Edge/Fog Computing Technologies for IoT Infrastructure
The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data c...
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (231 p.) |
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| 100 | 1 | |a Yoo, Seong-eun |4 edt | |
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| 300 | |a 1 electronic resource (231 p.) | ||
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| 520 | |a The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies. | ||
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| 653 | |a Prometheus | ||
| 653 | |a resource metrics | ||
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| 653 | |a task allocation | ||
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| 653 | |a task scheduling | ||
| 653 | |a markov decision process (MDP) | ||
| 653 | |a deep reinforcement learning (DRL) | ||
| 653 | |a resource management | ||
| 653 | |a algorithm classification | ||
| 653 | |a evaluation framework | ||
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| 653 | |a IoT actor | ||
| 653 | |a data manager | ||
| 653 | |a GDPR | ||
| 653 | |a computing | ||
| 653 | |a computational offloading | ||
| 653 | |a dynamic offloading threshold | ||
| 653 | |a minimizing delay | ||
| 653 | |a minimizing energy consumption | ||
| 653 | |a maximizing throughputs | ||
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| 700 | 1 | |a Kim, Taehong |4 edt | |
| 700 | 1 | |a Kim, Youngsoo |4 edt | |
| 700 | 1 | |a Yoo, Seong-eun |4 oth | |
| 700 | 1 | |a Kim, Taehong |4 oth | |
| 700 | 1 | |a Kim, Youngsoo |4 oth | |
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