Automating Fault Tolerance in High-Performance Computational Biological Jobs Using Multi-Agent Approaches

Background: Large-scale biological jobs on high-performance computing systems require manual intervention if one or more computing cores on which they execute fail. This places not only a cost on the maintenance of the job, but also a cost on the time taken for reinstating the job and the risk of losing data and execution accomplished by the job before it failed. Approaches which can proactively detect computing core failures and take action to relocate the computing core's job onto reliable cores can make a significant step towards automating fault tolerance. Method: This paper describes an experimental investigation into the use of multi-agent approaches for fault tolerance. Two approaches are studied, the first at the job level and the second at the core level. The approaches are investigated for single core failure scenarios that can occur in the execution of parallel reduction algorithms on computer clusters. A third approach is proposed that incorporates multi-agent technology both at the job and core level. Experiments are pursued in the context of genome searching, a popular computational biology application. Result: The key conclusion is that the approaches proposed are feasible for automating fault tolerance in high-performance computing systems with minimal human intervention. In a typical experiment in which the fault tolerance is studied, centralised and decentralised checkpointing approaches on an average add 90% to the actual time for executing the job. On the other hand, in the same experiment the multi-agent approaches add only 10% to the overall execution time.Comment: Computers in Biology and Medicin

The Role of Computational Science and Emerging Technologies in the Natural Sciences Education at University Level

AbstractThis paper is focused on the role of Computational Science and emerging technologies in the natural sciences education at university level. We outline our Integrated Metacognitive Process Model (IMPM) and our Collaborative Learning approach based on Collaborative Creative Cross-Pollination activity model at postgraduate level. We present our multidisciplinary approach based on the following three components: the existence of multidisciplinary research environment (non-silos departmental culture), computational science research methods as core part of the curricula and collaborative teaching activities facilitated by novel collaborative tools using Collaborative Creative Cross-Pollination. Some results showing the advantages of such an environment and approach are presented. The initial results have shown overall average improvement of the average marks with around 5% plus clear satisfaction of the students as evident from their responses to the course evaluation

Discovering most significant news using Network Science approach

AbstractThe role of social network mass media has increased greatly in the recent years. In this paper we investigate news publication in Twitter based on Network Science approach. We analyzed news data posted using the most popular media sources to discover the most significant news over a given period of time. Significance is a qualitative property that reflects the degree of news impact on society and public opinion. We have attempted to define the threshold of significance and discover a number of news which had some significance for society in period of time from July 2014 to January 2015

Parallel Regularized Multiple-criteria Linear Programming

In this paper, we proposed a new parallel algorithm: Parallel Regularized Multiple-Criteria Linear Programming (PRMCLP) to overcome the computing and storage requirements increased rapidly with the number of training samples. Firstly, we convert RMCLP model into a unconstrained optimization problem, and then split it into several parts, and each part is computed by a single processor. After that, we analyze each part's result for next cycle going. By doing this, we are be able to obtain the final optimization solution of the whole classification problem. All experiments in public datasets show that our method greatly increases the training speed of RMCLP in the help of multiple processors.This work has been partially supported by China Postdoctoral Science Foundation under Grant No.2013M530702, and grants from National Natural Science Foundation of China(NO.11271361), key project of National Natural Science Foundation of China(NO.71331005), Major International (Regional) Joint Research Project(NO.71110107026), and the Ministry of water resources’ special funds for scientific research on public causes (No. 201301094).Peer ReviewedPostprint (published version

Collaborative Virtual Environment for Advanced Computing

Synchronous collaborative systems allow geographically distributed participants to form a virtual work environment enabling cooperation between peers and enriching the human interaction. The technology facilitating this interaction has been studied for several years and various solutions can be found at present. In this paper, we discuss our experiences with one such widely adopted technology, namely the Access Grid. We describe our experiences with using this technology, identify key problem areas and propose our solution to tackle these issues appropriately. Moreover, we propose the integration of Access Grid with an Application Sharing tool, developed by the authors. Our approach allows these integrated tools to utilise the enhanced features provided by our underlying dynamic transport layer

Towards Understanding Uncertainty in Cloud Computing Resource Provisioning

In spite of extensive research of uncertainty issues in different fields ranging from computational biology to decision making in economics, a study of uncertainty for cloud computing systems is limited. Most of works examine uncertainty phenomena in users’ perceptions of the qualities, intentions and actions of cloud providers, privacy, security and availability. But the role of uncertainty in the resource and service provisioning, programming models, etc. have not yet been adequately addressed in the scientific literature. There are numerous types of uncertainties associated with cloud computing, and one should to account for aspects of uncertainty in assessing the efficient service provisioning. In this paper, we tackle the research question: what is the role of uncertainty in cloud computing service and resource provisioning? We review main sources of uncertainty, fundamental approaches for scheduling under uncertainty such as reactive, stochastic, fuzzy, robust, etc. We also discuss potentials of these approaches for scheduling cloud computing activities under uncertainty, and address methods for mitigating job execution time uncertainty in the resource provisioning.Peer ReviewedPostprint (published version

A comprehensive sensitivity analisys of the WRF model for air quality applications over the Iberian Peninsula

Meteorological inputs play a vital role on regional air quality modelling. An extensive sensitivity analysis of the Weather Research and Forecasting (WRF) model was performed, in the framework of the Integrated Assessment Modelling System for the Iberian Peninsula (SIMCA) project. Up to 23 alternative model configurations, including Planetary Boundary Layer schemes, Microphysics, Land-surface models, Radiation schemes, Sea Surface Temperature and Four-Dimensional Data Assimilation were tested in a 3 km spatial resolution domain. Model results for the most significant meteorological variables, were assessed through a series of common statistics. The physics options identified to produce better results (Yonsei University Planetary Boundary Layer, WRF Single-Moment 6-class microphysics, Noah Land-surface model, Eta Geophysical Fluid Dynamics Laboratory longwave radiation and MM5 shortwave radiation schemes) along with other relevant user settings (time-varying Sea Surface Temperature and combined grid-observational nudging) where included in a “best case” configuration. This setup was tested and found to produce more accurate estimation of temperature, wind and humidity fields at surface level than any other configuration for the two episodes simulated. Planetary Boundary Layer height predictions showed a reasonable agreement with estimations derived from routine atmospheric soundings. Although some seasonal and geographical differences were observed, the model showed an acceptable behaviour overall. Despite being useful to define the most appropriate setup of the WRF model for air quality modelling over the Iberian Peninsula, this study provides a general overview of WRF sensitivity and can constitute a reference for future mesoscale meteorological modelling exercises

Sensitivity analysis of WRF for integrated assessment modelling in Spain

SIMCA (Air quality integrated assessment modelling system for the Iberian Peninsula) is a research project funded by the Spanish Ministry of Environment. Assessment and comparison of environmental policies and control Strategies. Multiscale and multipollutant approach Based on national projections from the Spain’s Emission Projection (SEP) project