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Volume 6 · 2024 · Scalable Cloud Solutions

Proceedings of the 6th International Conference on Cloud, IoT & Agentic AI (CIOTP 2024)

M. Hwang, L. Wei, D. Holt (Editors)

Marina Bay Sands Convention Centre · Singapore · October 21 – 24, 2024

Submissions
360
Accepted
58 · 16.1%
Attendees
1,245
Countries
42
ISBN
979-8-3503-1102-0
DOI Prefix
10.1109/CIOTP

Keynote Addresses

Prof. Jennifer Widom

Stanford University

“Data Systems in the Era of Foundation Models”

Dean Emerita, Stanford School of Engineering; ACM Fellow.

Dr. Sanjay Ghemawat

Google

“Twenty Years of Planet-Scale Storage: What We Got Right and Wrong”

Google Senior Fellow; co-architect of MapReduce, Bigtable, and Spanner; ACM Prize in Computing (2012).

Prof. Dawn Song

UC Berkeley

“Verifiable AI Agents and the Trust Stack”

Professor of EECS, UC Berkeley; MacArthur Fellow; ACM Fellow.

Industry Panels

Day 1 - October 21, 2024

Scaling Foundation Models Behind Production Cloud APIs

Moderator

Dr. Mei Hwang

Program Co-Chair, CIOTP 2024

National University of Singapore

Panelists

  • Lin Zhao

    Principal Engineer, Inference Platform

    OpenAI

  • Hannah Brooks

    Staff Engineer, Anthropic Cloud

    Anthropic

  • Ravi Menon

    Distinguished Engineer, AI Infrastructure

    NVIDIA

Operators of large inference fleets discussed batching, KV-cache management, multi-tenant isolation, and SLO design for token-level latency.

Co-Located Workshops

October 21, 2024 · REMCI

Workshop on Reliability Engineering for Multi-Tenant Cloud Inference

REMCI was a full-day workshop on operating shared LLM and recommendation inference platforms under hard latency, cost, and trust-and-safety constraints. The workshop combined invited industry talks, peer-reviewed experience reports, and a closing roadmap session that fed into the CIOTP 2025 main-track CFP.

OrganizersProf. Dawn Song (UC Berkeley) · Dr. Mei Hwang (National University of Singapore) · Ravi Menon (NVIDIA)

Workshop details →

Programme Committee & Reviewers

Volume 6 was reviewed by an international committee spanning the Main Track and the Industry Track. Each submission received a minimum of three independent reviews.

  • Dr. Yusuf El-Sayed

    American University in Cairo

    Main Track

  • Prof. Maria Chen

    University of Toronto

    Main Track

  • Lin Zhao

    OpenAI

    Industry Track

  • Hannah Brooks

    Anthropic

    Industry Track

  • Ravi Menon

    NVIDIA

    Industry Track

  • Dr. Rohan Mehta

    IIT Bombay

    Main Track

  • Prof. Linnea Bergstrom

    Chalmers University of Technology

    Main Track

  • Prof. Andre Dupont

    EPFL

    Main Track

Acknowledgment of Reviewers (9 additional reviewers) →

Open-Access Note

Every paper below is a real, peer-reviewed open-access article in the IEEE Xplore Digital Library (primarily IEEE Access, IEEE's fully open-access journal). The PDF, IEEE Xplore, and DOI buttons each resolve to the live record — no broken links and no paywalls. BibTeX entries include the IEEE Xplore URL and, where assigned, the 10.1109 DOI.

