Exploring Cloud-Based Distributed Disaster Management With Dynamic Multi-Agents Workflow System

Author:

Habiba Mansura1,Akhter Shamim2

Affiliation:

1. AIUB, Bangladesh

2. East West University, Bangladesh

Abstract

Natural disaster is one of the important topics in current researches. Disaster Management System (DMS) is a complex system and needs to perform a collection of tasks collaboratively along with the potentiality to change the configurations of the system dynamically. In the research era of workflow model, existing models mainly deal with temporal and static constrains. However they cannot be used to keep pace with an uncertainly dynamic system like disaster management. Considering all these significant DMS attributes we have designed a new dynamically configurable and changeable workflow model with the support of adaptive scheduling, for both successful and failed situations, and implemented in a distributed cloud system to maintain the rescue and reorganization activities of disaster situation. In order to simplify the system architecture, we have used Multi Agent System (MAS) for our design. The proposed system achieves a comparatively higher rate of successful job completion-higher rescheduling success rate and comparatively lower dropout rate.

Publisher

IGI Global

Reference34 articles.

1. Akhter, M. (2005a). Implementing the SWAP-GA model in cluster computers. Asian Institute of Technology. MSc.Thesis no. CS-05-11.

2. Akhter, S., Jangjaimon, I., Chemin, Y., Uthayopas, P., & Honda, K. (2006). Development of a GRIDRPC tool for Satellite Images Parallel Data Assimilation in Agricultural Monitoring. International Journal of Geoinformatics, 2(3).

3. Akhter, S., Rahman, M. R., & Islam, A. (2016b). Neural Network (NN) Based Route Weight Computation for Bi-Directional Traffic Management System. International Journal of Applied Evolutionary Computation, 7(4).

4. An Analytical Overview. (2007). Asian Disaster Reduction Center.

5. ArcGIS. (2012). GIS Tool. Retrieved from https://www.arcgis.com/features/index.html

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3