It is yet unclear whether large-scale segregation of immiscibile liquids and eruption of high-Si lavas exist in nature(Charlier et al.,2013).We present a possible case of segregation of immscible liquids in the 1780 M...It is yet unclear whether large-scale segregation of immiscibile liquids and eruption of high-Si lavas exist in nature(Charlier et al.,2013).We present a possible case of segregation of immscible liquids in the 1780 Ma Taihang展开更多
In this paper, we propose MAMNID, a mobile agent-based model for networks incidents diagnosis. It is a load-balance and resistance to attack model, based on mobile agents to mitigate the weaknesses of centralized syst...In this paper, we propose MAMNID, a mobile agent-based model for networks incidents diagnosis. It is a load-balance and resistance to attack model, based on mobile agents to mitigate the weaknesses of centralized systems like that proposed by Mohamed Eid which consists in gathering data to diagnose from their collecting point and sending them back to the main station for analysis. The attack of the main station stops the system and the increase of the amount of information can equally be at the origin of bottlenecks or DDoS in the network. Our model is composed of m diagnostiquors, n sniffers and a multi-agent system (MAS) of diagnosis management of which the manager is elected in a cluster. It has enabled us not only to reduce the response time and the global system load by 1/m, but also make the system more tolerant to attacks targeting the diagnosis system.展开更多
文摘It is yet unclear whether large-scale segregation of immiscibile liquids and eruption of high-Si lavas exist in nature(Charlier et al.,2013).We present a possible case of segregation of immscible liquids in the 1780 Ma Taihang
文摘In this paper, we propose MAMNID, a mobile agent-based model for networks incidents diagnosis. It is a load-balance and resistance to attack model, based on mobile agents to mitigate the weaknesses of centralized systems like that proposed by Mohamed Eid which consists in gathering data to diagnose from their collecting point and sending them back to the main station for analysis. The attack of the main station stops the system and the increase of the amount of information can equally be at the origin of bottlenecks or DDoS in the network. Our model is composed of m diagnostiquors, n sniffers and a multi-agent system (MAS) of diagnosis management of which the manager is elected in a cluster. It has enabled us not only to reduce the response time and the global system load by 1/m, but also make the system more tolerant to attacks targeting the diagnosis system.
