ACES develops a modular edge-services cloud which will support multiple architectural patterns for creating ad-hoc edge clouds in one site, and across multiple sites. The autopoiesis enables an autonomous configuration, orchestration and management collecting metrics and generating knowledge that intelligent agents use to execute edge-services and cloud requirements such as energy efficiency, availability, scalability, latency, data centricity, security and data protection. The event-driven data-centric architecture will be designed to have high levels of automation and autonomy and supports human operator control

The ACES solution builds on six modules: edge resource collaboration, service deployment, resource clustering at scale, workload placement, network control, workload optimization, in the view to optimize the data management, data storage, data replication

The ACES project will provide a distributed storage framework that has “knowledge” of the location of data in several ways: physical location of the data within the edge and across the different edge locations. Personal data will be linked to the current location the user is accessing ACES services from. ACES will develop and deploy data migration and replication solutions to enhance the reliability from some of the ad-hoc resources employed at the edge. Metadata about the data access requests will be logged into a distributed ledger (blockchain). Various access authorizations will grant different control over access to data and data placement on edge systems to ensure privacy. Range of services to be produced: Distributed storage and data movement; Data life cycle management for the edge; Data slicing and management at scale; Telemetry; Edge acceleration; AI security.

ACES aims to develop a set of tools for ACES platform operators to check ai/ml models against existing ones. Such set of tools for software developers address two areas: networking and observability; and offer distributed transaction monitoring, performance and latency optimization, root cause analysis, service dependency analysis, distributed context propagation. The following service will be developed in aces: application store; application monitoring; network function synthesis; visualisation of workload placement and orchestration.

Three different use-cases that are generic enough to be found in and representative for similar use-cases in other industry sectors. Use case 1: Market place & distribution dedicated to the energy grid; Use case 2: Distributed Process Management of the electric market management;
Use-case 3: An IoT based Asset Monitoring and Management the introduction of Advanced Metering Infrastructure data along with data from grid-edge sensors and GIS systems has allowed for faster outage detection, accurate outage prediction and more reliable investment planning.