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Optimal Fog Services Placement in SDN IoT Network Using Random Neural Networks and Cognitive Network Map

机译:使用随机神经网络和认知网络地图在SDN IOT网络中的最佳迷雾服务

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Due to a massive increase in the number of IoT devices and the number of cloud-based services a crucial task arises of optimally placing (both topologically and resource-wise) services in the network so that no of the clients will be victimized and all of them will receive the best possible time of response. Also - there must be a balance not to instantiate a service on every possible machine - which would take too many resources. The task which must be solved is an optimization of parameters such as QoS between service and client, equality of clients and usage of resources. Using the SDN - which is designed to answer some of the problems posed in this section such as QoS and knowledge about the topology of the whole network and newly connected clients -is a gateway to better-adapted service management. Machine learning provides less stiff rules to follow and more intelligent behavior of the manager.
机译:由于IOT设备数量的大幅增加和基于云的服务的数量,所以在网络中最佳地放置(拓扑和资源和资源和资源和资源方向)服务的关键任务,因此没有客户将成为受害者他们将获得最佳的响应时间。另外 - 必须有一个余额,不能在每个可能的机器上实例化服务 - 这将需要太多资源。必须解决的任务是优化参数,例如服务和客户之间的QoS,客户的平等和资源的使用。使用SDN - 旨在回答本节中的一些问题,例如QoS和关于整个网络拓扑的QoS和新连接的客户端 - 用于更好适应的服务管理的网关。机器学习提供了不太僵硬的规则,以跟随经理的更聪明的行为。

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