ExPECA: An Experimental Platform for Trustworthy Edge Computing Applications
Authors: 
Samie Mostafavi, Vishnu Narayanan Moothedath, Stefan Ronngren, 
Neelabhro Roy, Gourav Prateek Sharma, Sangwon Seo, Manuel Olguin Munoz, 
James GrossAuthors Info & Claims
Samie Mostafavi, Vishnu Narayanan Moothedath, Stefan Ronngren, Neelabhro Roy, Gourav Prateek Sharma, Sangwon Seo, Manuel Olguin Munoz, James GrossAuthors Info & ClaimsPages 294 - 299
Abstract
This paper presents ExPECA, an edge computing and wireless communication research testbed designed to tackle two pressing challenges: comprehensive end-to-end experimentation and high levels of experimental reproducibility. Leveraging OpenStack-based Chameleon Infrastructure (CHI) framework for its proven flexibility and ease of operation, ExPECA is located in a unique, isolated underground facility, providing a highly controlled setting for wireless experiments. The testbed is engineered to facilitate integrated studies of both communication and computation, offering a diverse array of Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and wired links, as well as containerized computational environments. We exemplify the experimental possibilities of the testbed using OpenRTiST, a latency-sensitive, bandwidth-intensive application, and analyze its performance. Lastly, we highlight an array of research domains and experimental setups that stand to gain from ExPECA's features, including closed-loop applications and time-sensitive networking.
References
[1]
Ghina Al-Atat, Andrea Fresa, Adarsh Prasad Behera, Vishnu Narayanan Moothedath, James Gross, and Jaya Prakash Champati. 2023. The Case for Hierarchical Deep Learning Inference at the Network Edge. In Proceedings of the 1st International Workshop on Networked AI Systems (Helsinki, Finland) (NetAISys '23). Association for Computing Machinery, New York, NY, USA, Article 3, 6 pages.
[2]
Joe Breen, Andrew Buffmire, Jonathon Duerig, Kevin Dutt, Eric Eide, Mike Hibler, David Johnson, Sneha Kumar Kasera, Earl Lewis, Dustin Maas, Alex Orange, Neal Patwari, Daniel Reading, Robert Ricci, David Schurig, Leigh B. Stoller, Jacobus Van der Merwe, Kirk Webb, and Gary Wong. 2020. POWDER: Platform for Open Wireless Data-driven Experimental Research. In Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WiNTECH).
[3]
Hongchang Gao, An Xu, and Heng Huang. 2021. On the Convergence of Communication-Efficient Local SGD for Federated Learning. Proceedings of the AAAI Conference on Artificial Intelligence 35, 9 (May 2021), 7510--7518.
[4]
Shilpa George, Thomas Eiszler, Roger Iyengar, Haithem Turki, Ziqiang Feng, Junjue Wang, Padmanabhan Pillai, and Mahadev Satyanarayanan. 2020. OpenRTiST: End-to-End Benchmarking for Edge Computing. IEEE Pervasive Computing 19, 4 (2020), 10--18.
[5]
Kiryong Ha, Zhuo Chen, Wenlu Hu, Wolfgang Richter, Padmanabhan Pillai, and Mahadev Satyanarayanan. 2014. Towards Wearable Cognitive Assistance. In Proc. of the 12th Annual International Conference on Mobile Systems, Applications, and Services. Association for Computing Machinery, 68--81.
[6]
Kate Keahey, Jason Anderson, Michael Sherman, Cody Hammock, Zhuo Zhen, Jenett Tillotson, Timothy Bargo, Lance Long, Taimoor Ul Islam, Sarath Babu, Hongwei Zhang, and François Halbach. 2022. CHI-in-a-Box: Reducing Operational Costs of Research Testbeds. In Practice and Experience in Advanced Research Computing (Boston, MA, USA) (PEARC '22). Association for Computing Machinery, New York, NY, USA, Article 14, 8 pages.
[7]
Kate Keahey, Jason Anderson, Zhuo Zhen, Pierre Riteau, Paul Ruth, Dan Stanzione, Mert Cevik, Jacob Colleran, Haryadi S. Gunawi, Cody Hammock, Joe Mambretti, Alexander Barnes, François Halbach, Alex Rocha, and Joe Stubbs. 2020. Lessons Learned from the Chameleon Testbed. In Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC '20). USENIX Association.
