Tenant Resource Optimizer for Kubernetes Pod Placement
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Solution Overview
Problem
Kubernetes multi-cluster environments face challenges in tenant resource optimization due to dynamic node utilization, inadequate descheduler policies, and lack of application performance awareness, leading to suboptimal pod placement and resource allocation.
Innovation Solution
A Tenant Resource Optimizer (TRO) collects and analyzes cluster and application information across multiple clusters, using offline schedulers and inter-tenant resource policies to adjust pod placement and resource utilization, ensuring optimal application performance and resource allocation based on real-time metrics and KPIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If multiple applications run on a single physical server without resource boundaries, then hardware costs are reduced, but resource allocation becomes unbalanced and application performance deteriorates
Solution Approach 1:
The patent segments physical servers into multiple virtual machines using virtualization technology. Each VM is assigned specific resource boundaries (CPU, memory, storage) through virtual resource pools, allowing multiple applications to run on single physical servers while maintaining resource allocation balance and application performance isolation.
2Reliability
If each application runs on a separate physical server, then application performance is guaranteed, but hardware costs increase and resource utilization decreases
Solution Approach 1:
The patent merges multiple physical servers into unified virtual resource pools that can be dynamically allocated to multiple applications. This consolidation allows hardware resources to be shared across applications while maintaining performance guarantees through resource boundaries, reducing overall hardware costs and improving utilization.
3Productivity
If virtualization is used to run multiple VMs on a single server, then resource utilization improves, but system complexity increases
Solution Approach 1:
The patent introduces a virtualization layer as an intermediary between physical hardware and applications. This layer abstracts complex virtualization management, presenting simplified interfaces for resource allocation and VM management, thereby improving resource utilization while masking system complexity from end users and administrators.
4Ease of operation
If static descheduler policies are used in Kubernetes, then scheduling simplicity is maintained, but resource optimization capability deteriorates
Solution Approach 1:
The patent transforms static descheduler policies into dynamic, adaptive policies that automatically adjust scheduling decisions based on real-time application performance metrics and resource utilization data. This enables continuous resource optimization while maintaining operational simplicity through automated policy management and performance-based adjustments.
5Reliability
If pod placement is optimized for current performance, then application performance improves, but adaptability to changing conditions deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor application performance metrics and resource utilization, using this information to dynamically adjust pod placement decisions. This closed-loop system maintains optimal application performance while adapting to changing conditions by incorporating real-time performance data into scheduling decisions.
Data Source
AI summary
A method of using a multi-cluster network is provided. The multi-cluster network has at least a plurality of clusters, where each cluster of the plurality of clusters has at least a node and a pod. The method includes collecting cluster and application information of the multi-cluster network. The cluster and application information includes at least a cluster capacity and an application performance metric. The application performance metric corresponds to at least an application. The method further includes analyzing the cluster and application information for a current pod arrangement on each cluster and adjusting at least the application performance metric based at least on the analyzed cluster and application information by taking at least a pod-based action achieving a new pod arrangement on a target cluster. An apparatus for using the multi-cluster network is also provided.