Inaugural Lecture as Professor of Software Systems, University College London, 13 December 2004.

1. Scalability in
Software Systems Engineering
The Good, the Bad, and the Ugly
David S. Rosenblum
Professor of Software Systems
Director, London Software Systems
Inaugural Lecture, 13 December 2004 © 2004

2. The Concept of Scalability

3. The Importance of Scalability
 Gartner predictions for 2008
 Moore’s Law continues to hold
 Desktop PC: 4–8 CPUs @ 40GHz, 4–12GB
RAM, 1.5TB storage, 100Gb network
 Desktop PCs < 50% of end-user devices
 Bandwidth more cost-effective than
computing
But only if software systems can scale!
But only if software systems can scale!
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4. Some Notions of Scalability (1)
“Scalability is a key requirement for the corporate
content infrastructure, … [which] needs to be
capable of handling high volumes of content as
well as of fulfilling high performance
requirements.”
— Documentum
“The Java 2 Platform, Micro Edition (J2ME) technology
from Sun Microsystems, Inc. is used by developers
to scale Java technology-based applications into
small consumer and embedded devices.”
— Sun Microsystems
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5. Some Notions of Scalability (2)
“[A Session Announcement Protocol] announcement is
multicast with the same scope as the session it is
announcing … [This is] important for the
scalability of the protocol, as it keeps local
session announcements local.”
— Mark Handley et al., RFC 2974
“Scalability: the ease with which a system or
component can be modified to fit the problem
area.”
— Software Engineering Institute5

6. What Is Scalability?
 Is It High Performance?
 Computations/messages/transactions per
second
 Fixed-size and fixed-time parallel speedup
 Is It Computational Complexity?
 Time and space complexity of algorithms
 Is It Abstraction?
 Example: programmer productivity versus
expressive power of programming languages
It is a characterisation of resource
It is a characterisation of resource
consumption as a function of problem size
consumption as a function of problem size 6

7. Techniques for
Achieving Scalability

8. Scalability in My Own Research
 Continual interest in problems of
engineering large-scale software
systems
 Scalable software infrastructures
 Scalable software development tools
 Many techniques used to achieve
scalability
 Each has an associated cost
 Each was chosen serendipitously
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9. Technique #1
Abstraction
 Intuition: Analyse larger systems by
ignoring the “nonessential detail”
 Cost: Abstraction can hide useful
information and introduce
inaccuracies
 Example: 5ESS Build Process Studies
(1991–1994)
A.L. Wolf and D.S. Rosenblum, “A Study in Software Process Data Capture and Analysis”,
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Proc. 2nd Int’l Conf. on the Software Process, Berlin, Germany, Feb. 1993, pp. 115–124.

10. The 5ESS®
Switching System Software
 Primary central office switch product of
Lucent (formerly AT&T)
 By the numbers (c. 1994)
 5–7 million lines of code in 50+ subsystems
 2000 developers
 New version built every 4 weeks
 2 hours downtime per year (“The Good”)
 Except on 15 January 1990 (“The Ugly”)
“It’s not just a program—it’s a field of study!”
“It’s not just a program—it’s a field of study!” 10

11. The 5ESS Build Process
 Nominally required 1 week to compile new
version of software into executable form
 Frequently took 2–3 weeks (“The Bad”)
 Simple syntax and semantics errors required
frequent restart of build
 What are the “problem subsystems”?
 Events from build tools give very accurate
abstract characterisation of process
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13. Technique #2
Execution
 Intuition: Observing individual executions
is easier than analysing whole programs
 Cost: Forgo results about all executions
 Example: Runtime assertion checking
 ANNA (ANNotated Ada) (1983–1988)
 Attain benefits of systematic specification without
difficulties and costs of formal proofs of correctness
 APP (1988–1996)
 Assertions detected 80% of faults in case study
 Assertion diagnostics quickly isolated faults
D.S. Rosenblum, “A Practical Approach to Programming with Assertions”,
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IEEE Transactions on Software Engineering, Vol. 21, No. 1, Jan. 1995, pp. 19–31.

14. Technique #3
Coarse-Grained Analysis
 Intuition: Variant of abstraction
 Cost: Reduced precision
 Example: TestTube (1991–2001)
 Selective regression testing of C programs
 Existing approaches worked at statement level
 TestTube analyses test coverage in terms of
Functions + Global Vars + Types + Macros
 First large empirical study of selective
regression testing (30 KLOC)
Y.-F. Chen, D.S. Rosenblum and K.-P. Vo, “TestTube: A System for Selective Regression
Testing”, Proc. 16th Int’l Conf. on Software Engineering, Sorrento, Italy, May 1994,
pp. 211–220. 14

15. Technique #4
Distribution
 Intuition: Build or analyse larger systems
by partitioning the solution
 Enhance with replication, decentralisation,
localisation, multicasting, …
 Cost: Increased complexity of solution
 Example: SIENA (1996- )
 Internet-scale publish/subscribe network
communication
A. Carzaniga, D.S. Rosenblum and A.L. Wolf, “Design and Evaluation of a Wide-Area
Event Notification Service”, ACM Transactions on Computer Systems, Vol. 19, No. 3,
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August 2001, pp. 332–383.

16. Publish/Subscribe
A natural communication style for asynchronous,
A natural communication style for asynchronous,
real-time, distributed content dissemination
real-time, distributed content dissemination
Publishers Publish/Subscribe Infrastructure Subscribers
Infrastructure register subscriptions to
Publishers publish notifications with
Subscribers delivers notifications
Infrastructure determines which
subscriptions matchinformation
characterizing information interests
interesting subscribers
matching which notifications
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17. SIENA Content-Based Routing
s1:1
s1:1
s2:5
s2:5
s1:a
s1:a
a 2
1 s2:2
s2:2
s1:2 s1:2
s1:2
s1:2
s2:8
s2:8 3
5
s1:1
s1:1 4 s1:3
s1:3 6
s1:3
s1:3
7
n1
s1:5
s1:5 8
b s2:b
s2:b 9
s1:6
s1:6
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18. Technique #5
Approximation
 Intuition: Handle larger problem sizes by forgoing
exact results
 Cost: False positives and/or false negatives
 Example: PreCache (2001–2003)
 Conservative approximate matching algorithms for
improved performance in publish/subscribe networking
 O(1) and O(log k) matching time against k subscriptions
 Was scalability achieved?
 Approximation increases messages traffic
 Increased traffic requires increased matching
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19. Other Techniques
6. Compositionality
o Intuition: Subdivide problem,
manipulate pieces, compose results
o Different from Distribution
7. Scale with Moore’s Law
o Intuition: Hope that physics saves you
o This has worked for regression testing
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20. The Future:
Scalability Engineering

21. Scalability Questions
 How can one demonstrate the ability of a
system like 5ESS to scale before trying to
implement it and deploy it on thousands of
computers across the world?
 What information about a software system
enables prediction of its scalability?
 What design characteristics increase or
decrease (or contribute to or detract from)
scalability?
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22. Scalability Engineering
 A principled basis for
 Choosing and applying scalability
enabling techniques
 Evaluating scalability of software system
designs and implementations
 Choosing scalable engineering methods
 Comparing scalability of different
designs/implementations/methods
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23. Conclusion

24. Conclusion
 Scalability is an increasingly important
issue for software systems engineering
 Everybody talks about it
 Yet we lack well-defined, applicable,
measurable principles of scalability
Scalability engineering should become a part
Scalability engineering should become a part
of every software system engineering effort
of every software system engineering effort
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