Multi-tenant Control Plane Management via Dynamic Thread Spawning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing multiple tenants on a cloud computing platform is challenging due to the difficulty in isolating computing resources and the inefficiency of maintaining separate cluster control planes for each tenant.
Innovation Solution
Implementing a cloud provider control plane that manages multiple tenants with dedicated control planes as threads of a process, allowing for the spawning and destruction of threads to handle API requests, and using a shared cache for immutable data to reduce memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate nodes are designated for each cluster control plane, then tenant isolation is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
Multiple cluster control planes are merged into a single shared node, where they coexist and share computational resources. The control planes are implemented as isolated processes or containers within the same node, allowing resource sharing while maintaining functional separation for different tenants.
Solution Approach 2:
A single node is designed to host multiple cluster control planes simultaneously, making it a multi-functional resource that serves multiple tenants. The node infrastructure (CPU, memory, storage) is universally shared across different control plane instances through virtualization or containerization technologies.
2Productivity
If computing resources are shared on the same cluster nodes, then resource utilization efficiency is improved, but tenant isolation deteriorates
Solution Approach 1:
The shared node is segmented into isolated execution environments for different tenants' control planes. Each control plane operates in its own isolated process space or container, with dedicated resource quotas and access controls, ensuring that tenants cannot interfere with each other while sharing the underlying hardware resources.
Solution Approach 2:
An intermediary layer (such as a container runtime or virtualization hypervisor) is introduced between the physical hardware and the control planes. This intermediary manages resource allocation, enforces isolation policies, and mediates access to shared resources, allowing efficient resource sharing while maintaining strong tenant isolation guarantees.
3Reliability
If fixed minimum node allocation is used for each control plane, then tenant isolation is improved, but cost efficiency deteriorates
Solution Approach 1:
The system transitions from static, fixed minimum node allocation to dynamic resource allocation. Control planes can dynamically share resources when idle and access additional resources when needed, with allocation adjusted in real-time based on actual demand rather than predetermined minimums.
Solution Approach 2:
Resource allocation parameters change from fixed minimum values to flexible, demand-based values. The system monitors resource usage patterns and adjusts allocation parameters dynamically, allowing small tenants to use fewer resources than the traditional minimum while maintaining isolation through software-based enforcement rather than hardware partitioning.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods, systems, devices, and computer-readable storage media for managing a multi-tenant cloud computing platform. Rather than maintain cluster control planes separately for each tenant, a cloud provider control plane implemented on one or more nodes can manage the creation, allocation, execution, and destruction of cluster control planes in an on-demand way. Computing resources can be used more efficiently at least because resources are not under-utilized. Adding additional tenants can be done with zero or small marginal cost per-tenant, because cluster control planes for servicing requests by the tenant are executed in response to a request, but destroyed when a tenant for the plane is idle. Multiple instances of an API server implementing a platform API for communicating between devices of the platform and clusters of computing resources can be generated and modified on a per-tenant basis.