hongwei zhang hzhanghzhang/courses/7290b/lectures/00 - course plan.pdfinternet packet forwarding...
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Sprint US backbone network
Seattle
Atlanta
Chicago
Roachdale
Stockton
San Jose
Anaheim
Fort Worth
Orlando
Kansas City
CheyenneNew York
PennsaukenRelayWash. DC
Tacoma
…
to/from customers
peering
to/from backbone
…
.………
POP: point-of-presence
DS3 (45 Mbps)
OC3 (155 Mbps)
OC12 (622 Mbps)
OC48 (2.4 Gbps)
Q0: Why this (and not other) network topology?
Internet structure: network of networks
Tier 1 ISP
Tier 1 ISP
Tier 1 ISP
NAP
Tier-2 ISPTier-2 ISP
Tier-2 ISP Tier-2 ISP
Tier-2 ISP
localISPlocal
ISPlocalISP
localISP
localISP Tier 3
ISP
localISP
localISP
localISP
Q0’: Why this (and not other) network hierarchy?
Internet packet forwarding
Tier 1 ISP
Tier 1 ISP
Tier 1 ISP
NAP
Tier-2 ISPTier-2 ISP
Tier-2 ISP Tier-2 ISP
Tier-2 ISP
localISPlocal
ISPlocalISP
localISP
localISP Tier 3
ISP
localISP
localISP
localISP
Q1: Why this (and not other) end-to-end path?
Objectives of the course
� Ultimate goal:
� You become an expert in network design and optimization
� Humble course objectives:
� To help students understand the foundation, algorithms, and systems techniques for network design and optimization
� To help students appreciate both classical and emerging network design problems
� To build up students' capability in enhancing the state of the art in computer networking
Suppose we are building the Internet from scratch up:
How to solve different network design and optimization problems (e.g., topology design, routing)
Topics to cover
� Introduction
� Overview of network design
� Notation and illustrations of network design problems
� Technology-specific network modeling
� Foundation
� Modeling of network design problems
� General optimization methods for network design:
� linear programming
� mixed-integer programming
� stochastic heuristic methods
� convex programming
� multi-commodity flow optimization, etc
� Case studies of classical network design problems
� Location and topological design
� Shortest-path routing
� Fairness, network resilience, etc
� Case studies of emerging network design problems (project-based)
� Vehicular networks
� Sensor networks
� Wireless networks
Two major components of the course
� Lecture
� Focus on basic concepts, techniques, and tools
� Project
� Applying the basic concepts, techniques, and tools to real-world
applications, especially in innovative, emerging networking
technologies such as embedded wireless networks as used in
connected vehicles, smart grid, and industrial automation
Textbooks
� Required:
� [R0] Michal Pioro, Deepankar Medhi, Routing, Flow, and Capacity Design in Communication and Computer
Networks, Morgan Kaufmann, 2004.
� Recommended references:
� [R1] Gilbert Held, Inter- and Intra-Vehicle Communications, Auerbach Publications, 2008.
� [R2] Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin, Network Flows: Theory, Algorithms, and
Applications, Prentice Hall, 1993.
� [R3] Anurag Kumar, D. Manjunath, Joy Kuri, Communication Networking: An Analytical Approach, Morgan
Kaufmann, 2004.
� [R4] Dimitri Bertsekas and Robert Gallager, Data Networks (2nd edition), Prentice Hall, 1992.
� [R5] Thomas G. Robertazzi, Computer Networks and Systems: Queueing Theory and Performance Evaluation
(3rd edition), Springer.
� [R6] Raj Jain, The Art of Computer Systems Performance Analysis: Techniques for Experimental Design,
Measurement, Simulation, and Modeling, John Wiley & Sons, Inc., 1991.
� [R7] Sheldon M. Ross, Introduction to Probability Models, 9th edition, Academic Press, 2006.
� [R8] Robert G. Gallager, Discrete Stochastic Processes, Kluwer Academic Publishers, 1996.
