Bev Littlewood (City University
London) Dependability
Assessment of Software-based Systems: State of the Art
18 May @ 11:00 AM
St.
Louis Ballroom A [Floor
Plan]
Session Chair: Jeff Kramer
[Slides]
Biography: Bev Littlewood
was co-Founder of the Centre for Software Reliability
and Director from 1983-2003. He is Professor of Software Engineering
at City University London. Bev has worked for many years on problems
associated with the modeling and evaluation of dependability of
software-based systems; he has published many papers in international
journals and conference proceedings and has edited several books. He
is a member of the UK Nuclear Safety Advisory Committee, of IFIP
Working Group 10.4 on Reliable Computing and Fault Tolerance, and of
the BCS Safety-Critical Systems Task Force. He is a Fellow of the
Royal Statistical Society.
Abstract: Everyone
knows that it is important to make systems dependable. Indeed, much of
software engineering can be seen to be a
means to this end (albeit not always acknowledged as
such). Unfortunately, these means of achieving dependability -
reliability, safety, security - cannot be guaranteed to succeed,
particularly for systems in which complex software plays a key
role. In particular, claims for system 'perfection' are never
believable. It is therefore necessary to have procedures for
assessing, preferably quantitatively, what level of dependability has
actually been achieved for a particular system. This turns out to be a
hard problem.
In this talk I shall describe the progress
that has been made in recent years in quantitative assessment of modest
levels of
reliability for software-based systems, such as in a safety-case
formalism. I shall identify deficiencies in our present capabilities,
as in assessment of socio-technical systems, the limits to the levels
of dependability that can be claimed, and in assessment of operational
security. I shall identify, and critically analyse, some of the
proposed ways forward, such as the use of BBNs and 'diversity'.
Armando Fox (Stanford
University)
Addressing Software Dependability with Statistical
and Machine Learning Techniques
18 May @ 11:00 AM
St.
Louis Ballroom A [Floor
Plan]
Session Chair: Jeff Kramer
[Slides]
Biography: Armando
Fox joined the Stanford faculty as an Assistant Professor in
January 1999. He received his Ph.D. from UC Berkeley, where he worked
with Professor Eric Brewer (co-founder of Inktomi Corp.) building
research prototypes of today's clustered Internet services and showing
how to use them to support mobile computing applications, including
the world's first graphical Web browser for handheld computers. His
research interests include system dependability and ubiquitous
computing. Armando was listed among the "Scientific American 50" of
2003 for his work on Recovery-Oriented Computing.
Prof. Fox has
received the Associated Students of Stanford University Teaching Award and the
Tau Beta Pi Award for Excellence in Undergraduate Engineering Education,
and has been named a Professor of the Year by the Stanford chapter of the
Society of Women Engineers. He received a BSEE from M.I.T. and an MSEE from
the University of Illinois, and worked as a
CPU architect at Intel Corp. He is also an ACM member and a founder of ProxiNet
(acquired by Pumatech in 1999), which commercialized thin
client mobile computing technology he helped develop at UC Berkeley. He can be
reached at fox@cs.stanford.edu.
Abstract: Our ability
to design and deploy large complex systems is outpacing
our ability to understand their behavior. How do we detect and
recover from "heisenbugs", which account for up to 40% of failures
in
complex Internet systems, without extensive application-specific
coding? Which users were affected, and for how long? How do we
diagnose and correct problems caused by configuration errors or
operator errors? Although these problems are posed at a high level of
abstraction, all we can usually measure directly are low-level
behaviors---analogous to driving a car while looking through a
magnifying glass. Machine learning can bridge this gap using
techniques that learn "baseline" models automatically or
semi-automatically, allowing the characterization and monitoring of
systems whose structure is not well understood a priori. In this talk
I'll discuss initial successes and future challenges in using machine
learning for failure detection and diagnosis, configuration
troubleshooting, attribution (which low-level properties appear to be
correlated with an observed high-level effect such as decreased
performance), and failure forecasting.
