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Quantifying the Impact of Edge Computing on Mobile Applications
Open accessAbstract
Computational offloading services at the edge of the Internet for mobile devices are becoming a reality. Using a wide range of mobile applications, we explore how such infrastructure improves latency and energy consumption relative to the cloud. We present experimental results from WiFi and 4G LTE networks that confirm substantial wins from edge computing for highly interactive mobile applications.
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“many new types of applications, e.g., cooperative autonomous driving, have strict or tight delay requirements that the cloud would have difficulty meeting since it may be far away from the users [4].”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [4]
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Metadata
- Authors
- Hu, W., Gao, Y., Ha, K., Wang, J., Amos, B., Chen, Z., Pillai, P., & Satyanarayanan, M.
- Affiliations
- Carnegie Mellon University (US)
- Intel (United States) (US)
- Venue
- Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems
- Publisher
- ACM
- Published
- 2016-08-04
- Pages
- 1-8
- OpenAlex
- W2508388616
- Cited by
- 171
- References
- 18
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 13 · 2022: 20 · 2021: 23 · 2020: 24 · 2019: 30 · 2018: 25 · 2017: 20
- FWCI
- 21.36
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGOPS Asia-Pacific Workshop on Systems (APSys) (publishedVersion)
- Funders
- National Science Foundation (CNS-1518865)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- edge computing, computation offloading, mobile application, latency, energy consumption, cloud computing, Wi-Fi, 4G LTE, mobile devices
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