Policy-Based Computing Resource Scaling Service
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Solution Overview
Problem
Existing remote computing resources lack efficient scaling capabilities, making it difficult for organizations to dynamically adjust computing resources based on demand, leading to potential performance issues and increased costs.
Innovation Solution
The implementation of an automated Computing Resource Scaling Service that allows clients to specify scaling policies based on resource utilization metrics, enabling dynamic scaling of computing resources through magnitude scaling policies and prioritization policies, which automatically adjust the number of computing resources in response to defined thresholds and alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If remote computing resources are utilized to forego establishment costs, then cost is reduced, but control to scale computing resources dynamically is limited
Solution Approach 1:
The system implements self-service through automated scaling services that monitor resource utilization metrics and automatically adjust computing resources based on predefined policies, eliminating the need for manual intervention and providing dynamic control while maintaining cost efficiency
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring resource utilization metrics and using this information to trigger scaling actions according to defined policies, enabling dynamic adaptation of computing resources to actual demand patterns
2Ease of operation
If computing resources are scaled manually, then control over resource allocation is maintained, but responsiveness to demand changes is slow
Solution Approach 1:
The system implements preliminary action by pre-defining scaling policies and thresholds before demand changes occur, enabling automated systems to immediately execute appropriate scaling actions when triggers are met, thus maintaining both control and rapid responsiveness
Solution Approach 2:
The automated scaling service performs self-service by autonomously monitoring metrics and executing scaling operations based on predefined policies, eliminating manual intervention delays while maintaining precise control over resource allocation
3Reliability
If computing resources are increased to handle peak demand, then performance is improved, but cost increases due to paying for unused resources during low demand
Solution Approach 1:
The system applies dynamics by implementing dynamic scaling that continuously adjusts computing resources based on real-time demand conditions, allowing the system to have high resources during peak demand for performance and low resources during low demand to reduce costs
Solution Approach 2:
The system uses feedback from resource utilization monitoring to dynamically adjust resource allocation, ensuring resources are scaled up when needed for performance and scaled down when not needed to optimize cost efficiency
Data Source
AI summary
Techniques are described for scaling a group of computing resources. A computing resource service receives a scaling policy for use in scaling the group of computing resources. The scaling policy specifies a target level for a resource utilization metric and magnitude-based changes to the group. The computing resource service receives information about a magnitude of a measurement for the resource utilization metric. The computing resource service determines, based at least in part on the scaling policy, one or more changes for the group and initiates the one or more changes in the group.