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Towards self-optimisation in fog computing environments

机译:朝向雾计算环境中的自我优化

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摘要

In recent years, the number of smart devices has grown exponentially. However, the computational demand for latency-sensitive applications has grown and the traditional model of cloud computing is no longer able to meet alone all the needs of this type of application. In this direction, a new paradigm of computation was introduced, it is called Fog Computing. Many challenges need to be overcome, especially those regarding issues such as security, power consumption, high latency in communication with critical IoT applications, and need for QoS. We have presented a container migration mechanism to Fog and the cloud computing that supports the implementation of optimisation strategies to achieve different objectives and solutions to problems of resources allocation. In addition, our work emphasises the performance and latency optimisation, through an autonomic architecture based on the MAPE-K control loop, and provides a foundation for the analysis and optimisation architectures design to support IoT applications.
机译:近年来,智能设备的数量是指数增长的。但是,对潜伏敏感应用的计算需求已经增长,传统的云计算模型不再能够独自满足这种类型应用的所有需求。在这个方向上,引入了一个新的计算范式,它被称为雾计算。需要克服许多挑战,特别是关于安全,功耗,与关键IOT应用的通信中的高延迟等问题的挑战,以及需要QoS的问题。我们向FOG和云计算提出了一个集装箱迁移机制,支持实施优化策略,以实现不同的目标和解决资源分配问题的解决方案。此外,我们的工作强调了通过基于MAPE-K控制循环的自主体系结构的性能和延迟优化,并为分析和优化架构设计提供了支持IOT应用程序的基础。

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