Poster Abstract: Automatic Deployment Right-Sizing Through Hyperparameter Optimization
Pages 438 - 439
Abstract
Internet of Things (IoT) and Edge deployments are diverse, complex, and highly constrained. These properties make correctness difficult or impossible to verify a priori. We present early work on an automatic deployment right-sizing tool for edge and IoT deployments. Our tool uses the PROWESS testbed to accurately emulate candidate deployment form-factors, and optimizes deployment parameters to minimize costs. We show that our early work finds optimal deployment configurations 6.3X faster than Bayesian optimization, a state of the art hyperparameter optimization technique.
References
[1]
James Bergstra, Brent Komer, Chris Eliasmith, Dan Yamins, and David D Cox. 2015. Hyperopt: a python library for model selection and hyperparameter optimization. Computational Science & Discovery 8, 1 (2015), 014008.
[2]
Mark Berman, Jeffrey S Chase, Lawrence Landweber, Akihiro Nakao, Max Ott, Dipankar Raychaudhuri, Robert Ricci, and Ivan Seskar. 2014. GENI: A federated testbed for innovative network experiments. Computer Networks 61 (2014), 5–23.
[3]
Jayson Boubin, Avishek Banerjee, Jihoon Yun, Haiyang Qi, Yuting Fang, Steve Chang, Kannan Srinivasan, Rajiv Ramnath, and Anish Arora. 2022. PROWESS: An Open Testbed for Programmable Wireless Edge Systems. In Practice and Experience in Advanced Research Computing. 1–9.
[4]
Jayson Boubin, Codi Burley, Peida Han, Bowen Li, Barry Porter, and Christopher Stewart. 2022. MARbLE: Multi-Agent Reinforcement Learning at the Edge for Digital Agriculture. In Proceedings of the 7th ACM/IEEE Symposium on Edge Computing.
[5]
Jayson G Boubin, Naveen TR Babu, Christopher Stewart, John Chumley, and Shiqi Zhang. 2019. Managing edge resources for fully autonomous aerial systems. In Proceedings of the 4th ACM/IEEE Symposium on Edge Computing. ACM, 74–87.
[6]
Piotr Czyzżak and Adrezej Jaszkiewicz. 1998. Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. Journal of multi-criteria decision analysis 7, 1 (1998), 34–47.
[7]
Kate Keahey, Jason Anderson, Michael Sherman, Cody Hammock, Zhuo Zhen, Jenett Tillotson, Timothy Bargo, Lance Long, Taimoor Ul Islam, Sarath Babu, 2022. CHI-in-a-Box: Reducing Operational Costs of Research Testbeds. In Practice and Experience in Advanced Research Computing. 1–8.
[8]
Kate Keahey, Jason Anderson, Zhuo Zhen, Pierre Riteau, Paul Ruth, Dan Stanzione, Mert Cevik, Jacob Colleran, Haryadi S Gunawi, Cody Hammock, 2020. Lessons learned from the chameleon testbed. In 2020 USENIX annual technical conference (USENIX ATC 20). 219–233.
[9]
Chengyi Qu, Jayson Boubin, Durbek Gafurov, Jianfeng Zhou, Noel Aloysius, henry Nguyen, and Prasad Calyam. 2022. UAV Swarms in Smart Agriculture: Experiences and Opportunities. In 2022 IEEE International Conference on eScience. IEEE.
[10]
Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016. Edge computing: Vision and challenges. IEEE internet of things journal 3, 5 (2016), 637–646.
Index Terms
- Poster Abstract: Automatic Deployment Right-Sizing Through Hyperparameter Optimization
Recommendations
Right-Provisioned IoT Edge Computing: An Overview
GLSVLSI '19: Proceedings of the 2019 Great Lakes Symposium on VLSIEdge computing on the Internet of Things (IoT) is an increasingly popular paradigm in which computation is moved closer to the data source (i.e., edge devices). Edge computing mitigates the overheads of cloud-based computing arising from increased ...
Optimisation of the public key encryption infrastructure for the internet of things
ICFNDS '18: Proceedings of the 2nd International Conference on Future Networks and Distributed SystemsThe Internet of Things (IoT) faces unique security challenges due to its resource constrained devices. Limited processing, storage, memory and power of IoT objects do not support the deployment of advanced security protocols, which are often resource ...
Deployment optimization of IoT devices through attack graph analysis
WiSec '19: Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile NetworksThe Internet of things (IoT) has become an integral part of our life at both work and home. However, these IoT devices are prone to vulnerability exploits due to their low cost, low resources, the diversity of vendors, and proprietary firmware. Moreover,...
Comments
Information & Contributors
Information
Published In
May 2023
514 pages
ISBN:9798400700378
DOI:10.1145/3576842
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 May 2023
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Funding Sources
Conference
IoTDI '23
Sponsor:
IoTDI '23: International Conference on Internet-of-Things Design and Implementation
May 9 - 12, 2023
TX, San Antonio, USA
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 48Total Downloads
- Downloads (Last 12 months)20
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format