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Smart Execution Strategy Selection for Multi Tier Execution in Named Function Networking

机译:命名功能网络中多层执行的智能执行策略选择

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Named Function Networking was designed to perform computations inside Information Centric Networks, whereas the network decides where to execute a computation depending on the input data. However, this decision is based on fixed forwarding strategies. Initially, the forwarding strategies aimed to execute computations deep inside the network satisfying the requirements of data centers and cloud computing facing large input data and computational intensive operations. Data are produced by content providers, later processed and delivered from the cloud towards the clients. Recently, this characteristic changed as Internet of Things (IoT) devices entered the game. Sensors for home automation, autonomous cars, etc generate new data, at the edge of the network. To respond to those developments, forwarding strategy can be modified to perform computations at the edge of the network to provide low latency and to reduce the traffic to the core of the network. However, this reduces the capabilities of the network to choose the execution location and a computation may require input data from the edge as well as from data centers. For those cases, neither computing at the edge nor in the cloud is sufficient. By enabling NFN nodes to decompose computations and to choose a forwarding strategy per subcomputation, edge computing nodes can offload parts of the computation into the network for execution inside data centers.
机译:命名功能网络旨在在信息中心网络内部执行计算,而网络则根据输入数据来决定在何处执行计算。但是,此决定基于固定的转发策略。最初,转发策略旨在执行深入网络内部的计算,以满足数据中心和面向大型输入数据和计算密集型操作的云计算的需求。数据由内容提供商生成,然后进行处理,然后从云中传输到客户端。最近,随着物联网(IoT)设备进入游戏,这一特征发生了变化。用于家庭自动化,自动驾驶汽车等的传感器会在网络边缘生成新数据。为了响应这些发展,可以修改转发策略以在网络边缘执行计算,以提供低延迟并减少到网络核心的流量。然而,这降低了网络选择执行位置的能力,并且计算可能需要来自边缘以及来自数据中心的输入数据。对于这些情况,在边缘或在云中进行计算都不够。通过使NFN节点能够分解计算并为每个子计算选择转发策略,边缘计算节点可以将部分计算卸载到网络中以在数据中心内执行。

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