Full metadata
Title
EdgeFaaS: A Function-based Framework for Edge Computing
Description
The rapid growth of data generated from Internet of Things (IoTs) such as smart phones and smart home devices presents new challenges to cloud computing in transferring, storing, and processing the data. With increasingly more powerful edge devices, edge computing, on the other hand, has the potential to better responsiveness, privacy, and cost efficiency. However, resources across the cloud and edge are highly distributed and highly diverse. To address these challenges, this paper proposes EdgeFaaS, a Function-as-a-Service (FaaS) based computing framework that supports the flexible, convenient, and optimized use of distributed and heterogeneous resources across IoT, edge, and cloud systems. EdgeFaaS allows cluster resources and individual devices to be managed under the same framework and provide computational and storage resources for functions. It provides virtual function and virtual storage interfaces for consistent function management and storage management across heterogeneous compute and storage resources. It automatically optimizes the scheduling of functions and placement of data according to their performance and privacy requirements. EdgeFaaS is evaluated based on two edge workflows: video analytics workflow and federated learning workflow, both of which are representative edge applications and involve large amounts of input data generated from edge devices.
Date Created
2021
Contributors
- Jin, Runyu (Author)
- Zhao, Ming (Thesis advisor)
- Shrivastava, Aviral (Committee member)
- Sarwat Abdelghany Aly Elsayed, Mohamed (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
53 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.168534
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2021
Field of study: Computer Science
System Created
- 2022-08-22 04:29:20
System Modified
- 2022-08-22 04:29:46
- 2 years 2 months ago
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