Distributed Systems

Student Colloquium Topics

Cluster Scheduling for DLT workloads

Reviewer: Kawsar Haghshenas
Status: Available.
Date: 11/12/2023.
References:

  1. Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, and Matei Zaharia. "Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads." In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), pp. 481-498. 2020.
  2. Tan N. Le, Xiao Sun, Mosharaf Chowdhury, and Zhenhua Liu. "Allox: compute allocation in hybrid clusters." In Proceedings of the fifteenth european conference on computer Systems, pp. 1-16. 2020.
  3. Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel et al. "Gandiva: Introspective Cluster Scheduling for Deep Learning." In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pp. 595-610. 2018.

Estimating Deep Learning GPU Memory Consumption

Reviewer: Kawsar Haghshenas
Status: Available.
Date: 11/12/2023.
References:

  1. Yanjie Gao, Yu Liu, Hongyu Zhang, Zhengxian Li, Yonghao Zhu, Haoxiang Lin, and Mao Yang. "Estimating GPU memory consumption of deep learning models." In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 1342-1352. 2020.
  2. Haiyi Liu, Shaoying Liu, Chenglong Wen, and W. Eric Wong. "TBEM: Testing-Based GPU-Memory Consumption Estimation for Deep Learning." IEEE Access, 10, pp.39674-39680. 2022.
  3. Lu Bai, Weixing Ji, Qinyuan Li, Xilai Yao, Wei Xin, and Wanyi Zhu. "Dnnabacus: Toward accurate computational cost prediction for deep neural netw." arXiv preprint arXiv:2205.12095. 2022.
  4. Yanjie Gao, Xianyu Gu, Hongyu Zhang, Haoxiang Lin, and Mao Yang. "Runtime performance prediction for deep learning models with graph neural network." In IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 368-380. 2023.

Federated Rule Mining in Complex Event Processing

Reviewer: Majid Lotfian Delouee
Status: Available.
Date: 01/12/2023.
References:

  1. Simsek, Mehmet Ulvi, Feyza Yildirim Okay, and Suat Ozdemir. "A deep learning-based CEP rule extraction framework for IoT data." The Journal of Supercomputing 77 (2021): 8563-8592.
  2. Lv, Jiayao, Bihui Yu, and Huajun Sun. "CEP rule extraction framework based on evolutionary algorithm." In 2022 11th International Conference of Information and Communication Technology (ICTech)), pp. 245-249. IEEE, 2022.
  3. Roldán-Gómez, José, Jesús Martínez del Rincon, Juan Boubeta-Puig, and José Luis Martínez. "An automatic unsupervised complex event processing rules generation architecture for real-time IoT attacks detection." Wireless Networks (2023): 1-18.
  4. Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N. and Jararweh, Y., 2022. Federated learning review: Fundamentals, enabling technologies, and future applications. Information Processing & Management, 59(6), p.103061.

Distributed Digital Twin

Reviewer: Dilek Dustegor
Status: available.
Date: 30/11/2023.
References:

  1. Aboelhassan, Ayman; Sakr, Ahmed H.; Yacout, Soumaya, "General purpose digital twin framework using digital shadow and distributed system concepts," Computers and Industrial Engineering VOLUME 183, 2023, https://doi.org/10.1016/j.cie.2023.109534
  2. Hasse, H; van der Valk, H; Möller, F; Otto, B, "Design Principles for Shared Digital Twins in Distributed Systems," Business and Information Systems Engineering, 64-6 (751-772), 2022, https://link.springer.com/article/10.1007/s12599-022-00751-1
  3. Azeroual Mohamed, Tijani Lamhamdi, Hassan El Moussaoui and Hassane El Markhi, "IoTwins: Toward Implementation of Distributed Digital Twins in Industry 4.0 Settings," Computers, Vol: 11, Issue: 5, 2022, https://doi.org/10.3390/computers11050067
  4. Ricci, A; Croatti, A; Mariani, S; ; Montagna, S; Picone, M, "Web of Digital Twins," ACM Transactions on Internet Technology, Vol: 22, Issue: 4, https://doi.org/10.1145/3507909

Digital Twin for Water Network

Reviewer: Dilek Dustegor
Status: available.
Date: 30/11/2023.
References:

