Further reading
Articles mentioned in the course
 Using the TCP fixed point equations to calculate throughputs:
 Buffer size and TCP:

Models for a selfmanaged Internet, F.Kelly, 1999.

Traffic engineering versus
content distribution: a game theoretic perspective,
D.DiPalantino and R.Johari, 2009
 A Study of Networks Simulation Efficiency: Fluid Simulation vs. Packetlevel Simulation
Benyuan Liu, Daniel R. Figueiredo, Yang Guo, Jim Kurose, Don Towsley
[pdf]
 The fourth quadrant: a map of the limits of statistics, Nassim Taleb. Edge
 The mathematics of traffic
in networks,
F. Kelly. Princeton companion to mathematics.
 SybilGuard: defending against Sybil attacks via social networks,
H. Yu, M. Kaminsky, P. B. Gibbons, A. Flaxman. SIGCOMM 2006.
See also the public review.
 A Brief History of
Generative Models for Power Law and Lognormal Distributions, M.Mitzenmacher, Internet Mathematics 2004.
 Widearea
traffic: the failure of Poisson modeling, V. Paxson and
S. Floyd. IEEE/ACM Transactions on Networking, 1995.
 An empirical study of operating systems errors,
A. Chou, J. Yang, B. Chelf, S. Hallem and D. Engler, Symposium on Operating Systems Principles, 2001.
 The
anatomy of a largescale hypertextual web search engine,
S. Brin and L. Page. WWW7 / Computer Networks, 1998.
 Inside
PageRank,
Monica Bianchini, Marco Gori, Franco Scarselli. ACM Transactions on
Internet Technology, 2005.
 Loss Networks, Frank Kelly. Annals of Applied Probability, 1991.
 Characteristics
of WWW clientbased traces, C.A. Cunha, A. Bestavros,
M.E. Crovella. Technical report TR95010, Boston University Dept of
CS, 1995.
 Congestion avoidance and control, Van Jacobson, 1988.
Related books and courses
Some courses and books which cover similar material to this course,
though usually with more mathematics:
An brilliant guide to how to think about visualizing data:
Books from which to learn probability:

A modern introduction to probability and statistics:
understanding why and how, F.M.Dekking, C.Kraaikamp, H.P.Lopuhaa,
L.E.Meester (Springer).
[1 copy in science library]
[1 copy borrowable from DJW]
[amazon].
Very clear presentation; chapters 1—9 all relevant.


Probability and computing: randomized algorithms and probabilistic
analysis, M.Mitzenmacher and E.Upfal (Cambridge)
[amazon].
Chapters 1,2,7 are a brisk and wellwritten introduction to
probability. The rest of the book is fascinating and very relevant to
computer scientists.


Probability via expectation, P.Whittle (Springer).
[3 copies in science library]
[amazon].
Idiosyncratic and very thoughtful.
Very readable introduction—see pages 1–20, 39–60.


Probability and statistics by example: basic probability and
statistics,
Y.Suhov and M.Kelbert (Cambridge)
[amazon].
Thorough and dense to learn from, but does have a wealth of good exercises.


Introduction to probability and statistics, published by Schaum's Outlines,
ISBN 9780070380844
[amazon]
[1 copy in science library].
An introduction to probability suitable for firstyear undergraduates.


Probability Demystified by Allan G. Bluman, published by McGrawHill,
ISBN 9780071445498
[amazon].
A good introduction to the basics with plenty of worked questions.


Complete Advanced Level Mathematics—Statistics,
published by Nelson Thornes, ISBN 9780748735600
[amazon].
A clear and straightforward guide to Alevel probability and statistics.

Books from which to revise calculus:

Beginning Calculus by Elliott Mendelson,
published by Schaum's Outlines,
ISBN 9780071635356
[amazon].
A slightly more sophisticated introduction to calculus, well written, somewhat dense.


Calculus for Dummies by Mark Ryan, published by Wiley,
ISBN 9780764524981
[amazon].
Sometimes too wordy, but clear enough.


AS Use of Maths—Calculus, published by Nelson Thornes,
ISBN 9780748769780
[amazon].
A clear and straightforward guide to Alevel calculus.