Logistics
� Class timings
� MW 3:00pm-4:20pm in 318 State Hall
� Office hours
� MW 4:30pm-5:30pm in Suite 14101.3, Maccabees Building, or by
appointment
� Teaching Assistant
� TBA
Logistics (contd.)
� Prerequisites
� Basic knowledge of computer networks, for instance, materials
covered in CSC4290/CSC6290 or equivalent
� Calculus, linear algebra
� Or consent of instructor.
� Course website
� http://www.cs.wayne.edu/~hzhang/courses/7290b/7290b.html
� Course mailing list
� Web-section only: [email protected]
Logistics (contd.)
� Grading
� Class participation: 10%
� TinyExam: 30%
� Project: 60%
� Note: Students in the web section are required to come to campus to take the quizzes
and to give the project presentations in class
� Letter grades will be assigned based on performance relative to other students;
A tentative grading scale:
A: 93-100
A-: 90-92
B+: 85-89
B: 80-84
B-: 75-79
C+: 70-74
C: 65-69
C-: 60-64
F: 0-60
Project
The project consists of three parts:
� Study embedded wireless networking for intra- and/or inter-vehicle
sensing and control, smart grid sensing and control, or industrial plant
sensing and control; characterize the corresponding traffic demand in
wireless networked sensing and control;
� Formulate and solve the network design problem for wireless
networked sensing and control in connected vehicles, smart grid, or
industrial automation;
� Implement and evaluate the performance of your solution in TOSSIM,
another simulator, or NetEye testbed.
Project (contd.)
Overview of an emerging networking technology ---
Wireless Sensor Networks
� Opportunities
� Challenges in network/system design
Retrospect on computing & networking
ENIAC: first computer (1945)
Apple II: first successful PC (1977)
Internet, wireless …
Laptop, PDA … (1979 -)
First computer network
(1969)
What if
Ubiquitous Computing & Networking + Sensing & Control ?
Ubiquitous, fine-grained sensing & control
????
Sensor nodes
� A XSM sensor node (2004)
� 8MHz CPU, 4KB RAM, 128KB ROM
� Chipcon CC1000 radio: 19.2 kbps
� Infrared, acoustic, and magnetic sensors
� Sounder
…
� Many more (2001 - )
Wireless sensor networks:innovative ways of interacting with the world …
Science: ecology, seismology, oceanography …
Engineering: industrial automation, precision
agriculture, structural monitoring …
Daily life: traffic control, health care, home security, disaster recovery, virtual tour …
Tiny computers that constantly monitor ecosystems, buildings, and even human bodies could turn science on its head.
Nature, March 2006
The use of sensornets throughout society could well dwarf previous milestones in information revolution.
National Research Council report, 2001
Sensor networks of today
Redwood ecophysiology
Wind response of Golden Gate Bridge
Intruder detection, classification, and tracking
Temperature vs. Time
8
13
18
23
28
33
7/7/03
9:40
7/7/03
13:41
7/7/03
17:43
7/7/03
21:45
8/7/03
1:47
8/7/03
5:49
8/7/03
9:51
8/7/03
13:53
8/7/03
17:55
8/7/03
21:57
9/7/03
1:59
9/7/03
6:01
9/7/03
10:03
Date
Humidity vs. Time
35
45
55
65
75
85
95
Rel Humidity (%)
101 104 109 110 111
ExScal
� Field project to study scalability of middleware and applications in sensornets
� Deployed in an area of ~1,300m × 300m
� 2-tier architecture
� Lower tier: ~ 1,000 XSM, ~210 MICA2 sensor nodes (TinyOS)
� Higher tier: ~ 210 IEEE 802.11b Stargates (Linux)
• • • • … •
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• • ………... • • • ………... • • • ………... • • • ………... •
• • ………... • • • ………... • • • ………... • • • ………... •
Base Station
Other sensornet projects/applications
� Precision agriculture
� Social networking
� Ecosystem conservation
� Homeland security
� Healthcare
� Industrial control
…
BikeNet: mobile sensing system for cyclist experience mapping
� Monitor cyclist performance/fitness: speed, distance
traveled, calories burned, heart rate, galvanic skin
response, etc
� Collect environmental data: pollution, allergen, noise, and
terrain condition monitoring/mapping, etc
TurtleNet: track wood-turtles …
the turtle came out of the water to
sun itself for only brief periods and
went back into the colder water …
SealNet: use nature to help scientific study
� To measure ocean’s temperature and salinity levels,
as well as the seal’s location and depth.
