A site selection method of DNS using the particle swarm optimization algorithm

Abstract

The Domain Name System (DNS) is an essential component of the functionality of the Internet. With the growing number of domain names and Internet users, the growing rate and number of visit quantity and analytic capacity of DNS are also proportional to the Internet users' size. This study (based on the analysis of access popularity and the distribution of massive DNS log data) aims to optimize the configuration of the DNS sites, which has become an important problem.The ArcGIS software is used to show the temporal and spatial distributions of visit source of DNS logs. This study also analyzes the influence of different sites and the dependence on DNS service in different regions of the world. This information is important to further decision-making on new DNS site selection. This article proposes new DNS site selection solutions, using particle swarm and multi-objective particle swarm optimization algorithms for one new site and multiple sites, respectively. The results from particle swarm optimization, genetic, and simulated annealing algorithms were compared and experimental results confirmed the correctness and effectiveness of the proposed methods. The proposed methods could also be extended to solve other layout related issues, such as onsite facility layout and road network optimization. K E Y W O R D SDNS logs, genetic, multi-objective particle swarm and particle swarm optimization algorithms, site selection methods | I NTR OD U CTI ONThe Domain Name System (DNS) is a hierarchical distributed naming system for computers, services, or any resource connected to the Internet or a private network (Klensin, 2003). The DNS can easily translate the memorized domain names to numerical IP addresses, which is required to locate computer services and devices worldwide. In an area with multiple servers, the purpose of this process is to improve the reliability of the DNS. When a domain name server fails, Transactions in GIS. 2017;21:969-983.wileyonlinelibrary.com/journal/tgis

Spatial Allocation Method of Evacuation Guiders in Urban Open Public Spaces: A Case Study of Binjiang Green Space in Xuhui District, Shanghai, China

“…For optimizing the spatial allocation of evacuation guiders, the main factors, such as the number of guiders, the guiding range of guiders and the spatial location of guiders, are generally considered, which have a high similarity to the spatial allocation of public facilities. Currently, the Lagrangian relaxation algorithm [22], greed algorithm [23], genetic algorithm [24,25], simulated annealing algorithm [26], ant colony algorithm [27,28], Particle Swarm Optimization (PSO) algorithm [29][30][31][32][33] and other heuristic algorithms as well as the multi-objective evolutionary algorithm [34] are commonly used in the research of public facility allocation problems. Heuristic algorithms can integrate qualitative and quantitative problems well and meet the needs of multi-objective decision making.…”