Resource Token Queues for Service Thread Throughput
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Solution Overview
Problem
Existing Web service and RPC systems face performance issues due to blocking of service threads while waiting for downstream services, database connections, or other constrained resources, leading to reduced throughput and increased complexity when addressing resource unavailability or slowness.
Innovation Solution
Implementing a system with multiple resource queues and service request queues, where requests collect resource tokens from token buckets based on dependencies, allowing prioritization and dynamic allocation of constrained resources, and employing a stochastic fair queuing algorithm to manage requests effectively during traffic spikes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service threads wait for constrained resources (downstream services, database connections, I/O channels), then the service can complete its work correctly, but the service threads become blocked and throughput decreases
Solution Approach 1:
The system performs preliminary actions by pre-allocating resource tokens to service requests before they actually need the resources. Token buckets are pre-configured with tokens representing available constrained resources, and requests collect tokens in advance as they move through the queueing system, preventing blocking when resources are needed for actual service execution.
Solution Approach 2:
The service system is segmented into multiple independent queues (resource queues, service request queues) that operate separately. Each queue manages specific resource types or service categories independently, allowing different segments of the system to progress without being blocked by bottlenecks in other segments. This is implemented through separate token buckets and queueing structures for different resource types.
2Reliability
If asynchronous I/O mechanisms and timeouts are added to handle resource unavailability, then service blocking is reduced, but system complexity increases and throughput significantly decreases
Solution Approach 1:
Token buckets serve as intermediary structures between service requests and constrained resources. Instead of directly managing complex asynchronous I/O operations and timeout logic, the system uses token buckets as mediators that abstract resource availability into simple token possession. Requests interact with the token bucket interface rather than directly with downstream services, database connections, or I/O channels, simplifying the service implementation.
Solution Approach 2:
The system implements self-service through automatic token management. Token buckets automatically refill based on observed system performance and resource availability, without requiring manual intervention or complex control logic. The admission control subsystem automatically monitors queue lengths and performance metrics, dynamically adjusting token allocation to maintain optimal throughput while preventing blocking.
3Reliability
If admission control throttling is used to prevent too many requests, then resource overload is avoided, but throughput is significantly reduced
Solution Approach 1:
The admission control system is dynamic rather than static. Token bucket capacities and refill rates are not fixed but are continuously adjusted based on observed system performance, queue lengths, and resource availability. This dynamic adaptation allows the system to maximize throughput during periods of resource availability while automatically preventing overload when resources are constrained, eliminating the need for conservative static throttling.
Solution Approach 2:
The system implements feedback loops where performance metrics and resource availability information are continuously monitored and fed back to the token bucket management. The admission control subsystem observes queue lengths, service completion rates, and resource response times, then adjusts token allocation accordingly. This feedback mechanism enables the system to maintain high throughput when resources are available while automatically reducing admission when resources are constrained, without requiring manual throttling configuration.
Data Source
AI summary
A system for allocating constrained resources (e.g., downstream services, execution threads, database connections, input/output channels, computational resources, and/or memory) to requested services that are dependent on those resources may include multiple resource queues, each of which maintains a queue of requests for a respective constrained resource, and multiple service request queues, from which requests may be subsequently serviced. As each request reaches the head of a resource queue, it may receive a resource token for a respective constrained resource. Once the request has collected resource tokens for each of the constrained resources on which it depends, the request may be passed to a service request queue that maintains a queue of requests of a particular type. Requests in the multiple service request queues may be serviced on a round-robin or weighted round-robin basis. The number of tokens available for each constrained resource may be modified based on observed system performance.