� Sensing data are collected for every dive; Each time
the seals resurfaced to breathe, that data was relayed
via satellite to certain data centers in US and France
� As the seals migrated and foraged for food during their
winter journey, they circumnavigated the Antarctic
continent and its continental shelf, diving down to 2,000
feet more than 60 times a day
Healthcare
Medical implant: artificial retina …
Assisted living: health monitoring & coordination …
Health-environment monitoring: air quality, noise, bio
& chemical-agent …
Industrial control: Intel Semiconductor Factory monitoring …
Preventative equipment maintenance:
monitoring vibration signals …
• New applications and startups keep emerging …
� A seamless cyber-physical world of intelligentcomputing and networking agents …
Are sensornets mature enough to be readily used in practice?
Each large scale project may well take
� 5 professors
� 10 PhDs
� 20 Master and undergraduate
� 40 labors
� A few months/years of hard work
� $$$
…
Challenging aspects of sensor networks
� Dynamic, unreliable, and interference-prone wireless channels
� Reliable messaging
0 2 4 6 8 10 12 140
20
40
60
80
100
distance (meter)
pa
cket
deliv
ery
rate
(%
)
0 50 100 150 200 250 30075
80
85
90
95
100
time series
pa
cket
deliv
ery
rate
(%
)
5.5 meters
(×2 secs)
transitional region (unstable & unreliable)
� Indoor testbed at OSU; 3 feet node separation
� 300 data points for each distance, with each data points representing the status of 100 broadcast transmissions
Challenging aspects of sensor networks
� Dynamic, unreliable, and interference-prone wireless channels
� Reliable messaging
� Resource constraints (e.g., bandwidth, energy, memory)
� Resource-efficient services, sensornet architecture
� 19.2 kbps
� 2 AA batteries
� 4KB RAM
…
Challenging aspects of sensor networks
� Dynamic, unreliable, and interference-prone wireless channels
� Reliable messaging
� Resource constraints (e.g., bandwidth, energy, memory)
� Resource-efficient services, sensornet architecture
� Application diversity (e.g., traffic patterns, QoS requirements)
� Application-adaptivity
� infrequent aperiodic report of hazards
� need reliable and real-time report
� periodic data collection
� can tolerate certain data loss and
delay
Challenging aspects of sensor networks
� Dynamic, unreliable, and interference-prone wireless channels
� Reliable messaging
� Resource constraints (e.g., bandwidth, energy, memory)
� Resource-efficient services, sensornet architecture
� Application diversity (e.g., traffic patterns, QoS requirements)
� Application-adaptivity
� Complex faults and large system scale
� Dependability despite fault complexity and system scaleNode/link failure, state corruption, system signal loss, malfunctioning sensor, etc
Fault propagation
Increased overall probability of fault occurrence
Challenging aspects of sensor networks
� Dynamic, unreliable, and interference-prone wireless channels
� Reliable messaging
� Resource constraints (e.g., bandwidth, energy, memory)
� Resource-efficient services, sensornet architecture
� Application diversity (e.g., traffic patterns, QoS requirements)
� Application-adaptivity
� Complex faults and large system scale
� Dependability despite fault complexity and system scale
� Heterogeneity
� Architecture and service provisioning in integrated systems
Challenging aspects of sensor networks
� Dynamic and potentially unreliable wireless channels
� Reliable messaging
� Resource constraints (e.g., bandwidth, energy, memory)
� Resource-efficient services
� Application diversity (e.g., traffic patterns, QoS requirements)
� Application-adaptivity
� Complex faults and large scale
