Worker Pool Management for Cold Start Latency
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Solution Overview
Problem
Existing computing systems face delays in processing work requests due to the time required to initialize workers when a work request is received, which can lead to reduced throughput and increased latency in executing program code functions.
Innovation Solution
Initializing workers in advance and maintaining them in a worker pool, with a reservation policy to manage the number of workers based on anticipated demand, allows for immediate execution of work requests without the need for cold starts, thereby enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If workers are initialized when a work request is received, then system resource usage is optimized, but processing time increases due to cold starts
Solution Approach 1:
The system pre-initializes workers before work requests arrive and maintains them in a worker pool in a suspended state. When work requests are received, pre-initialized workers are immediately assigned and activated, eliminating cold start delays. This preliminary action ensures workers are ready to execute immediately while resources are only fully consumed when actually needed.
2Speed
If workers are pre-initialized and maintained in a worker pool, then processing speed improves, but system resource consumption increases
Solution Approach 1:
The system dynamically adjusts worker states based on demand. Workers are pre-initialized and kept in a suspended state in the worker pool, consuming minimal resources. When work requests arrive, workers are activated and transition to an executing state, consuming full resources only when needed. This dynamic state management allows fast processing while optimizing resource consumption.
3Productivity
If a reservation policy is implemented to manage worker numbers, then resource allocation efficiency improves, but system complexity increases
Solution Approach 1:
The system implements a reservation policy that monitors work request patterns and feedback from the worker pool status. Based on this feedback, the system dynamically adjusts the number of pre-initialized workers in the pool, ensuring optimal resource allocation. This feedback mechanism improves productivity by matching worker availability to actual demand while managing complexity through automated adjustments.
Data Source
AI summary
A technology is described for executing work requests. In one example, a worker configured to execute an instance of a program code function of a defined function type that corresponds to an expected work request to execute the program code function may be initialized in computer memory, and the worker may be added to a worker pool. A worker reservation policy may determine how many workers may be held in the worker pool based in part on an anticipated demand for the workers. A work request to execute the program code function may be received, and in response, a work item may be generated for execution by the worker. The worker may be identified in the worker pool, and the worker and the work item may be assigned to a work item manager to enable the work item manager to invoke the worker to execute the work item.