RESOURCE MANAGEMENT AND ALLOCATION IN FOG COMPUTING

Dr Radha Karampudi
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Abstract

Smart objects are increasingly playing a crucial role in the daily operations of both industries and individuals. These devices collect data through various apps and sensors, leading to a significant accumulation of information across various sectors. The use of smart objects has grown exponentially with the advent of the Internet of Things (IoT). This has led to a significant increase in the amount of data being generated, including both structured and unstructured data. However, there are currently no effective ways to manage this data. Despite the significant advancements made in the field of IoT, incorporating cloud computing is still facing challenges such as latency, performance, network and security concerns of computing can address the challenges faced by cloud computing in the context of the Internet of Things (IoT) by bringing the cloud closer to the edge. The primary objective of fog computing is to process and store data collected by IoT devices locally on a fog node, rather than transmitting it to a remote cloud server. This approach results in faster response times and better quality of services compared to cloud computing. Fog computing is an effective solution to enable the IoT to provide reliable and secure services to a large number of IoT customers. Fog computing allows for the management of service and resource provisioning from outside of cloud computing, closer to devices, at the edge networks, or at locations specified by Service Level Agreements (SLAs). It is not intended to replace cloud computing, but rather to enhance it by enabling computation at the edge while still providing access to cloud data centers. It covers various computing frameworks, fog computing features, a comprehensive reference architectural style of fog with its multiple levels, a comprehensive study of fog with IoT, various fog system methodologies, and a thorough evaluation of the challenges in fog computing, which also serves as a middle layer between IoT sensors or devices and cloud data centers.
雾计算中的资源管理与分配
智能对象在行业和个人的日常操作中发挥着越来越重要的作用。这些设备通过各种应用程序和传感器收集数据,从而在各个部门积累大量信息。随着物联网(IoT)的出现,智能对象的使用呈指数级增长。这导致生成的数据量显著增加,包括结构化和非结构化数据。然而,目前还没有有效的方法来管理这些数据。尽管在物联网领域取得了重大进展,但整合云计算仍然面临诸如计算的延迟、性能、网络和安全问题等挑战,可以通过使云更接近边缘来解决物联网(IoT)背景下云计算所面临的挑战。雾计算的主要目标是在雾节点上处理和存储物联网设备本地收集的数据,而不是将其传输到远程云服务器。与云计算相比,这种方法可以带来更快的响应时间和更好的服务质量。雾计算是一种有效的解决方案,可以使物联网为大量的物联网客户提供可靠、安全的服务。雾计算允许从云计算外部、更靠近设备、边缘网络或服务水平协议(sla)指定的位置管理服务和资源供应。它的目的不是取代云计算,而是通过在边缘支持计算,同时仍然提供对云数据中心的访问来增强云计算。它涵盖了各种计算框架,雾计算特性,雾的综合参考架构风格及其多层次,雾与物联网的综合研究,各种雾系统方法,以及对雾计算挑战的全面评估,也作为物联网传感器或设备与云数据中心之间的中间层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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