publish-assistant/site/data/cloud-edge/papers/IC-2024-digest.yaml

venue: IC
year: 2024
selected:
  - title: "Revisiting Edge AI: Opportunities and Challenges"
    authors:
    - Tobias Meuser
    - Lauri Lovén
    - Monowar Bhuyan
    - Shishir G. Patil
    - Schahram Dustdar
    - Atakan Aral
    - Suzan Bayhan
    - Christian Becker 0001
    - Eyal de Lara
    - Aaron Yi Ding
    - Janick Edinger
    - James Gross
    - Nitinder Mohan
    - Andy D. Pimentel
    - Etienne Rivière
    - Henning Schulzrinne
    - Pieter Simoens
    - Gürkan Solmaz
    - Michael Welzl
    tldr: "A multi-author position paper that revisits the state of edge AI, cataloguing deployment barriers and open research problems across hardware, networking, and software layers."
    why_notable: "Brings together 19 leading researchers to synthesize the field's most pressing edge AI challenges, making it an authoritative reference for practitioners and researchers planning edge deployments."

  - title: "On Causality in Distributed Continuum Systems"
    authors:
    - Víctor Casamayor-Pujol
    - Boris Sedlak
    - Praveen Kumar Donta
    - Schahram Dustdar
    tldr: "Formalizes causal reasoning across cloud-to-edge continuum systems, providing a conceptual framework for tracking cause-and-effect relationships in highly distributed deployments."
    why_notable: "Addresses a foundational gap in distributed systems theory that becomes critical when debugging or optimizing multi-tier edge–cloud pipelines."

  - title: "Beyond Von Neumann in the Computing Continuum: Architectures, Applications, and Future Directions"
    authors:
    - Dragi Kimovski
    - Nishant Saurabh
    - Matthijs Jansen
    - Atakan Aral
    - Auday Al-Dulaimy
    - André B. Bondi
    - Antonino Galletta
    - Alessandro V. Papadopoulos
    - Alexandru Iosup
    - Radu Prodan
    tldr: "Surveys non-von Neumann architectural paradigms—neuromorphic, in-memory, and dataflow computing—and maps them onto continuum computing use cases spanning edge to cloud."
    why_notable: "Offers a rare cross-cutting view of how emerging hardware architectures reshape the design space for distributed Internet applications."

  - title: "ARASEC: Adaptive Resource Allocation and Model Training for Serverless Edge-Cloud Computing"
    authors:
    - Dewant Katare
    - Eduard Marin
    - Nicolas Kourtellis
    - Marijn Janssen
    - Aaron Yi Ding
    tldr: "Proposes ARASEC, a system that jointly optimizes resource allocation and on-device model training for serverless functions deployed across edge and cloud nodes."
    why_notable: "Demonstrates measurable efficiency gains in a realistic serverless edge-cloud setting, directly informing how operators should provision heterogeneous serverless infrastructure."

  - title: "WebAssembly at the Edge: Benchmarking a Serverless Platform for Private Edge Cloud Systems"
    authors:
    - Giuseppe De Palma
    - Saverio Giallorenzo
    - Jacopo Mauro
    - Matteo Trentin
    - Gianluigi Zavattaro
    tldr: "Benchmarks a WebAssembly-based serverless runtime on private edge cloud hardware, measuring cold-start latency, throughput, and isolation overhead compared to container-based alternatives."
    why_notable: "Provides concrete empirical data that practitioners need when evaluating WebAssembly as a lightweight alternative to Docker for edge serverless deployments."

  - title: "HeROsim: An Allocation and Scheduling Simulator for Evaluating Serverless Orchestration Policies"
    authors:
    - Vincent Lannurien
    - Laurent d'Orazio
    - Olivier Barais
    - Stéphane Paquelet
    - Jalil Boukhobza
    tldr: "Introduces HeROsim, an open simulator that models serverless function placement and scheduling policies across heterogeneous infrastructure, enabling fair policy comparison without live cluster costs."
    why_notable: "Fills a practical tooling gap for researchers and platform engineers who need reproducible evaluation environments for serverless orchestration algorithms."

  - title: "Hierarchical Network Data Analytics Framework for 6G Network Automation: Design and Implementation"
    authors:
    - Youbin Jeon
    - Sangheon Pack
    tldr: "Designs and implements a hierarchical analytics framework that aggregates network telemetry at multiple granularities to automate management decisions in 6G deployments."
    why_notable: "Bridges the gap between 6G vision and practical automation by providing a concrete architecture with implementation details and empirical evaluation."

  - title: "Digital-Twin-Driven End-to-End Network Slicing Toward 6G"
    authors:
    - Mahnoor Yaqoob
    - Ramona Trestian
    - Mallik Tatipamula
    - Huan Xuan Nguyen
    tldr: "Proposes a digital-twin framework that continuously models and reconfigures end-to-end network slices, enabling dynamic SLA enforcement across heterogeneous 6G infrastructure."
    why_notable: "Connects digital twin technology to the operational problem of network slice management, a key requirement for 6G service assurance."

  - title: "The Internet of Things in the Era of Generative AI: Vision and Challenges"
    authors:
    - Xin Wang 0120
    - Zhongwei Wan
    - Arvin Hekmati
    - Mingyu Zong
    - Samiul Alam
    - Mi Zhang 0002
    - Bhaskar Krishnamachari
    tldr: "Examines how generative AI models can be integrated into IoT pipelines for data synthesis, anomaly detection, and on-device inference, and identifies the key resource and privacy constraints."
    why_notable: "Provides a structured research agenda for one of the most active intersections in Internet computing, relevant to both IoT platform designers and ML practitioners."

  - title: "Distributed Federated Deep Learning in Clustered Internet of Things Wireless Networks With Data Similarity-Based Client Participation"
    authors:
    - Evangelia Fragkou 0001
    - Eleftheria Chini
    - Maria Papadopoulou 0008
    - Dimitrios K. Papakostas
    - Dimitrios Katsaros 0001
    - Schahram Dustdar
    tldr: "Proposes a clustered federated learning scheme for wireless IoT networks that selects participating clients based on data similarity, reducing communication overhead and improving model convergence."
    why_notable: "Addresses a core practical challenge in IoT federated learning—heterogeneous and non-IID data—with an empirically validated participation strategy."