KubeSlice
Avesha Enterprise for KubeSlice
Service Connectivity Layer for managing fleet of clusters for better application performance
KubeBurst
On demand cloud capacity for datacenters
KubeTally
Multi - cluster chargeback by application and teams
KubeAccess
Service gateway for multi-cloud applications
Smart Scaler
Smart Scaler
Predictive autoscaling based on application behaviors
Smart Event Scaler
AI assisted infrastructure scaling for peak traffic days
Smart Karpenter
Predictive autonomous scaling of pods and nodes
Elastic GPU Service
EGS
Single/Multi-cluster and multi-cloud GPU provisioning and management platform
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Elastic GPU Service (EGS) -- Workload Automation, Optimization, Cost Reduction, and Observability
Despite advancements in ML scheduling tools like KubeFlow, optimizing GPU and CPU usage remains difficult. Mismatches between resource management and workload orchestration cause idle GPUs: creating delays, and inefficiencies in large-scale setups. Current GPU allocation relies on manual adjustment and lacks dynamic adaptation. Without standardized GPU rating and sharing approaches, advanced ML schedulers still struggle with scheduling, leading to bottlenecks and resource waste.
Author(s):
Raj Nair
