RA2: predicting simulation execution time for cloud-based design space explorations

Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running large-scale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key problem would be to accurately predict the running time for each simulation instance in advance. This is not trivial due to the currently wide range of cloud resource types which offer varying levels of performance. In addition, the widespread use of virtualization techniques in most cloud providers often introduces unpredictable performance interference. In this paper, we propose a resource and application-aware (RA2) prediction approach to combat performance variability on clouds. In particular, we employ neural network based techniques coupled with non-intrusive monitoring of resource availability to obtain more accurate predictions. We conducted extensive experiments on commercial cloud platforms using an evacuation planning design problem over a month-long period. The results demonstrate that it is possible to predict simulation execution times in most cases with high accuracy. The experiments also provide some interesting insights on how we should run similar simulation problems on various commercially available clouds

Guide them through: an automatic crowd control framework using multi-objective genetic programming

We propose an automatic crowd control framework based on multi-objective optimisa- tion of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for opti- mal overall crowd flow in realtime, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front al- lows selection of resilient and efficient crowd control strategies in different situations. We first chose a benchmark scenario as used in [1] to test the proposed method. Results show that our method is capable of finding control strategies that are not only quanti- tatively measured better, but also well aligned with domain experts’ recommendations on effective crowd control such as “slower is faster” and “asymmetric control”. We further applied the proposed framework in actual event planning with approximately 400 participants navigating through a multi-story building. In comparison with the baseline crowd models that do no employ control strategies or just use some hard-coded rules, the proposed framework achieves a shorter travel time and a significantly lower (20%) congestion along critical segments of the path

Protective effects of resveratrol on the inhibition of hippocampal neurogenesis induced by ethanol during early postnatal life

AbstractEthanol (EtOH) exposure during early postnatal life triggers obvious neurotoxic effects on the developing hippocampus and results in long-term effects on hippocampal neurogenesis. Resveratrol (RSV) has been demonstrated to exert potential neuroprotective effects by promoting hippocampal neurogenesis. However, the effects of RSV on the EtOH-mediated impairment of hippocampal neurogenesis remain undetermined. Thus, mice were pretreated with RSV and were later exposed to EtOH to evaluate its protective effects on EtOH-mediated toxicity during hippocampal development. The results indicated that a brief exposure of EtOH on postnatal day 7 resulted in a significant impairment in hippocampal neurogenesis and a depletion of hippocampal neural precursor cells (NPCs). This effect was attenuated by pretreatment with RSV. Furthermore, EtOH exposure resulted in a reduction in spine density on the granular neurons of the dentate gyrus (DG), and the spines exhibited a less mature morphological phenotype characterized by a higher proportion of stubby spines and a lower proportion of mushroom spines. However, RSV treatment effectively reversed these responses. We further confirmed that RSV treatment reversed the EtOH-induced down-regulation of hippocampal pERK and Hes1 protein levels, which may be related to the proliferation and maintenance of NPCs. Furthermore, EtOH exposure in the C17.2 NPCs also diminished cell proliferation and activated apoptosis, which could be reversed by pretreatment of RSV. Overall, our results suggest that RSV pretreatment protects against EtOH-induced defects in neurogenesis in postnatal mice and may thus play a critical role in preventing EtOH-mediated toxicity in the developing hippocampus

Spin-glass ground state in a triangular-lattice compound YbZnGaO4_4

We report on comprehensive results identifying the ground state of a triangular-lattice structured YbZnGaO$_4$ to be spin glass, including no long-range magnetic order, prominent broad excitation continua, and absence of magnetic thermal conductivity. More crucially, from the ultralow-temperature a.c. susceptibility measurements, we unambiguously observe frequency-dependent peaks around 0.1 K, indicating the spin-glass ground state. We suggest this conclusion to hold also for its sister compound YbMgGaO$_4$, which is confirmed by the observation of spin freezing at low temperatures. We consider disorder and frustration to be the main driving force for the spin-glass phase.Comment: Version as accepted to PR