ACES
Autopoietic Cognitive Edge-cloud Services
Full project details (EU Research results portal): https://cordis.europa.eu/project/id/101093126
Project description:
The increasing need for cloud services at the edge (edge–services) is caused by the rapidly growing quantity and capabilities of connected and interacting edge devices exchanging vast amounts of data. This poses different challenges to cloud computing architectures at the edge, such as i) ability to provide end-to-end transaction resiliency of applications broken down in distributions of microservices; ii) creating reliability and stability of automation in cloud management under increasing complexity iii) secure and timely handling of the increasing and latency sensitive flow (east-west) of sensitive data and applications; iv)need for explainable AI and transparency of the increasing automation in edge-services platform by operators, software developers and end-users. ACES will solve these challenges by infused autopoiesis and cognition on different levels of cloud management to empower with AI different functionalities such as: workload placement, service and resource management, data and policy management. ACES key outcomes will be: i) autopoiesis cognitive cloud-edge framework; ii) awareness tools, AI/ML agents for workload placement, service and resource management, data and policy management, telemetry and monitoring; iii) agents safeguarding stability in situations of extreme load and complexity; iv) swarm technology-based methodology and implementation for orchestration of resources in the edge; v) edge-wide workload placement and optimization service; vi) an app store for classification, storage, sharing and rating of AI models used in ACES.ACES will be demonstrated and validated in 3 scenarios demanding for support of highly decentralised computing, ability to take autonomic decisions, reducing costs of cloud-edge management and increasing their efficiency ,thus reducing impact on environment.To foster the uptake of ACES outcomes beyond its lifespan, different activities are foreseen to drive adoption to a wider network of stakeholders in key sectors
EuroVoc IDs: /natural sciences/computer and information sciences/software
EU Programme: Horizon Europe
EU Project
Project publications:
| EU Project | Has Title | Has Category | Has Type | Has Year | Has DOI |
|---|---|---|---|---|---|
| ACES | Using Range-Revocable Pseudonyms to Provide Backward Unlinkability in the Edge (Extended Version) | Cybersecurity, Privacy, and Trust | Conference proceedings | 2023 | https://doi.org/10.1145/3576915.3623111 |
| ACES | FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks | Cybersecurity, Privacy, and Trust | Conference proceedings | 2023 | https://doi.org/10.48550/arxiv.2310.02113 |
| ACES | FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning | Cybersecurity, Privacy, and Trust | Conference proceedings | 2024 | https://doi.org/10.48550/arxiv.2312.04432 |
| ACES | PoTR: Accurate and Efficient Proof of Timely-Retrievability for Storage Systems | Cybersecurity, Privacy, and Trust | Conference proceedings | 2024 | https://doi.org/10.1109/PRDC59308.2023.00023 |
| ACES | ProKube: Proactive Kubernetes Orchestrator for Inference in Heterogeneous Edge Computing | Cybersecurity, Privacy, and Trust | Peer reviewed articles | 2024 | https://doi.org/10.1002/nem.2298 |
| ACES | Poster: In-Network ML Feature Computation for Malicious Traffic Detection | Cybersecurity, Privacy, and Trust | Other | https://doi.org/10.1145/3603269.3610866 | |
| ACES | MASTER: Machine Learning-Based Cold Start Latency Prediction Framework in Serverless Edge Computing Environments for Industry 4.0 | Cybersecurity, Privacy, and Trust | Peer reviewed articles | 2024 | https://doi.org/10.1109/JSAS.2024.3396440 |
| ACES | ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments | Cybersecurity, Privacy, and Trust | Peer reviewed articles | 2023 | https://doi.org/10.1109/TSUSC.2023.3348157 |
| ACES | AI-based fog and edge computing: A systematic review, taxonomy and future directions | Cybersecurity, Privacy, and Trust | Peer reviewed articles | 2023 | https://doi.org/10.1016/j.iot.2022.100674 |