ExPECA: An Experimental Platform for Trustworthy: Acknowledgements & References

Table of Links

• Abstract & Introduction
• Testbed Design and Architecture
• Experimental Testbed Validation
• Supported Experimentation
• Acknowledgements & References

V. ACKNOWLEDGEMENTS

This research has been partially funded by (1) the VINNOVA Competence Center for Trustworthy Edge Computing Systems and Applications (TECoSA) at KTH Royal Institute of Technology; and (2) the Swedish Foundation for Strategic Research (SSF), through grant number ITM17–0246.

REFERENCES

[1] D. Raychaudhuri, I. Seskar, G. Zussman, T. Korakis, D. Kilper, T. Chen, J. Kolodziejski, M. Sherman, Z. Kostic, X. Gu, H. Krishnaswamy, S. Maheshwari, P. Skrimponis, and C. Gutterman, Challenge: COSMOS: A City-Scale Programmable Testbed for Experimentation with Advanced Wireless. New York, NY, USA: Association for Computing Machinery, 2020.

[2] J. Yu, T. Chen, C. Gutterman, S. Zhu, G. Zussman, I. Seskar, and D. Kilper, “Cosmos: Optical architecture and prototyping,” in 2019 Optical Fiber Communications Conference and Exhibition (OFC), pp. 1– 3, 2019.

[3] J. Breen, A. Buffmire, J. Duerig, K. Dutt, E. Eide, M. Hibler, D. Johnson, S. K. Kasera, E. Lewis, D. Maas, A. Orange, N. Patwari, D. Reading, R. Ricci, D. Schurig, L. B. Stoller, J. Van der Merwe, K. Webb, and G. Wong, “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), Sept. 2020.

[4] H. Zhang, Y. Guan, A. Kamal, D. Qiao, M. Zheng, A. Arora, O. Boyraz, B. Cox, T. Daniels, M. Darr, et al., “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, pp. 9–16, 2022.

[5] K. Keahey, J. Anderson, Z. Zhen, P. Riteau, P. Ruth, D. Stanzione, M. Cevik, J. Colleran, H. S. Gunawi, C. Hammock, J. Mambretti, A. Barnes, F. Halbach, A. Rocha, and J. Stubbs, “Lessons learned from the Chameleon Testbed,” in Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20), USENIX Association, July 2020.

[6] K. Keahey, J. Anderson, M. Sherman, C. Hammock, Z. Zhen, J. Tillotson, T. Bargo, L. Long, T. Ul Islam, S. Babu, H. Zhang, and F. Halbach, “Chi-in-a-box: Reducing operational costs of research testbeds,” in Practice and Experience in Advanced Research Computing, PEARC ’22, (New York, NY, USA), Association for Computing Machinery, 2022.

[7] S. George, T. Eiszler, R. Iyengar, H. Turki, Z. Feng, J. Wang, P. Pillai, and M. Satyanarayanan, “Openrtist: End-to-end benchmarking for edge computing,” IEEE Pervasive Computing, vol. 19, no. 4, pp. 10–18, 2020.

[8] X.-M. Zhang, Q.-L. Han, X. Ge, D. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: a survey of trends and techniques,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 1, pp. 1–17, 2020.

[9] M. Olgu´ın Munoz, ˜ An Emulation-Based Performance Evaluation Methodology for Edge Computing and Latency Sensitive Applications. PhD thesis, KTH Royal Institute of Technology, 2023.

[10] M. Olgu´ın Munoz, N. Roy, and J. Gross, “CLEAVE: Scalable and edge- ˜ native benchmarking of networked control systems,” in Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking, EdgeSys ’22, (New York, NY, USA), pp. 37–42, Association for Computing Machinery, 2022.

[11] K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan, “Towards wearable cognitive assistance,” in Proc. of the 12th Annual International Conference on Mobile Systems, Applications, and Services, p. 68–81, Association for Computing Machinery, 2014.

[12] B. Lu, Y. Li, X. Wu, and Z. Yang, “A review of recent advances in wind turbine condition monitoring and fault diagnosis,” in IEEE Power Electronics and Machines in Wind Applications, pp. 1–7, 2009.

[13] T.-H. Oh, R. Jaroensri, C. Kim, M. Elgharib, F. Durand, W. T. Freeman, and W. Matusik, “Learning-based video motion magnification,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 633–648, 2018.

[14] C. Liu, A. Torralba, W. T. Freeman, F. Durand, and E. H. Adelson, “Motion magnification,” ACM transactions on graphics (TOG), vol. 24, no. 3, pp. 519–526, 2005.

[15] G. P. Sharma, D. Patel, J. Sachs, M. De Andrade, J. Farkas, J. Harmatos, B. Varga, H.-P. Bernhard, R. Muzaffar, M. K. Atiq, et al., “Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward,” arXiv preprint arXiv:2304.01299, 2023.

[16] S. Mostafavi, G. P. Sharma, and J. Gross, “Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities,” arXiv preprint arXiv:2307.10648, 2023.

[17] M. Skocaj, F. Conserva, N. S. Grande, A. Orsi, D. Micheli, G. Ghinamo, S. Bizzarri, and R. Verdone, “Data-driven predictive latency for 5g: A theoretical and experimental analysis using network measurements,” arXiv preprint arXiv:2307.02329, 2023.

[18] V. N. Moothedath, J. P. Champati, and J. Gross, “Online algorithms for hierarchical inference in deep learning applications at the edge,” 2023.

[19] G. Al-Atat, A. Fresa, A. P. Behera, V. N. Moothedath, J. Gross, and J. P. Champati, “The case for hierarchical deep learning inference at the network edge,” in Proceedings of the 1st International Workshop on Networked AI Systems, NetAISys ’23, (New York, NY, USA), Association for Computing Machinery, 2023.

[20] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54 of Proceedings of Machine Learning Research, pp. 1273–1282, PMLR, 20–22 Apr 2017.

[21] H. Gao, A. Xu, and H. Huang, “On the Convergence of CommunicationEfficient Local SGD for Federated Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 7510–7518, May 2021.

[22] J. Perazzone, S. Wang, M. Ji, and K. S. Chan, “Communication-efficient device scheduling for federated learning using stochastic optimization,” in IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, pp. 1449–1458, 2022.