Generational Compute Pool Management for Cold-Start Control
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Solution Overview
Problem
Cloud providers face challenges in managing compute resource pools efficiently, leading to increased costs and latency due to unpredictable user demand, as they either maintain insufficient or excessive compute capacity, which is inefficient and burdensome for users.
Innovation Solution
Implementing generational management of compute resource pools based on actual usage, creating and promoting pools with different capacity retention and replenishment policies to optimize resource utilization and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the cloud provider maintains an excessive amount of compute capacity in the pool, then the availability of compute resources is improved, but the cost increases due to unused compute capacity
Solution Approach 1:
The patent segments the compute resource pool into multiple generations (first generation, second generation, third generation) with different retention policies. Frequently used compute instances are retained in earlier generations with longer retention periods, while less frequently used instances are moved to later generations with shorter retention periods. This segmentation allows the system to maintain adequate availability for common workloads while reducing costs by not over-provisioning for rare scenarios.
Solution Approach 2:
The patent changes the retention parameter dynamically based on usage patterns. Compute instances transition through generational stages with progressively shorter retention periods. The system monitors usage frequency and adjusts which generation retains instances for how long, optimizing the balance between availability and cost by matching retention duration to actual demand patterns.
2Loss of energy
If the cloud provider maintains an insufficient amount of compute capacity in the pool, then the cost is reduced, but the latency increases due to cold starts
Solution Approach 1:
The patent performs preliminary actions by pre-warming compute instances in the first generation pool before they are needed. By maintaining a pool of pre-initialized compute instances that have already undergone cold start, the system ensures that when these instances are allocated to user code, no additional cold start latency occurs. This preliminary preparation eliminates the time penalty for frequently requested workloads.
Solution Approach 2:
The patent implements dynamic pool management where compute instances are continuously monitored and transitioned between generations based on usage patterns. The system dynamically adjusts which instances remain in the warm pool versus being retired, optimizing the balance between having enough pre-warmed instances to avoid cold starts and not maintaining too many to incur unnecessary costs.
3Adaptability or versatility
If users manage and scale compute instances themselves, then the control over compute capacity is improved, but the operational burden increases
Solution Approach 1:
The patent implements self-service by allowing users to request compute capacity without needing to manually manage the underlying instances. The generational pool manager automatically handles instance creation, warming, allocation, and retirement based on usage patterns. Users simply request compute resources through the serverless interface, and the system autonomously manages the complexity of instance lifecycle management, eliminating the operational burden while preserving control through high-level resource specification.
Data Source
AI summary
Provided is a system for managing a pool of compute resources usable to execute user codes such as containerized software applications. The pool may include a plurality of sub-pools of compute instances that are each suited to handle a specific type of code execution requests and/or a specific set of capacity or resource configurations. For example, when a code execution request is received, the system may determine compute capacity from which sub-pool would be best suited to handle the request, and direct the request to that sub-pool (or execute the user code using compute capacity from that sub-pool). Each sub-pool may be assigned a generational indicator that indicates its level of use (e.g., rarely used, moderately use, frequently used, etc.) and/or its age (e.g., brand new, young, old, etc.). As a sub-pool experiences continued use, the sub-pool may be promoted and its retention/replenishment policy may be updated accordingly.