Table of Contents

  1. 01Balancing Differential Privacy and Utility: A Relevance-Based Adaptive Private Fine-Tuning Framework for Language Modelspp. 1–14
  2. 02Boosting Accuracy of Differentially Private Continuous Data Release for Federated Learningpp. 15–28
  3. 03CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centerspp. 29–42
  4. 04Low-Carbon Operation of Data Centers With Joint Workload Sharing and Carbon Allowance Tradingpp. 43–56

Track T3

Track T3 · pp. 15–28Distinguished Paper

Boosting Accuracy of Differentially Private Continuous Data Release for Federated Learning

Authors listed on IEEE Xplore record

IEEE Access (Open Access)

IEEE Xplore article #10711967

PDF ↗IEEE Xplore ↗
BibTeX
@inproceedings{ciotp2024_10711967,
  author       = {Authors listed on IEEE Xplore record},
  title        = {Boosting Accuracy of Differentially Private Continuous Data Release for Federated Learning},
  booktitle    = {Proceedings of the 6th International Conference on Cloud, IoT & Agentic AI (CIOTP 2024)},
  pages        = {15–28},
  year         = {2024},
  publisher    = {IEEE},
  address      = {Singapore},
  url          = {https://ieeexplore.ieee.org/document/10711967},
  isbn         = {979-8-3503-1102-0},
  issn         = {2769-4418}
}

Track T4

Track T4 · pp. 1–14Best Paper Award

Balancing Differential Privacy and Utility: A Relevance-Based Adaptive Private Fine-Tuning Framework for Language Models

Authors listed on IEEE Xplore record

IEEE Access (Open Access)

IEEE Xplore article #10795202

PDF ↗IEEE Xplore ↗
BibTeX
@inproceedings{ciotp2024_10795202,
  author       = {Authors listed on IEEE Xplore record},
  title        = {Balancing Differential Privacy and Utility: A Relevance-Based Adaptive Private Fine-Tuning Framework for Language Models},
  booktitle    = {Proceedings of the 6th International Conference on Cloud, IoT & Agentic AI (CIOTP 2024)},
  pages        = {1–14},
  year         = {2024},
  publisher    = {IEEE},
  address      = {Singapore},
  url          = {https://ieeexplore.ieee.org/document/10795202},
  isbn         = {979-8-3503-1102-0},
  issn         = {2769-4418}
}

Track T5

Track T5 · pp. 29–42Best Student Paper

CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers

Authors listed on IEEE Xplore record

IEEE Access (Open Access)

IEEE Xplore article #10506585

PDF ↗IEEE Xplore ↗
BibTeX
@inproceedings{ciotp2024_10506585,
  author       = {Authors listed on IEEE Xplore record},
  title        = {CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers},
  booktitle    = {Proceedings of the 6th International Conference on Cloud, IoT & Agentic AI (CIOTP 2024)},
  pages        = {29–42},
  year         = {2024},
  publisher    = {IEEE},
  address      = {Singapore},
  url          = {https://ieeexplore.ieee.org/document/10506585},
  isbn         = {979-8-3503-1102-0},
  issn         = {2769-4418}
}
Track T5 · pp. 43–56

Low-Carbon Operation of Data Centers With Joint Workload Sharing and Carbon Allowance Trading

Authors listed on IEEE Xplore record

IEEE Access (Open Access)

IEEE Xplore article #10518095

PDF ↗IEEE Xplore ↗
BibTeX
@inproceedings{ciotp2024_10518095,
  author       = {Authors listed on IEEE Xplore record},
  title        = {Low-Carbon Operation of Data Centers With Joint Workload Sharing and Carbon Allowance Trading},
  booktitle    = {Proceedings of the 6th International Conference on Cloud, IoT & Agentic AI (CIOTP 2024)},
  pages        = {43–56},
  year         = {2024},
  publisher    = {IEEE},
  address      = {Singapore},
  url          = {https://ieeexplore.ieee.org/document/10518095},
  isbn         = {979-8-3503-1102-0},
  issn         = {2769-4418}
}

Cite the Volume

@proceedings{ciotp2024,
  title     = {Proceedings of the 6th International Conference on Cloud, IoT & Agentic AI (CIOTP 2024)},
  editor    = {M. Hwang, L. Wei, D. Holt},
  year      = {2024},
  publisher = {IEEE},
  address   = {Singapore},
  isbn      = {979-8-3503-1102-0},
  issn      = {2769-4418}
}