Network Decontamination from a Black Virus

Jie Cai, P. Flocchini, N. Santoro
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引用次数: 11

Abstract

In this paper, we consider the problem of decontaminating a network from a black virus (BV) using a team of mobile system agents. The BV is a harmful process which, like the extensively studied black hole (BH), destroys any agent arriving at the network site where it resides; when that occurs, unlike a black hole which is static by definition, a BV moves, spreading to all the neighboring sites, thus increasing its presence in the network. If however one of these sites contains a system agent, that clone of the BV is destroyed (i.e., removed permanently from the system). The initial location of the BV is unknown a priori. The objective is to permanently remove any presence of the BVfrom the network with minimum number of site infections (and thus casualties). The main cost measure is the total number of agents needed to solve the problem. This problem integrates in its definition both the harmful aspects of the classical black hole search problem (where however the dangerous elements are static) with the mobility aspects of the classical intruder capture or network decontamination problem(where however there is no danger for the agents). Thus, it is the first attempt to model mobile intruders harmful not only forth sites but also for the agents. We start the study of this problem by focusing on some important classes of interconnection networks: grids, tori, and hypercubes. For each class we present solution protocols and strategies for the team of agents, analyze their worst case complexity, and prove their optimality.
从黑色病毒中清除网络污染
在本文中,我们考虑了使用一组移动系统代理对网络进行黑病毒(BV)净化的问题。BV是一个有害的过程,就像被广泛研究的黑洞(BH)一样,它会摧毁任何到达它所在网络位置的代理;当这种情况发生时,与定义上静止的黑洞不同,BV会移动,扩散到所有邻近的位置,从而增加它在网络中的存在。但是,如果其中一个站点包含系统代理,则该BV的克隆将被销毁(即从系统中永久删除)。BV的初始位置是先验未知的。目标是以最少的站点感染数量(从而减少人员伤亡)永久地从网络中移除任何bv的存在。主要的成本度量是解决问题所需的代理总数。该问题在其定义中集成了经典黑洞搜索问题的有害方面(然而危险元素是静态的)和经典入侵者捕获或网络净化问题的移动性方面(然而对代理没有危险)。因此,这是第一次尝试模拟移动入侵者不仅对网站有害,而且对代理也有害。我们从关注一些重要的互连网络类别开始研究这个问题:网格、环面和超立方体。对于每个类,我们给出了智能体团队的解决方案和策略,分析了它们的最坏情况复杂度,并证明了它们的最优性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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