures power behavior of ones that do not
necessarily imply utilization
– Can be traced back to process, thread, function
• Eases energy accounting
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7. Finite-State-Machine (FSM)
as Power Model Representation
Use Finite-State-Machine (FSM)
•Nodes: Power states
– Base State: No activity on phone
– Productive state: Actual utilization
– Tail state: No-useful work
•Edges: Transition rules
– System calls (start/completion)
– Workload (Ex: 50 pkts/sec)
– Timeout
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State
1 State
2
Transitions
State
3

8. Virtualization: Device Namespace
Linux
Kernel
VP 1 VP 2 VP 3
Power
WiFi
Cell Radio
Framebuffer
GPU
RTC / Alarms
•••
Sensors
Input
Android...
Audio/Video
•••
safely,
correctly
multiplex
access to
devices
device namespaces
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9. How Apps Use Storage?
• Exactly what makes web browsing slow on Android?
– Key lies in understanding how apps use SQLite and FS interface
/
data/data/com.necla.webvi
ew
lib (empty)
cache
webviewCac
he
6aaa3f00, 03051d8d,
…
databases many files (5.5MB)
webview.db (14KB)
webviewCache.db
(129KB)
These files written
to SQLite in sync
These files written to
FS in write-behind
WebBench
Storage Schema
 Apps typically store some data in FS (e.g., cache files)
and some in a SQLite database (e.g., cache map)
– All data through SQLite is written synchronously  slow!
– Apps often use SQLite oblivious to performance effects
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10. Interaction with Cellular Networks
• Basics of 3G/LTE cellular networks
• Impact of radio access network on mobile
apps
– Radio resource usage profiling (ARO)
• Impact of cellular network core on mobile
applications
– In-depth study of middleboxes in cellular networks
– Cellular network architecture characterization and
Implication to CDN
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11. eNodeB 1
Cellular Core Network
eNodeB 3 S-GW 2
P-GW
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S-GW 1
eNodeB 2
Internet and
Other IP Networks
GTP Tunnels
UE 1
UE 2
LTE Infrastructure
MME/PCRF/HSS
• UE: user equipment
• eNodeB: base station
• S-GW: serving
gateway
• P-GW: packet data
network gateway
• MME: mobility
management entity
• HSS: home
subscriber server
• PCRF: policy charging
and rule function

12. 12
LTE Architecture (Cont’d)
• eNodeB, S-GW and P-GW
are involved in
session setup, handoff,
routing
Control Plane
Data Plane
User
Equipme
nt (UE) Gateway
BBaassee SSeerrvviningg PPaacckkeett D Daattaa
(S-GW)
Mobility
Management
Entity
(MME)
Network
Gateway
(P-GW)
Home
Subscriber
Server
(HSS)
Policy Control
and Charging
Rules Function
(PCRF)
Station
(eNodeB)

13. Power Management: LTE
• UE runs radio resource
control (RRC) state
machine
• Two states: IDLE,
CONNECTED
• Discontinuous
reception (DRX):
monitor one subframe
per DRX cylce; receiver
sleeps in other
subframes
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14. Power Management: UMTS
• State promotions have promotion delay
• State demotions incur tail times
Tail Time
Delay: 2s Delay: 1.5s
Tail Time
Channel Radio
Power
IDLE Not
allocated
Almost
zero
CELL_FACH Shared,
Low Speed
Low
CELL_DCH Dedicated,
High Speed
High
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15. Example: RRC State Machine
for a Large Commercial 3G Network
DCH Tail: 5 sec
Promo Delay: 2 Sec
FACH Tail: 12 sec
Tail Time
Waiting inactivity timers to expire
DCH: High Power State (high throughput and power consumption)
FACH: Low Power State (low throughput and power consumption)
IDLE: No radio resource allocated
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16. ARO: Mobile Application Resource
Optimizer
• Motivations:
– Are developers aware of the RRC state machine and its implications on radio
resource / energy? NO.
– Do they need a tool for automatically profiling their prototype applications? YES.
– If we provide that visibility, would developers optimize their applications and
reduce the network impact? Hopefully YES.
• ARO: Mobile Application Resource Optimizer
– Provide visibility of radio resource and energy utilization.
– Benchmark efficiencies of cellular radio resource and battery life for a specific
application
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17. RRC State Machine Inference
• State promotion inference
– Determine one of the two promotion procedures
– P1: IDLEFACHDCH;P2:IDLEDCH
A packet of min bytes never triggers FACHDCH promotion (we use 28B)
A packet of max bytes always triggers FACHDCH promotion (we use 1KB)
• State demotion and inactivity time inference
– See paper for details
P1: IDLEFACH, P2:IDLEDCH
P1: FACHDCH, P2:Keep on DCH
Normal RTT < 300ms
RTT w/ Promo > 1500ms
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18. ARO System Architecture
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