Summary
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
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
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101093126 |
| Start date: | 01-01-2023 |
| End date: | 31-12-2025 |
| Total budget - Public funding: | 5 543 925,00 Euro - 5 543 925,00 Euro |
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Original 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
Status
SIGNEDCall topic
HORIZON-CL4-2022-DATA-01-02Update Date
09-02-2023
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Organisations
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| INESC ID - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, INVESTIGACAO E DESENVOLVIMENTO EM LISB... (Coördinator)
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| DATAPOWER SRL
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| HIRO MICRODATACENTERS B.V. (HIRO)
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| INDEPENDENT POWER TRANSMISSION OPERATOR SA
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| LAKESIDE LABS GMBH
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| MARTEL GMBH (MARTEL GMBH)
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| SIXSQ SA
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| TECHNISCHE UNIVERSITAT DARMSTADT
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| UNIVERSIDAD POLITECNICA DE MADRID
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| University of Ljubljana, Faculty of Mechanical engineering
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| University of applied sciences and arts of southern switzerland (SUPSI)