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 Karpenter
Predictive autonomous scaling of pods and nodes
Elastic GPU Service
EGS
Single/Multi-cluster and multi-cloud GPU provisioning and management platform
KubeAgent
KubeAgent
KubeAgent adds intelligence and autonomy to Kubernetes
EGS Resources
Explore Resources for Elastic GPU Service
Analyst Reports
Navigating Key Metrics for Growth and Success
Blog
Source for Trends, Tips, and Timely Topics
Documentation
The Blueprint for Mastering Tools and Processes
Customer Case Studies
Success stories from our valued customers and partners
WhitepaperDownload Whitepaper
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The Case for RL-based Load Balancing
Avesha's automated RL-based Load Balancing (LB) optimizes performance and minimizes footprint, preventing overloading and low latency of servers. The Avesha automated LB solution assigns requests to servers of corresponding length, and optimizes utilization of servers with differing capacity limits. The RL-based LB automatically reacts to changes in server and network state, and takes actions to prevent high latency.
Author(s):
Raj Nair
