3,071 research outputs found
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Fine tuning distributed systems is considered to be a craftsmanship, relying
on intuition and experience. This becomes even more challenging when the
systems need to react in near real time, as streaming engines have to do to
maintain pre-agreed service quality metrics. In this article, we present an
automated approach that builds on a combination of supervised and reinforcement
learning methods to recommend the most appropriate lever configurations based
on previous load. With this, streaming engines can be automatically tuned
without requiring a human to determine the right way and proper time to deploy
them. This opens the door to new configurations that are not being applied
today since the complexity of managing these systems has surpassed the
abilities of human experts. We show how reinforcement learning systems can find
substantially better configurations in less time than their human counterparts
and adapt to changing workloads
Sporadic Aurora near Geomagnetic Equator: In the Philippines, on 27 October 1856
While low latitude auroral displays are normally considered to be a
manifestation of magnetic storms of considerable size, Silverman (2003, JGR,
108, A4) reported numerous "sporadic auroras" which appear locally at
relatively low magnetic latitudes during times of just moderate magnetic
activity. Here, a case study is presented of an aurora near the geomagnetic
equator based on a report from the Philippine Islands on 27 October 1856. An
analysis of this report shows it to be consistent with the known cases of
sporadic aurorae except for its considerably low magnetic latitude. The record
also suggests that extremely low-latitude aurora is not always accompanied with
large magnetic storms. The description of its brief appearance leads to a
possible physical explanation based on an ephemeral magnetospheric disturbance
provoking this sporadic aurora.Comment: 15 pages, 3 figures, accepted for publication in Annales Geophysicae
on 18 August 201
155-day Periodicity in Solar Cycles 3 and 4
The near 155 days solar periodicity, so called Rieger periodicity, was first
detected in solar flares data and later confirmed with other important solar
indices. Unfortunately, a comprehensive analysis on the occurrence of this
periodicity during previous centuries can be further complicated due to the
poor quality of the sunspot number time-series. We try to detect the Rieger
periodicity during the solar cycles 3 and 4 using information on aurorae
observed at mid and low latitudes. We use two recently discovered aurora
datasets, observed in the last quarter of the 18th century from UK and Spain.
Besides simple histograms of time between consecutive events we analyse monthly
series of number of aurorae observed using different spectral analysis (MTM and
Wavelets). The histograms show the probable presence of Rieger periodicity
during cycles 3 and 4. However different spectral analysis applied has only
confirmed undoubtedly this hypothesis for solar cycle 3.Comment: 13 pages, 6 figures, to appear in New Astronom
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