7 research outputs found
Solving the At-Most-Once Problem with Nearly Optimal Effectiveness
We present and analyze a wait-free deterministic algorithm for solving the
at-most-once problem: how m shared-memory fail-prone processes perform
asynchronously n jobs at most once. Our algorithmic strategy provides for the
first time nearly optimal effectiveness, which is a measure that expresses the
total number of jobs completed in the worst case. The effectiveness of our
algorithm equals n-2m+2. This is up to an additive factor of m close to the
known effectiveness upper bound n-m+1 over all possible algorithms and improves
on the previously best known deterministic solutions that have effectiveness
only n-log m o(n). We also present an iterative version of our algorithm that
for any is both
effectiveness-optimal and work-optimal, for any constant . We
then employ this algorithm to provide a new algorithmic solution for the
Write-All problem which is work optimal for any
.Comment: Updated Version. A Brief Announcement was published in PODC 2011. An
Extended Abstract was published in the proceeding of ICDCN 2012. A full
version was published in Theoretical Computer Science, Volume 496, 22 July
2013, Pages 69 - 8
Doing-it-All with Bounded Work and Communication
We consider the Do-All problem, where cooperating processors need to
complete similar and independent tasks in an adversarial setting. Here we
deal with a synchronous message passing system with processors that are subject
to crash failures. Efficiency of algorithms in this setting is measured in
terms of work complexity (also known as total available processor steps) and
communication complexity (total number of point-to-point messages). When work
and communication are considered to be comparable resources, then the overall
efficiency is meaningfully expressed in terms of effort defined as work +
communication. We develop and analyze a constructive algorithm that has work
and a nonconstructive
algorithm that has work . The latter result is close to the
lower bound on work. The effort of each of
these algorithms is proportional to its work when the number of crashes is
bounded above by , for some positive constant . We also present a
nonconstructive algorithm that has effort