The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption
| Autor: | Francesco Tusa, Stuart Clayman |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Distributed computing 020207 software engineering Cloud computing 02 engineering and technology Microservices JSON Resource (project management) Hardware and Architecture Analytics Encoding (memory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Enhanced Data Rates for GSM Evolution business computer Software Edge computing Information Systems computer.programming_language |
| Zdroj: | Journal of Grid Computing. 19 |
| ISSN: | 1572-9184 1570-7873 |
| DOI: | 10.1007/s10723-021-09577-9 |
| Popis: | Edge microservice applications are becoming a viable solution for the execution of real-time IoT analytics, due to their rapid response and reduced latency. With Edge Computing, unlike the central Cloud, the amount of available resource is constrained and the computation that can be undertaken is also limited. Microservices are not standalone, they are devised as a set of cooperating tasks that are fed data over the network through specific APIs. The cost of processing these feeds of data in real-time, especially for massive IoT configurations, is however generally overlooked. In this work we evaluate the cost of dealing with thousands of sensors sending data to the edge with the commonly used encoding of JSON over REST interfaces, and compare this to other mechanisms that use binary encodings as well as streaming interfaces. The choice has a big impact on the microservice implementation, as a wrong selection can lead to excessive resource consumption, because using a less efficient encoding and transport mechanism results in much higher resource requirements, even to do an identical job. |
| Databáze: | OpenAIRE |
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