  1. Wei, YY; Law, AWK; Yang, C; Tang, D, "Combined Anomaly Detection Framework for Digital Twins of Water Treatment Facilities," Water VOLUME 14, Issue 7, 2022, https://doi.org/10.3390/w14071001
  2. Wei, YY; Law, AWK; Yang, C, "Real-Time Data-Processing Framework with Model Updating for Digital Twins of Water Treatment Facilities," Water, 14 (22):3591, 2022, https://doi.org/10.3390/w14223591
  3. Liu, WT; He, SD; Mou, JP; Xue, T; Chen, HT; Xiong, WL, "Digital twins-based process monitoring for wastewater treatment processes," Reliability Engineering and System Safety 238, 2023, https://doi.org/10.1016/j.ress.2023.109416
  4. Rand, Honey, "Digital Twins: The Next Generation of Water Treatment Technology," Journal American Water Works Association, Volume: 111, Issue: 12, 2019, https://doi.org/10.1002/awwa.1414

Data Driven Methods for Leakage Detection in Water Network

Reviewer: Dilek Dustegor
Status: available.
Date: 30/11/2023.
References:

  1. Romero-Ben, L; Alves, D; Blesa, J; Cembrano, G; Puig, V; Duviella, E, "Leak detection and localization in water distribution networks: Review and perspective," Annual Reviews in control 55, pp 392–419, 2023, https://doi.org/0.1016/j.arcontrol.2023.03.012
  2. Zhang, XQ; Wu, XW; Yuan, YQ; Long, ZH; Yu, TC, "Burst detection based on multi-time monitoring data from multiple pressure sensors in district metering areas," Water Supply, 2023, https://doi.org/10.2166/ws.2023.220
  3. Tyagi, V; Pandey, P; Jain, S; Ramachandran, P, "A Two-Stage Model for Data-Driven Leakage Detection and Localization in Water Distribution Networks," Water, 2023, https://doi.org/10.3390/w15152710
  4. Wan, X; Farmani, R; Keedwell, E, "Gradual Leak Detection in Water Distribution Networks Based on Multistep Forecasting Strategy," JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2023, https://doi.org/10.3390/w15152710

Generalization in Graph Neural Networks

Reviewer: Andrés Tello
Status: available.
Date: 21/11/2023.
References:

  1. Lee, H., Yoon, K., 2023. Towards better generalization with flexible representation of multi-module graph neural networks. Transactions on Machine Learning Research.
  2. Yu, J., Liang, J., & He, R. (2023). Mind the Label Shift of Augmentation-based Graph OOD Generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11620-11630).
  3. Buffelli, D., Liò, P., & Vandin, F. (2022). Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks. Advances in Neural Information Processing Systems, 35, 31871-31885.
  4. Yehudai, G., Fetaya, E., Meirom, E., Chechik, G., Maron, H., 2021. From local structures to size generalization in graph neural networks, in: Meila, M., Zhang, T. (Eds.), Proceedings of the 38th International Conference on Machine Learning, PMLR. pp. 11975–11986.

Spatio-temporal GNNs for time series forecasting

Reviewer: Andrés Tello
Status: available.
Date: 21/11/2023.
References:

  1. Cini, A., Marisca, I., Zambon, D., & Alippi, C. (2023). Taming Local Effects in Graph-based Spatiotemporal Forecasting. arXiv preprint arXiv:2302.04071.
  2. Cini, A., Mandic, D., & Alippi, C. (2023). Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting. arXiv preprint arXiv:2305.19183.
  3. Tong, C., Rocheteau, E., Veličković, P., Lane, N., & Liò, P. (2021, February). Predicting patient outcomes with graph representation learning. In International Workshop on Health Intelligence (pp. 281-293). Cham: Springer International Publishing.
  4. Liu, J., Wang, X., Xie, F., Wu, S., & Li, D. (2023). Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network. Engineering Applications of Artificial Intelligence, 121, 106000.

