Adaptive Rate Component for Host Rate Limiting
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Solution Overview
Problem
Distributed computing systems face challenges in efficiently managing workload and rate limits across multiple hosts, leading to potential system overload and downtime, as existing methods require frequent communication and consensus among hosts, causing computational burdens.
Innovation Solution
Implementing an adaptive rate component within each host to determine and update its rate limit based on a fleet-wide service rate value and host count, allowing hosts to independently manage rate limits and reduce computational overhead by periodically updating and caching these values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workload management is implemented based on limiting throughput or bandwidth of computing resources, then system overload is prevented, but computational burden increases due to frequent communication and consensus among hosts
Solution Approach 1:
Each host independently determines its own rate limit by retrieving the fleet-wide service rate value and host count from a data store, then calculating and caching its rate limit locally. This eliminates the need for hosts to communicate with each other or a central workload manager to establish rate limits, as each host serves itself by autonomously computing its rate limit based on publicly available fleet parameters.
Solution Approach 2:
The workload management function is segmented from centralized control to distributed autonomous operation. Instead of a single workload manager coordinating all hosts, each host becomes an independent decision-making unit that calculates and enforces its own rate limit, dividing the management responsibility across all hosts rather than concentrating it in one component.
2Adaptability or versatility
If rate limits are dynamically adjusted for each host, then service level agreements are maintained, but inter-host communication overhead increases
Solution Approach 1:
A data store acts as an intermediary between the fleet-wide service rate value/host count and individual host rate limits. Instead of hosts communicating directly with each other or a central manager to adjust rate limits, they independently query the data store for the fleet-wide parameters and calculate their own rate limits, using the data store as a shared information source rather than requiring peer-to-peer communication.
Solution Approach 2:
The system pre-establishes the fleet-wide service rate value and host count in a data store before hosts need to determine their rate limits. This preliminary preparation of fleet parameters allows hosts to independently and quickly calculate their rate limits without needing to communicate with other hosts at the moment rate limit adjustment is needed, reducing real-time communication overhead.
3Adaptability or versatility
If hosts communicate to determine rate limits, then consensus is reached, but system scalability is reduced due to computational burden
Solution Approach 1:
Each host independently determines its rate limit without needing to communicate with or reach consensus with other hosts. The host retrieves fleet-wide parameters from a data store, calculates its own rate limit autonomously, and caches it locally. This self-service approach eliminates the communication and consensus-building overhead that would otherwise limit system scalability.
Solution Approach 2:
The consensus-determination function is extracted from the system by removing the need for inter-host communication entirely. Instead of hosts exchanging messages to agree on rate limits, each host independently computes its rate limit using publicly available fleet parameters from a data store, extracting the consensus problem from the system architecture and replacing it with independent calculation.
Data Source
AI summary
A system for adjusting individual host rate limits based on a fleet-wide service rate limit and a fleet host count. The system includes a fleet of hosts for a service. Individual hosts of the fleet of hosts respectively include application logic, a host rate limiter to limit interactions between the application logic and another service according to a host rate limit for the individual host, and an adaptive rate component. The adaptive rate component updates a host entry in a host table to indicate a current heartbeat timestamp for the individual host. The adaptive rate component retrieves the fleet-wide service rate value and the fleet host count value. The adaptive rate component determines the host rate limit for the individual host based on the fleet-wide service rate value and the fleet host count value. The adaptive rate component provides the host rate limit to the host rate limiter.