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What is End-to-End Latency

Handbook of Research on Big Data and the IoT
In the context of cloud services, it refers to the time that takes to receive the response of a service across the network once it is triggered. In the context of IoT and cloud services, it includes the network delay of transmitting the data to the cloud, the processing time of the cloud service and finally the network transport delay of transmitting the information to the IoT system.
Published in Chapter:
IoT Big Data Architectures, Approaches, and Challenges: A Fog-Cloud Approach
David Sarabia-Jácome (Universitat Politècnica de València (UPV), Spain), Regel Gonzalez-Usach (Universitat Politècnica de València (UPV), Spain), and Carlos E. Palau (Universitat Politècnica de València (UPV), Spain)
Copyright: © 2019 |Pages: 24
DOI: 10.4018/978-1-5225-7432-3.ch008
The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.
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