Exploring Multi-hop Propagation in Graph Neural Networks

Reviewer: Huy Truong
Status: Available.
Date: 21/11/2023.
References:

  1. Huang, S., Song, Y., Zhou, J., & Lin, Z. (2023). Tailoring Self-Attention for Graph via Rooted Subtrees. Thirty-Seventh Conference on Neural Information Processing Systems.
  2. Zhong, Z., Li, C.-T., & Pang, J. (2023). Hierarchical message-passing graph neural networks. Data Mining and Knowledge Discovery, 37(1), 381–408.
  3. Zhao, J., Dong, Y., Ding, M., Kharlamov, E., & Tang, J. (2021). Adaptive Diffusion in Graph Neural Networks. In A. Beygelzimer, Y. Dauphin, P. Liang, & J. W. Vaughan (Eds.), Advances in Neural Information Processing Systems.

Self-supervision with Graph Neural Networks

Reviewer: Huy Truong
Status: Available.
Date: 21/11/2023.
References:

  1. Liu, Z., Shi, Y., Zhang, A., Zhang, E., Kawaguchi, K., Wang, X., & Chua, T.-S. (2023). Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules. NeurIPS.
  2. Hou, Z., He, Y., Cen, Y., Liu, X., Dong, Y., Kharlamov, E., & Tang, J. (2023). GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner. Proceedings of the ACM Web Conference 2023, 737–746.
  3. Li, J., Wu, R., Sun, W., Chen, L., Tian, S., Zhu, L., … Wang, W. (2023). What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1268–1279. Presented at the Long Beach, CA, USA. doi:10.1145/3580305.3599546

TCAM memory management approaches for network routing tables

Reviewer: Saad Saleh
Status: Available.
Date: 21/11/2023.
References:

  1. V. Rios & G. Varghese. "MashUp: Scaling TCAM-based IP Lookup to Larger Databases by Tiling Trees". 2022. arXiv preprint arXiv:2204.09813.
  2. Wang, Fei, Yi Hong, and Cong Xu. "PPLTCAM: A parallel TCAM‐based IP address lookup structure with high incremental update performance." Concurrency and Computation: Practice and Experience 31, 2019.
  3. W. Li, D. Li, X. Liu, T. Huang, X. Li, W. Le, H. Li,"A power-saving pre-classifier for TCAM-based IP lookup", Computer Networks, 164:106898, 2019.
  4. Y. Zhang, P. Cong, B. Liu, W. Wang, K. Xu, "AIR: An AI-based TCAM Entry Replacement Scheme for Routers", In IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), (pp. 1-10), 2021.

Load Balancing in Data Center Networks

Reviewer: Bochra Boughzala
Status: Not available.
Date: 28/11/2022.
References:

  1. Alizadeh, M., Edsall, T., Dharmapurikar, S., Vaidyanathan, R., Chu, K., Fingerhut, A., Lam, V.T., Matus, F., Pan, R., Yadav, N. and Varghese, G., 2014, August. CONGA: Distributed congestion-aware load balancing for datacenters. In Proceedings of the 2014 ACM conference on SIGCOMM (pp. 503-514).
  2. He, K., Rozner, E., Agarwal, K., Felter, W., Carter, J. and Akella, A., 2015. Presto: Edge-based load balancing for fast datacenter networks. ACM SIGCOMM Computer Communication Review, 45(4), pp.465-478.
  3. Katta, N., Ghag, A., Hira, M., Keslassy, I., Bergman, A., Kim, C. and Rexford, J., 2017, November. Clove: Congestion-aware load balancing at the virtual edge. In Proceedings of the 13th International Conference on emerging Networking EXperiments and Technologies (pp. 323-335).
  4. Ye, J.L., Chen, C. and Chu, Y.H., 2018, October. A weighted ECMP load balancing scheme for data centers using P4 switches. In 2018 IEEE 7th International Conference on Cloud Networking (CloudNet) (pp. 1-4). IEEE.

Conditional planning an overview of approaches

Expert reviewer: Heerko Groefsema
Status: available.
Date: 28/11/2022.
References:

  1. Peot, M. A., & Smith, D. E. (1992, January). Conditional nonlinear planning. In Artificial Intelligence Planning Systems (pp. 189-197). Morgan Kaufmann.
  2. Blythe, J. (1999). An overview of planning under uncertainty. Artificial intelligence today, 85-110.
  3. Rintanen, J. (1999). Constructing conditional plans by a theorem-prover. Journal of Artificial Intelligence Research, 10, 323-352.
  4. Karlsson, L. (2001, January). Conditional progressive planning under uncertainty. In IJCAI (pp. 431-438).