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