Remote Control Plane for Capacity Bursting in Provider Networks
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Solution Overview
Problem
As data centers scale, managing physical computing resources becomes increasingly complex due to the need for efficient and secure sharing of resources across multiple users and locations, with existing systems requiring colocation of control planes and resources, limiting flexibility and resilience in case of failures or capacity issues.
Innovation Solution
Implementing remote control planes that can manage resources from a different area, enabling automated failover and capacity bursting, allowing resources to be managed remotely and decoupled from their physical location, with a control plane monitoring service to ensure availability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If control planes are collocated with computing resources, then management and control is simplified, but flexibility and resilience are reduced
Solution Approach 1:
The system segments the control plane from the data plane by introducing a remote control plane that operates independently from the computing resources. The control plane functions are separated into a dedicated control plane instance that can remotely manage multiple data plane resources across different locations, enabling independent scaling and management of control functions versus computing functions.
Solution Approach 2:
A remote control plane acts as an intermediary between administrators and the distributed computing resources. This intermediary can be located in a different physical location than the resources it manages, providing centralized control while maintaining flexibility in resource placement and enabling failover capabilities through geographic distribution.
2Ease of operation
If control planes are collocated with computing resources, then local control is improved, but resilience to failures is reduced
Solution Approach 1:
Multiple data plane resources in different geographic locations are merged under the management of a single remote control plane. This consolidation allows the control plane to orchestrate failover across locations while maintaining local execution of computing workloads, combining the benefits of distributed resilience with centralized control efficiency.
Solution Approach 2:
The system adds a geographic dimension to control plane deployment by allowing control planes to operate remotely from the data plane resources they manage. This dimensional separation enables control functions to be located in data center locations optimized for control operations while resources are distributed across multiple geographic locations for resilience.
3Speed
If control planes manage only local resources, then network latency is reduced, but capacity scaling is limited
Solution Approach 1:
The remote control plane is designed with universal functionality to manage diverse computing resources across multiple locations and resource types. It can provision, configure, and monitor various virtual machine instances, storage resources, and networking components remotely, providing a single control interface for heterogeneous distributed infrastructure.
Solution Approach 2:
The system performs preliminary actions by pre-configuring remote control planes with the authority and capabilities to manage specific resource pools before failures or capacity needs occur. This advance preparation enables immediate failover and rapid capacity scaling without requiring control plane reconfiguration during critical events.
4Productivity
If computing resources are shared across multiple users, then resource utilization efficiency is improved, but management complexity increases
Solution Approach 1:
The management complexity is extracted from the distributed resources and consolidated into the remote control plane. The control plane handles user authentication, resource allocation, configuration management, and monitoring functions centrally, while the computing resources themselves remain simple execution environments focused on providing computational capacity.
Data Source
AI summary
Techniques for capacity bursting using a remote control plane are described. A method of capacity bursting using a remote control plane includes determining that a first control plane associated with a first area of a provider network has insufficient capacity to manage a plurality of resources in the first area, sending a request for a second control plane in a second area of the provider network to manage at least a first portion of the plurality of resources in the first area, the second control plane identified based at least on a backup hierarchy, and updating management of at least the first portion of the resources in the first area from the first control plane to the second control plane, wherein one or more references to endpoints of the first control plane are updated to be references to endpoints of the second control plane for at least the first portion of the resources managed by the second control plane.