Efficient Resource Management For Video Applications In The Era Of Internet Of Things Iot
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Efficient Resource Management for Video Applications in the Era of Internet-of-Things (IoT)
The Internet-of-Things (IoT) is a network of interconnected devices with sensing, monitoring and processing functionalities that work in a cooperative way to offer services. Smart buildings, self-driving cars, house monitoring and management, city electricity and pollution monitoring are some examples where IoT systems have been already deployed. Amongst different kinds of devices in IoT, cameras play a vital role, since they can capture rich and resourceful content. However, since multiple IoT devices share the same gateway, the data that is produced from high definition cameras congest the network and deplete the available computational resources resulting in Quality-of-Service degradation corresponding to the visual content. In this thesis, we present an edge-based resource management framework for serving video processing applications in an Internet-of-Things (IoT) environment. In order to support the computational demands of latency-sensitive video applications and utilize effectively the available network resources, we employ edge-based resource management policy. We evaluate our proposed framework with a face recognition use case.
Resource Management and Efficiency in Cloud Computing Environments
Today’s advancements in technology have brought about a new era of speed and simplicity for consumers and businesses. Due to these new benefits, the possibilities of universal connectivity, storage and computation are made tangible, thus leading the way to new Internet-of Things solutions. Resource Management and Efficiency in Cloud Computing Environments is an authoritative reference source for the latest scholarly research on the emerging trends of cloud computing and reveals the benefits cloud paths provide to consumers. Featuring coverage across a range of relevant perspectives and topics, such as big data, cloud security, and utility computing, this publication is an essential source for researchers, students and professionals seeking current research on the organization and productivity of cloud computing environments.
DATA PROCESSING AT THE EDGE
Author: Dr. Aadam Quraishi MD
language: en
Publisher: Xoffencer International Book Publication House
Release Date: 2024-12-30
It is possible that the idea of situating computing resources in close proximity to the sources of data is not that original. Serve apps and move them from the cloud data center to the network edge. This is something that is advised from a business point of view. It was in the year 2002 when the term "edge computing" was first used to characterize this development. When the phrase was first used in 2004, it was used to describe a system that enhanced the performance of the system by distributing program approaches and data to the edge of the network. End users were able to take advantage of elastic resources located at the network's peripheral, which allowed edge computing to finally overcome many of the problems that cloud computing had. Previously, cloud computing could only provide resources that were distributed and hosted on cloud data centers located in the heart of the network. This was the reason for this limitation. This section provides an overview of edge computing, defines what it is, covers some popular use cases, and outlines potential challenges that may arise during the design and implementation of edge computing systems. Additionally, this section provides an explanation of what edge computing is. This article not only provides an overview of potential future work in related approaches, but it also highlights a few opportunities and challenges that may arise in the future.