SRF - Scalability Research Forum

1st Edition 2️⃣0️⃣2️⃣4️⃣

📚 Surveys articles

Groups participation (reserved to students which are attending classes in 2024)

Within April 29th, communicate using this form:

  • the name of the group;
  • the participants (1, 2 or 3);
  • the list of 3️⃣ papers in the references of the survey proposed, in order of preference;
  • preferred timeslot in the day (only if you are attending other classes on those days);
  • classes PCPC, Serverless, or both.

Presentation guidelines

  • The group must be homogeneous: all attendees must attend Serverless OR Concurrent Programming OR both.
  • Presentation for:
    • One class students (PCPC or Serverless): 2️⃣0️⃣ minutes presentation ➕ 1️⃣0️⃣ minutes of questions.
    • Two classes students (PCPC and Serverless): 3️⃣0️⃣ minutes presentation ➕ 1️⃣0️⃣ minutes of questions.

🗓️ Important dates and 🏆 Grading

The presentations will take place at the Scalability Research Forum (SRF), which will be held for the first time on May 30th, 31st, and June 7th. Presentations must be in English for the Serverless students and may be in English (preferably) for the Concurrent Programming course (if you wish, questions/answers may be conducted in Italian).

The participants will receive a gadget at the end of the SRF (on the 7th), where the 3 best presentations will be awarded 🎁. Anyway, the presentations, for the purpose of the exam, are graded pass/fail.

  • May 30th 2024, P6, 9:00 14:00 (Presentations)
  • May 31st 2024, P6, 9:00 14:00 (Presentations)
  • June 7th 2024, P6, 9:00 14:00 (Final day with presentations and awards)

The calendar is now available.

Exam for non attending students

The SRF is reserved for students currently actively attending the classes. The other students will give the presentations on the day of every scheduled exam, respectively, for Serverless and Concurrent programming (obviously with no gadgets! 😄).

🗓️ Schedule

May 30th

  1. Califano-DeMaio, PipeDream: Generalized Pipeline Parallelism for DNN Training - https://doi.org/10.1145/3341301.3359646
  2. Gianluigi Memoli, Dynamic Aggregation and Scheduling in CoAP/Observe-Based Wireless Sensor Networks - http://dx.doi.org/10.1109/JIOT.2016.2517120
  3. Penna, FlexPS: Flexible Parallelism Control in Parameter Server Architecture - https://doi.org/10.1145/3187009.3177734
  4. LIBPAKIOT, Internet of things: Architectures, protocols, and applications - https://doi.org/10.1155/2017/9324035
  5. Gruppo Federated Learning, Federated Learning: Strategies for Improving Communication Efficiency - https://arxiv.org/abs/1610.05492
  6. Mutual Inclusion, Neugraph: parallel deep neural network computation on large graphs - https://dl.acm.org/doi/10.5555/3358807.3358845
  7. Pizza Team, A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration - https://doi.org/10.1145/3292674
  8. Data Dream Team, A Performance Evaluation of Federated Learning Algorithms- https://doi.org/10.1145/3292674

May 31th

  1. Melkia, Middlewarefor IoT-Cloud Integration Across Application Domains - https://doi.org/10.1109/MDAT.2014.2314602
  2. UniSec, Lucky thirteen: Breaking the TLS and DTLS record protocols - https://doi.org/10.1109/SP.2013.42
  3. DiPasqualeMonzillo, Complex Network Analysis using Parallel Approximate Motif Counting - https://doi.org/10.1109/IPDPS.2014.50
  4. Me, Myself and I, SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing - https://doi.org/10.1145/3464298.3476133
  5. Vitale-Cerciello, Multi-column deep neural network for traffic sign classification - https://doi.org/10.1016/j.neunet.2012.02.023
  6. GarofaloAdinolfiArdovino, Parallel hypergraph partitioning for scientific computing - https://doi.org/10.1109/IPDPS.2006.1639359
  7. The Solo Journey, Web Performance Evaluation for Internet of Things Applications - https://doi.org/10.1109/ACCESS.2016.2615181
  8. Gruppo Leone, Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU - https://doi.org/10.1145/1815961.1816021

June 7th

  1. Group 1.2.3 (Final), Authentication for the web of things: Secure end-to-end authentication between CoAP and HTTP - https://doi.org/10.1109/PIMRC.2017.8292352
  2. GNU/Kefir, ChainerMN: Scalable Distributed Deep Learning Framework - https://doi.org/10.48550/arXiv.1710.11351
  3. Bilovus, Performance evaluation of Websocket protocol for implementation of full-duplex web streams - https://doi.org/10.1109/MIPRO.2014.6859715
  4. YM, Fog computing and its role in the internet of things - http://dx.doi.org/10.1145/2342509.2342513
  5. Gioacchino Tortorelli, Active Access: A Mechanism for High-Performance Distributed Data-Centric Computations - https://doi.org/10.1145/2751205.2751219
  6. Santangelo, Horovod: fast and easy distributed deep learning in TensorFlow - https://arxiv.org/abs/1802.05799
  7. iRagazzi, Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines - https://doi.org/10.1145/3458817.3476145
  8. Nuvola, A Disruption-Tolerant RESTful Support for the Web of Things - https://doi.org/10.1109/FiCloud.2016.11
  1. Lorenzo&Lorenzo, Middleware solutions in WSN: The IoT oriented approach in the ICSI project - https://doi.org/10.1109/SoftCOM.2013.6671886
  2. The New Revolution Cloud Ranger, The importance of a standard security architecture for SOA-based iot middleware - https://doi.org/10.1109/MCOM.2015.7355580
  3. Solo(Serverless), Performance analysis of communication protocols for internet of things platforms - http://dx.doi.org/10.1109/ColComCon.2017.8088198
  4. Taranum, Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP - http://dx.doi.org/10.1109/SysEng.2017.8088251
  5. UNISArverless, Communication-avoiding parallel minimum cuts and connected components - https://doi.org/10.1145/3178487.3178504
  6. MegaBeat, Communication-Efficient Jaccard similarity for High-Performance Distributed Genome Comparisons - https://doi.ieeecomputersociety.org/10.1109/IPDPS47924.2020.00118