The CLEVER Project is tackling one of the most critical questions in AI for distributed environments: how do we make machine learning resilient when data comes from a wide range of sourcesāwith varying levels of trust? š Presented at theā¦
We’re excited to present a breakthrough from the CLEVER Project: a novel RDMA over InfiniBand Communicator that significantly accelerates GPGPU virtualization using GVirtuS ā enabling smarter, faster, and more efficient AI workloads across remote GPUs ā”š§ Ā š Why this matters: Traditionalā¦
Weāre thrilled to share our latest breakthrough from the CLEVER Project ā showcased at #OFC2024! Our research introduces Data Processing Units (DPUs) with embedded GPUs as a game-changing solution for programmable, high-performance optical metro/edge networks. šāØ These powerful DPUs, integratedā¦
CLEVER integrates P4-based programmable switches and smart NICs (DPUs) to revolutionize network security within the cloud-edge continuum. By leveraging hardware-accelerated technologies, CLEVER tackles modern challenges like DDoS attack mitigation and vulnerability detection with unmatched efficiency. Key Highlights š CLEVERās innovationsā¦
š£ Weāre proud to share another major step forward in the CLEVER Projectāour latest public deliverable is now available on Zenodo!Ā š Whatās it about? This document presents concrete use case scenarios and initial Key Performance Indicators (KPIs) that guide theā¦
At the heart of CLEVER lies a seamless, automated orchestration framework that connects computing and storage resources across the cloud-edge continuum. With a layered middleware architecture, CLEVER ensures smart service deployment and optimized resource utilization. š How Does It Work? ā¦
āØ Predicting the Future of Resource Allocation āØ In the CLEVER Project, we’re transforming how edge computing platforms handle resource management by integrating cutting-edge AI/ML models for proactive resource forecasting. Imagine a world where your applicationsāfrom autonomous vehicles to augmentedā¦
š Weāre excited to announce the release of our latest technical deliverable under the CLEVER Projectānow available on Zenodo!Ā š Ā š Whatās inside? This document outlines the core software architecture and initial integration of CLEVERās AI-based service orchestration platform acrossā¦
The Local Workload Placement component in the CLEVER platform introduces an ML-based scoring mechanism into Kubernetes’ scheduling pipeline, optimizing workload placement on worker nodes to meet platform objectives like energy efficiency, scalability and flexibility. Key Features and Workflow: Development Progress: ā¦
š This new release details our architecture design and methodology for enabling:ā Seamless orchestration across IoTāEdgeāCloud platformsā Smart, AI-driven resource optimizationā Secure and decentralized decision-making for critical computing environments š¼ Why this matters:Ā As digital infrastructures grow more complex, coordinated AIā¦
