The Evolution of FreeBSD and Linux
Clemente Izurieta
Is the nature of Open Source Software (OSS) evolution
fundamentally different from that of the traditional and
commercially available software systems? Lehman and others
conducted a series of empirical studies that found that traditional
systems grow at a linear or sub-linear rate. A prior case study of
the Linux OSS system suggests that OSS may evolve in a unique
manner. Godfrey and Tu found that some aspects of Linux are
growing at a super-linear rate rather than a sub-linear rate.
Additional studies are necessary before drawing conclusions.
Thus, we examine the evolution of FreeBSD and re-analyze the
evolution of Linux, and find evidence that the growth of both
systems has a linear upper bound, and thus appear to grow at
similar rates to that of commercial systems. These results do not
support the hypothesis that OSS systems grow at rates that exceed
that of traditional systems.
BIO
Clem Izurieta is a graduate student at Colorado State University. Born in
Chile, Clem has a bachelor of Mathematics from the University of Wollongong,
NSW, Australia, and a Masters degree in Computer Science from Montana State
University. Clem has worked in industry for many years including 12 years at
the Unix Research and Development Laboratories of Hewlett Packard and most
recently 2 years at the Fort Collins Design Center of Intel Corporation.
Clem's interests include algorithms, building software systems, and all
aspects of software engineering associated with this.
Mixed Discrete and Continuous Algorithms for Scheduling Airborne
Astronomy Observations
We describe the problem of scheduling astronomy observations for
the Stratospheric Observatory for Infrared Astronomy, an airborne
telescope. The
problem requires maximizing the number of requested observations
scheduled
subject to a mixture of discrete and continuous constraints
relating the feasibility
of an astronomical observation to the position and time at which
the observation
begins, telescope elevation limits, Special Use Airspace
limitations, and
available fuel. Solving the problem requires making discrete
choices (e.g. selection
and sequencing of observations) and continuous ones (e.g. takeoff
time
and setup actions for observations by repositioning the aircraft).
Previously, we
developed an incomplete algorithm called ForwardPlanner using a
combination
of AI and OR techniques including progression planning, lookahead
heuristics,
stochastic sampling and numerical optimization, to solve a
simplified version of
this problem. While initial results were promising, ForwardPlanner
fails to scale
when accounting for all relevant constraints. We describe a novel
combination of
Squeaky Wheel Optimization (SWO), an incomplete algorithm designed
to solve
scheduling problems, with previously devised numerical optimization
methods
and stochastic sampling approaches, as well as heuristics based on
reformulations
of the SFPP to traditional OR scheduling problems. We show that
this new
algorithm finds as good or better flight plans as the previous
approaches, often
with less computation time.
Department of Computer Science
Colorado State University
Jeremy Frank
Planning and Scheduling Group
NASA Ames Research Center
Paper:
J. Frank and E. Kurklu
Mixed Discrete and Continuous Algorithms
for Scheduling Airborne Astronomy Observations,
Proceedings of the 2nd International Conference on Constraint
Programming, Artificial Intelligence and Operations Research, 2005