[8]
Ce Liu, Antonio Torralba, William T Freeman, Frédo Durand, and Edward H Adelson. 2005. Motion magnification. ACM transactions on graphics (TOG) 24, 3 (2005), 519--526.
[9]
Bin Lu, Yaoyu Li, Xin Wu, and Zhongzhou Yang. 2009. A review of recent advances in wind turbine condition monitoring and fault diagnosis. In IEEE Power Electronics and Machines in Wind Applications. 1--7.
[10]
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 54). PMLR, 1273--1282. https://proceedings.mlr.press/v54/mcmahan17a.html
[11]
Vishnu Narayanan Moothedath, Jaya Prakash Champati, and James Gross. 2023. Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge. arXiv:2304.00891 [cs.LG]
[12]
Samie Mostafavi, Gourav Prateek Sharma, and James Gross. 2023. Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities. arXiv preprint arXiv:2307.10648 (2023).
[13]
Tae-Hyun Oh, Ronnachai Jaroensri, Changil Kim, Mohamed Elgharib, Fr'edo Durand, William T Freeman, and Wojciech Matusik. 2018. Learning-based video motion magnification. In Proceedings of the European Conference on Computer Vision (ECCV). 633--648.
[14]
Manuel Olguín Muñoz, Neelabhro Roy, and James Gross. 2022. CLEAVE: Scalable and Edge-Native Benchmarking of Networked Control Systems. In Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking (Rennes, France) (EdgeSys '22). Association for Computing Machinery, New York, NY, USA, 37--42.
[15]
Manuel Olguín Muñoz. 2023. An Emulation-Based Performance Evaluation Methodology for Edge Computing and Latency Sensitive Applications. Ph.D. Dissertation. KTH Royal Institute of Technology. https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-327222
[16]
Jake Perazzone, Shiqiang Wang, Mingyue Ji, and Kevin S. Chan. 2022. Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization. In IEEE INFOCOM 2022 - IEEE Conference on Computer Communications. 1449--1458.
[17]
Dipankar Raychaudhuri, Ivan Seskar, Gil Zussman, Thanasis Korakis, Dan Kilper, Tingjun Chen, Jakub Kolodziejski, Michael Sherman, Zoran Kostic, Xiaoxiong Gu, Harish Krishnaswamy, Sumit Maheshwari, Panagiotis Skrimponis, and Craig Gutterman. 2020. Challenge: COSMOS: A City-Scale Programmable Testbed for Experimentation with Advanced Wireless. Association for Computing Machinery, New York, NY, USA.
[18]
Gourav Prateek Sharma, Dhruvin Patel, Joachim Sachs, Marilet De Andrade, Janos Farkas, Janos Harmatos, Balazs Varga, Hans-Peter Bernhard, Raheeb Muzaffar, Mahin K Atiq, et al. 2023. Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward. arXiv preprint arXiv:2304.01299 (2023).
[19]
Marco Skocaj, Francesca Conserva, Nicol Sarcone Grande, Andrea Orsi, Davide Micheli, Giorgio Ghinamo, Simone Bizzarri, and Roberto Verdone. 2023. Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements. arXiv preprint arXiv:2307.02329 (2023).
[20]
Jiakai Yu, Tingjun Chen, Craig Gutterman, Shengxiang Zhu, Gil Zussman, Ivan Seskar, and Daniel Kilper. 2019. COSMOS: Optical Architecture and Prototyping. In 2019 Optical Fiber Communications Conference and Exhibition (OFC). 1--3.
[21]
Hongwei Zhang, Yong Guan, Ahmed Kamal, Daji Qiao, Mai Zheng, Anish Arora, Ozdal Boyraz, Brian Cox, Thomas Daniels, Matthew Darr, et al. 2022. ARA: A Wireless Living Lab Vision for Smart and Connected Rural Communities. In Proceedings of the 15th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization. 9--16.
[22]
Xian-Ming Zhang, Qing-Long Han, Xiaohua Ge, Derui Ding, Lei Ding, Dong Yue, and Chen Peng. 2020. Networked control systems: a survey of trends and techniques. IEEE/CAA Journal of Automatica Sinica 7, 1 (2020), 1--17.
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December 2023
405 pages
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Published: 07 August 2024
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