后图灵计算、分层命名网络和一类新的边缘计算

Rao V. Mikkilineni, G. Morana
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引用次数: 4

摘要

我们对所有生物(细胞生物、动物、植物和人类)的无数形式的认知本质的理解的进步,以及信息、信息计算和知识的新理论,为我们如何在数字宇宙中构建软件系统提供了启示,这些系统可以模仿物理宇宙中智能、有感觉和有弹性的生物,并与之互动。最近试图将认知注入计算系统以突破丘奇-图灵理论的界限,已经产生了新的计算模型,这些模型使用存储程序控制机器和神经网络中执行的算法来模拟生物系统编码知识结构。本文提出了一种新的模型,并通过对微服务网络进行动态配置和重构,实现了一个由微服务组成的分层命名网络来创建可管理流程工作流的应用。我们使用Platina Systems的新型边缘云平台展示了命名微服务网络的弹性、效率和可扩展性。该平台消除了对虚拟机覆盖的需求,并提供基于L3的100 GbE网络的高性能和低延迟,以及支持RDMA和NVMeoE的SSD。使用Kubernetes配置堆栈的分层命名微服务网络提供了所有的云特性,如弹性、自动扩展、自我修复和无需重新启动的实时迁移。该模型来源于最近使用“结构机器”统一不同计算模型的理论框架。它们被证明可以模拟图灵机、感应图灵机,并且比图灵机更有效。具有管理命名服务连接的控制器层次结构的结构化机器框架提供了从浏览器到数据库的服务网络的动态重新配置,以处理对资源的需求或可用性的快速波动,而无需重新配置IP地址基础网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Post-Turing Computing, Hierarchical Named Networks and a New Class of Edge Computing
Advances in our understanding of the nature of cognition in its myriad forms (Embodied, Embedded, Extended, and Enactive) displayed in all living beings (cellular organisms, animals, plants, and humans) and new theories of information, info-computation and knowledge are throwing light on how we should build software systems in the digital universe which mimic and interact with intelligent, sentient and resilient beings in the physical universe. Recent attempts to infuse cognition into computing systems to push the boundaries of Church-Turing thesis have led to new computing models that mimic biological systems in encoding knowledge structures using both algorithms executed in stored program control machines and neural networks. This paper presents a new model and implements an application as hierarchical named network composed of micro-services to create a managed process workflow by enabling dynamic configuration and reconfiguration of the micro-service network. We demonstrate the resiliency, efficiency and scaling of the named microservice network using a novel edge cloud platform by Platina Systems. The platform eliminates the need for Virtual Machine overlay and provides high performance and low-latency with L3 based 100 GbE network and SSD support with RDMA and NVMeoE. The hierarchical named microservice network using Kubernetes provisioning stack provides all the cloud features such as elasticity, auto-scaling, self-repair and live-migration without reboot. The model is derived from a recent theoretical framework for unification of different models of computation using "Structural Machines." They are shown to simulate Turing machines, inductive Turing machines and also are proved to be more efficient than Turing machines. The structural machine framework with a hierarchy of controllers managing the named service connections provides dynamic reconfiguration of the service network from browsers to database to address rapid fluctuations in the demand for or the availability of resources without having to reconfigure IP address base networks.
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