886 research outputs found
Towards Optimality in Parallel Scheduling
To keep pace with Moore's law, chip designers have focused on increasing the
number of cores per chip rather than single core performance. In turn, modern
jobs are often designed to run on any number of cores. However, to effectively
leverage these multi-core chips, one must address the question of how many
cores to assign to each job. Given that jobs receive sublinear speedups from
additional cores, there is an obvious tradeoff: allocating more cores to an
individual job reduces the job's runtime, but in turn decreases the efficiency
of the overall system. We ask how the system should schedule jobs across cores
so as to minimize the mean response time over a stream of incoming jobs.
To answer this question, we develop an analytical model of jobs running on a
multi-core machine. We prove that EQUI, a policy which continuously divides
cores evenly across jobs, is optimal when all jobs follow a single speedup
curve and have exponentially distributed sizes. EQUI requires jobs to change
their level of parallelization while they run. Since this is not possible for
all workloads, we consider a class of "fixed-width" policies, which choose a
single level of parallelization, k, to use for all jobs. We prove that,
surprisingly, it is possible to achieve EQUI's performance without requiring
jobs to change their levels of parallelization by using the optimal fixed level
of parallelization, k*. We also show how to analytically derive the optimal k*
as a function of the system load, the speedup curve, and the job size
distribution.
In the case where jobs may follow different speedup curves, finding a good
scheduling policy is even more challenging. We find that policies like EQUI
which performed well in the case of a single speedup function now perform
poorly. We propose a very simple policy, GREEDY*, which performs near-optimally
when compared to the numerically-derived optimal policy
Heavy traffic analysis of a polling model with retrials and glue periods
We present a heavy traffic analysis of a single-server polling model, with
the special features of retrials and glue periods. The combination of these
features in a polling model typically occurs in certain optical networking
models, and in models where customers have a reservation period just before
their service period. Just before the server arrives at a station there is some
deterministic glue period. Customers (both new arrivals and retrials) arriving
at the station during this glue period will be served during the visit of the
server. Customers arriving in any other period leave immediately and will retry
after an exponentially distributed time. As this model defies a closed-form
expression for the queue length distributions, our main focus is on their
heavy-traffic asymptotics, both at embedded time points (beginnings of glue
periods, visit periods and switch periods) and at arbitrary time points. We
obtain closed-form expressions for the limiting scaled joint queue length
distribution in heavy traffic and use these to accurately approximate the mean
number of customers in the system under different loads.Comment: 23 pages, 2 figure
Magical housing for status holders:a research on the social environment in Magic Mix projects
Wie of wat gelooft u? De status van kennis in de sociale media
De opkomst en daarmee het belang van sociale media
kan niet als iets ‘nieuws’ beschouwd worden. Zeker als we
‘nieuw’ definiëren in termen van gisteren, vandaag of morgen.
Sociale media zijn sinds de jaren 2000 een steeds groter en
populairder platform geworden. Ook het openbaar bestuur
probeert de laatste jaren aan te haken bij deze ontwikkeling
en vooral de mogelijkheden van sociale media te benutten.
Een betrekkelijk nieuw en belangrijk vraagstuk als gevolg
van deze activiteiten op sociale media wordt nog wel eens
vergeten en onderschat
A queueing-theoretic analysis of the threshold-based exhaustive data-backup scheduling policy
We analyse the threshold-based exhaustive data backup scheduling mechanism by means of a queueing-theoretic approach. Data packets that have not yet been backed up are modelled by customers waiting for service (back-up). We obtain the probability generating function of the system content (backlog size) at random slot boundaries in steady state
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