Roy Want (Intel Corp.)
System Challenges for Ubiquitous and Pervasive Computing
18 May @ 2:00 PM
St.
Louis Ballroom A & B [Floor
Plan]
Session Chair: David Garlan
[Slides]
Biography: Roy
Want is a Principal Engineer at Intel Research/CTG in Santa Clara,
California, and leader of the Ubiquity Strategic Research Project
(SRP). He is responsible for exploring long-term strategic research
opportunities in the area of Ubiquitous & Pervasive Computing.
His
interests include proactive computing, wireless protocols, hardware
design, embedded systems, distributed systems, automatic
identification and micro-electromechanical systems (MEMS).
Want received his BA in computer science from Churchill College,
Cambridge University, UK in 1983 and continued research at Cambridge
into reliable distributed multimedia-systems. He earned a PhD in
1988. He joined Xerox PARC's Ubiquitous Computing program in 1991. At
PARC Want managed the Embedded Systems group. He joined Intel in 2000.Want is
the author, or co-author, of more than 40 publications in the
areas of mobile and distributed systems; and also holds over 50
patents in these areas. Contact information: Intel Corporation, 2200
Mission College Blvd, Santa Clara, CA 95052, USA, e-mail
roy.want@intel.com
Abstract:The terms Ubiquitous and Pervasive computing
were first coined at the beginning of the 90's, by Xerox PARC and IBM
respectively, and capture
the realization that the computing focus was going to change from the
PC to a more distributed, mobile and embedded form of
computing. Furthermore, it was predicted by some researchers that the
true value of embedded computing would come from the orchestration of
the various computational components into a much richer and adaptable
system than had previously been possible.
Now some 15 years further on we have made progress towards these
aims. The hardware platforms encapsulate significant computation
capability in a small volume, at low power and cost. However, the
system software capabilities have not advanced at a pace that can take
full advantage of this infrastructure. This talk will describe where
software and hardware have combined to enable ubiquitous computing,
where these systems have limitations and where the biggest challenges
still remain.
Jeff Kephart (IBM Thomas
J. Watson Research Center) Research Challenges of Autonomic Computing
18 May @ 2:00 PM
St.
Louis Ballroom A & B [Floor
Plan]
Session Chair: David
Garlan
[Slides]
Biography: Jeffrey
O. Kephart manages the Agents and Emergent Phenomena group at
the IBM Thomas J. Watson Research Center, and shares responsibility
for developing IBM's Autonomic Computing research strategy. He and
his
group focus on the application of analogies from biology and economics
to massively distributed computing systems, particularly in the
domains of autonomic computing, e-commerce, antivirus, and anti-spam
technology.
Kephart's research efforts on digital immune
systems and economic software agents have been publicized in publications
such as The Wall Street Journal, The New York Times, Forbes, Wired, Harvard
Business Review, IEEE Spectrum, and Scientific American. In 2004, he co-founded the International Conference on Autonomic Computing.
Kephart received a BS from Princeton University and a PhD from Stanford
University, both in electrical engineering.
Abstract: The increasing
complexity of computing systems is beginning to
overwhelm the capabilities of software developers and system
administrators to design, evaluate, integrate, and manage these
systems. Major software and system vendors such as IBM, HP and
Microsoft have concluded that the only viable long-term solution is to
create computer systems that manage themselves.
Three years ago, IBM launched the autonomic computing initiative to
meet the grand challenge of creating self-managing systems. Although
much has already been achieved, it is clear that a worldwide
collaboration among academia, IBM, and other industry partners will be
required to fully realize the vision of autonomic computing. I will
discuss several fundamental challenges in the areas of artificial
intelligence and agents, performance modeling, optimization,
architecture, policy, and human-computer interaction, and describe
some of the initial steps that IBM and its partners in academia have
taken to address those challenges.