Distributed Systems (DS) are usually complex systems composed of various components. Due to increasing complexity and scaling of DSs, reliability becomes a major challenge for the design of such systems. The nodes and links of a DS typically have different hazard rates; therefore, proper task allocation can significantly improve system reliability. On the other hand, optimal task allocation in DSs is an NP-hard problem, thus finding exact solutions are limited to small-scale problems. This paper presents a new swarm intelligence technique based on Cat Swarm Optimization (CSO) algorithm to find near optimal solution. For evaluating the algorithm, CSO is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results show that in contrast to PSO and GA, CSO acquires acceptable reliability in reasonable execution time. The confidence interval is set at the 95% confidence level. By assessing the confidence interval, it is observable that CSO has low reliability deviation.

No abstract

No abstract

Stellar feedback in dwarf galaxies plays a critical role in regulating star formation via galaxy-scale winds. Recent hydrodynamical zoom-in simulations of dwarf galaxies predict that the periodic outward flow of gas can change the gravitational potential sufficiently to cause radial migration of stars. To test the effect of bursty star formation on stellar migration, we examine star formation observables and sizes of 86 local dwarf galaxies. We find a correlation between the R-band half-light radius (R e ) and far-UV luminosity (L FUV) for stellar masses below 108 M ⊙ and a weak correlation between the R e and Hα luminosity (L Hα ). We produce mock observations of eight low-mass galaxies from the FIRE-2 cosmological simulations and measure the similarity of the time sequences of R e and a number of star formation indicators with different timescales. Major episodes of R e time sequence align very well with the major episodes of star formation, with a delay of ∼50 Myr. This correlation decreases toward star formation rate indicators of shorter timescales such that R e is weakly correlated with L FUV (10–100 Myr timescale) and is completely uncorrelated with L Hα (a few Myr timescale), in agreement with the observations. Our findings based on FIRE-2 suggest that the R-band size of a galaxy reacts to star formation variations on a ∼50 Myr timescale. With the advent of a new generation of large space telescopes (e.g., JWST), this effect can be examined explicitly in galaxies at higher redshifts, where bursty star formation is more prominent.

Over the last decade, time series motif discovery has emerged as a useful primitive for many downstream analytical tasks, including clustering, classification, rule discovery, segmentation, and summarization. In parallel, there has been an increased understanding that Dynamic Time Warping (DTW) is the best time series similarity measure in a host of settings. Surprisingly however, there has been virtually no work on using DTW to discover motifs. The most obvious explanation of this is the fact that both motif discovery and the use of DTW can be computationally challenging, and the current best mechanisms to address their lethargy are mutually incompatible. In this work, we present the first scalable exact method to discover time series motifs under DTW. Our method automatically performs the best trade-off between time-to-compute and tightness-of-lower-bounds for a novel hierarchy of lower bounds representation we introduce. We show that under realistic settings, our algorithm can admissibly prune up to 99.99% of the DTW computations.