cloud workload optimization and insights tool
For un instrument pentru monitorizarea performanței workload-urilor Kubernetes, the strongest matches are robusta-dev/krr (KRR is a focused CLI tool that analyzes Kubernetes), opencost/opencost (OpenCost is a Kubernetes-native cost allocation and multi-cloud cost) and fairwindsops/goldilocks (Goldilocks is a Kubernetes resource optimizer that provides a). Each is ranked by relevance to your query, popularity and recent activity.
Selectăm repository-uri open-source de pe GitHub care se potrivesc cu „workload insights”. Rezultatele sunt clasificate după relevanța față de căutarea ta — folosește filtrele de mai jos pentru a rafina rezultatele sau utilizează AI-ul.
Rezultate pentru „un instrument pentru monitorizarea performanței workload-urilor Kubernetes”
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- robusta-dev/krr
robusta-dev/krr
4,466Vezi pe GitHubKRR is an open-source tool for analyzing Kubernetes resource requests and recommendations. It evaluates how pods are currently configured and provides suggestions for optimizing CPU and memory allocations based on actual usage patterns. The project focuses on helping teams right-size their Kubernetes workloads by identifying over-provisioned and under-provisioned resources. It scans clusters and generates reports that highlight where adjustments can reduce costs or improve performance without compromising reliability. KRR is distributed as a Python command-line tool that can be run directly
KRR is a focused CLI tool that analyzes Kubernetes resource usage and provides rightsizing recommendations, fitting the core workload optimization need, though it lacks multi-cloud support, dashboards, and alerting features.
PythonResource Recommendation EnginesResource Recommendation StrategiesResource Recommendation Reporting - opencost/opencost
opencost/opencost
6,605Vezi pe GitHubOpenCost is an open-source tool for monitoring and allocating Kubernetes and cloud infrastructure costs. It provides real-time visibility into spending by distributing asset costs to workloads based on resource requests and usage, breaking down spend by namespace, deployment, pod, and label. The system functions as both a Kubernetes cost allocation engine and a multi-cloud cost analyzer, ingesting billing data from AWS, Azure, and GCP to present unified cost metrics alongside cluster costs. The tool distinguishes itself through its allocation-based cost model, which compares requested versus
OpenCost is a Kubernetes-native cost allocation and multi-cloud cost analyzer that gives you detailed visibility into resource usage and spending by namespace, deployment, and label, making it a strong fit for cost-centric workload optimization, though it leans more on cost than broad performance or anomaly detection.
GoCloud Cost AllocationsGrafana Cost DashboardsKubernetes Cost Allocations - fairwindsops/goldilocks
FairwindsOps/goldilocks
3,144Vezi pe GitHubGoldilocks is a suite of tools for analyzing resource usage and managing autoscaling policies in Kubernetes. It functions as a resource optimizer and capacity planner, providing a dashboard and command line interface to analyze workload utilization patterns and suggest efficient CPU and memory requests and limits for containers. The project distinguishes itself by visualizing recommendations from the Vertical Pod Autoscaler via a web interface and providing a lifecycle manager to create and configure these autoscaler objects. It includes capabilities to aggregate resource recommendations acro
Goldilocks is a Kubernetes resource optimizer that provides a dashboard and VPA-based rightsizing recommendations, fitting the workload optimization and insights category; however, it lacks cost allocation, multi-cloud support, and anomaly detection, making it a narrower but valid choice.
GoResource Recommendation Engines
