Shadowed Throughput Provisioning for Computing Node Partition Relocation
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Solution Overview
Problem
Providers of computing services face challenges in managing computing capacity, including measuring current utilization and projecting future growth, which can lead to inefficient operation and system downtime when relocating services between computing devices.
Innovation Solution
The solution involves calculating a 'shadow-provisioned' capacity based on forecasted changes to provisioned capacity, allowing for a ranking system to identify suitable computing nodes for maintaining or relocating partitions, thereby minimizing relocation and associated data movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If service relocation is performed to balance capacity utilization, then capacity distribution is improved, but system downtime increases and data movement overhead increases
Solution Approach 1:
The system performs preliminary actions by calculating shadow-provisioned capacity values based on forecasted changes before actual relocation decisions are made. This allows the system to proactively identify suitable target computing devices and prepare capacity allocations, reducing the need for urgent relocations that cause downtime. The forecasted capacity values enable advance planning of service migrations during low-impact periods.
2Reliability
If capacity limits are imposed on services, then computing device efficiency is maintained, but service growth flexibility is reduced
Solution Approach 1:
The system implements dynamic capacity management by continuously calculating shadow-provisioned capacity values that reflect forecasted changes in service requirements. Capacity limits are not static but adapt over time based on predicted growth patterns and actual utilization trends. This allows the system to maintain efficiency constraints while automatically adjusting to service growth needs, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The system changes the parameter of capacity allocation by introducing shadow-provisioned capacity values that represent forecasted future states. These parameter changes enable the system to anticipate capacity needs and adjust allocations proactively, maintaining efficiency thresholds while accommodating service growth through dynamic parameter adjustment rather than rigid limits.
3Loss of time
If service relocation is avoided to maintain system stability, then system downtime is reduced, but capacity utilization efficiency decreases
Solution Approach 1:
The system employs feedback mechanisms by continuously monitoring actual capacity utilization and comparing it against shadow-provisioned capacity values that represent forecasted requirements. This feedback loop enables the system to identify when capacity imbalances occur and trigger targeted relocation decisions only when necessary, rather than performing frequent relocations. The feedback-driven approach maintains system stability while improving capacity utilization efficiency through data-driven decision-making.
Data Source
AI summary
Partitions of a hosted computing service may be maintained on a computing node. Processing of requests to access the partition may be limited to constrain capacity utilization to a provisioned amount of capacity reserved for the partition. A second, additional amount of capacity may be associated with the partition and may reflect potential future changes to the provisioned amount of capacity. A sum of provisioned and additional capacities associated with partitions on a computing node may be calculated. The computing node may be ranked, relative to other computing nodes, for maintaining new or relocated partitions based on the sum.