📋 Tender Overview
Adaptive Scheduling And Deployments Of Data Intensive Workloads On Energy Efficient Edge To Cloud Continuum
Addressing The Data Deluge With Cutting-Edge Techniquesthe Current Proliferation Of Data-Intensive Applications Strains Existing Infrastructure, Leading To Inefficiencies And Suboptimal Resource Management. Traditional Approaches Struggle To Cope With The Dynamic Nature Of Modern Computing Demands, Especially In Edge And Cloud Environments. This Fragmentation Hampers Scalability, Elasticity And Portability, Hindering The Seamless Operation Of Distributed Applications. In This Context, The Eu-Funded Enact Project Will Develop A Cognitive Computing Continuum. It Harnesses Dynamic Graph Models To Visualise Real-Time Resource Status, Aiding Ai Models Like Graph Neural Networks And Deep Reinforcement Learning Agents In Suggesting Optimal Deployment Configurations. These Advancements Pave The Way For Intelligent Decision-Making Engines, Revolutionising Infrastructure Management. Enact Also Pioneers An Application Programming Model Supporting Self-Determining Applications For Diverse Resources.
Funded Under
Digital, Industry And Space
Total Cost
€ 0,00
Eu Contribution
€ 5 055 074,00
Coordinated By
Ethniko Kentro Erevnas Kai Technologikis Anaptyxis
Greece
Start Date
1 January 2024
End Date
31 December 2026
Objective
ENACT develops cutting-edge techniques and technology solutions to realise a Cognitive Computing Continuum (CCC) that can address the needs for optimal (edge and Cloud) resource management and dynamic scaling, elasticity, and portability of hyper-distributed data-intensive applications. At infrastructure level, the project brings visibility to distributed edge and Cloud resources by developing Dynamic Graph Models capable of capturing and visualising the real-time and historic status information, connectivity types, dependencies, energy consumption etc. from diverse edge and Cloud resources. The graph models are used by AI (Graph Neural Networks - GNN) models and Deep Reinforcement Learning (DRL) agents to suggest the optimal deployment configurations for hyper distributed applications considering their specific needs. The AI (GNN and DRL) models are packaged as an intelligent decision-making engine that can replace the scheduling component of open-source solutions such as KubeEdge. This will enable real-time and predictive management of distributed infrastructure and applications. To take full advantage of the potential (compute, storage, energy efficiency etc) opportunities in the CCC, ENACT will develop an innovative Application Programming Model (APM). The APM will support the development of distributed platform agnostic applications, capable of self-determining their optimal deployment and optimal execution configurations while taking advantage of diverse resources in the CCC. An SDK to develop APM-based distributed applications will be developed. Moreover, services for automatic (zero-touch provisioning-based)