� Dependability irrespective of scale
� Growing heterogeneity
� Architecture and service provisioning in integrated systems
Our focus (2):mission-critical sensing and control in smart grids and industrial automation
O th e r C o n tro lla b le
L o a d s
W a s h e r
D ry e r
W a te r H e a te r
U n c o n tro lla b le L o a d s
P lu g -in H y b r id E le c tr ic V e h ic le
M ic rog rid
H o m e R e n e w a b le & E n e rg y
S to ra g e S y s te m
A /C
H o m e
C o n tro lle r (s )
M ic ro g rid D is tr ib u te d G e n e ra to r &
C o m b in e d H e a t a n d P o w e r S y s te m
M ic ro tu rb in eF C
M ic ro g r id
C o n tro lle r (s )
M ic ro g rid
R e n e w a b le & E n e rg y S to ra g e S y s te m
U tility G
rid
C o m m e rc ia l M ic ro g rid
O th e r M ic ro g r id s
In d u s tr ia l M ic ro g rid
R e s id e n tia l M ic ro g rid
Tra n
smiss io
n
Netw
ork
G rid
C o n tro lle r (s )
Project (contd.)
� Rules
� Students are allowed to form groups in doing projects, but the number of
students per group should be no more than 2
� Deliverables
� In-class presentation
� Written project report (in the form of a technical paper)
� Timeline
� Form your project team and select reading materials by 01/31/2015
� Submit your detailed project plan (including precise problem formulation)
and timeline by 02/28/2015
� Submit slides for your presentation at least one day before your
presentation (date to be decided)
� Submit your project report electronically by midnight 05/01/2015
Project (contd.)
� Evaluation criteria
� Breadth and depth of your understanding of the problem, as evidenced by
your project report and presentation
� Presentation quality (e.g., clarity, readability, and conciseness) of your
talk and written report
� Whether or not you are able to stick to the project timeline
Project (contd.): related resources
� MatLab
� http://www.mathworks.com/academia/student_center/tutorials/launchpad.html
� AMPL/CPLEX optimization software package
� http://www.ampl.com/DOWNLOADS/cplex80.html
� http://www.ampl.com/BOOKLETS/amplcplex90userguide.pdf
� http://www.ampl.com/
� Tools for performance evaluation
� Network simulators: TOSSIM, ns-2, qualnet/glomosim, opnet
� NetEye sensor network testbed
� TinyOS
� TinyOS Community Forum, http://www.tinyos.net/
� Phil Levis's book on TinyOS programming: http://csl.stanford.edu/%7Epal/pubs/tinyos-
programming.pdf
� TinyOS documentation: http://www.tinyos.net/tinyos-2.x/doc/
� Resources on using TinyOS and motes: http://www.tinyos.net/scoop/special/support
What is this course NOT for?
� Technology tutorial
� Instead, we focus on foundational issues
� Network programming
� Assemble networks with switches, routers, firewalls, etc.
� Design websites
� I do not really want to learn anything new, just want to get the
credits and a good grade ☺
Policies
� Lecture attendance required
� Students in web section: watch webcasting after each lecture and ask questions via the course mailing list
� TinyExam
� Exercises
� Strongly encouraged; help understanding and quiz
� Frequently check out the course website for updated information
� Other university regulations apply
How to succeed in this course?
� Attend lectures
� Look at the “big” screen, NOT the “small” computer monitor
� Read books
� Work on exercises and project
� Ask questions!!!
Questions?
Student questionnaire
� Name (optional): E-mail (optional):
� Major: Degree/Expected Year:
� Previous coursework in computer networking:
� Previous coursework in calculus, linear algebra, probability theory, and statistics:
� What do you expect to learn from this course? How do you think this course should be taught?
� How might this course contribute to your career objectives?