Predictive Rate Limiting for Cloud Bandwidth Optimization
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Solution Overview
Problem
Static rate limiting in cloud computing systems is inefficient as it throttles legitimate clients with burst requests during low activity periods, leading to unused bandwidth and inefficient resource allocation, while dynamic rate limiting faces challenges in balancing active and dormant client requests.
Innovation Solution
A system that dynamically adjusts rate limits using a combination of real-time and historical data models, incorporating machine learning to predict client request patterns and adaptively enforce rate limits based on current and past behavior, ensuring fair resource distribution and minimizing throttling of burst requests from active clients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rate limiting is used to prevent DoS attacks and ensure fair resource distribution, then system security and resource fairness are improved, but legitimate clients with burst requests are throttled and bandwidth utilization is reduced
Solution Approach 1:
The patent implements dynamic rate limiting by transitioning from fixed static thresholds to adaptive thresholds that automatically adjust based on real-time system conditions. The system monitors current load, historical patterns, and client behavior to dynamically set rate limits, allowing higher limits during low-utilization periods and lower limits during peak demand, thereby resolving the contradiction between security and bandwidth utilization
Solution Approach 2:
The system changes the parameter of rate limit thresholds from static constants to dynamic variables that are continuously adjusted based on system state. By monitoring metrics such as current request volume, response times, and resource utilization, the system modifies rate limit parameters in real-time to optimize both security and bandwidth efficiency
2Ease of operation
If static rate limiting is applied to all clients uniformly, then resource fairness is improved, but active clients during low activity periods are unnecessarily throttled
Solution Approach 1:
The patent applies local quality by differentiating rate limiting treatment for different clients based on their individual characteristics and current system context. Instead of uniform application, the system analyzes each client's historical behavior patterns, request types, and timing to apply customized rate limits, allowing active clients to receive more generous limits during low-activity periods while maintaining fairness through personalized rather than discriminatory treatment
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor client request patterns and system response to adjust rate limits accordingly. By analyzing historical data and real-time metrics, the system learns from past behavior and adapts rate limiting decisions, reducing unnecessary throttling of legitimate burst requests while maintaining overall fairness across the client base
3Productivity
If dynamic rate limiting is used to accommodate burst requests from active clients, then bandwidth utilization is improved, but it becomes difficult to balance requests from active and dormant clients
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing historical rate limit thresholds based on past system conditions and client behavior patterns. When a client makes a request, the system retrieves pre-computed thresholds rather than performing complex real-time calculations, significantly reducing computational complexity while still enabling dynamic adaptation to changing conditions
Solution Approach 2:
The system segments the rate limiting control into multiple independent components: historical threshold calculation, real-time condition monitoring, and decision logic. This modular segmentation allows each component to operate independently with well-defined interfaces, reducing overall system complexity while maintaining the ability to balance requests from active and dormant clients effectively
Data Source
AI summary
Examples include a method of predictive rate limiting for performing services requested by a client in a cloud computing system. The method includes receiving a request from a client for one of a plurality of services to be performed, the client belonging to an organization; and determining a current threshold for the organization by applying a real time data model and a historical data model, the real time data model generating a first threshold at least in part by determining a number of requests received from the organization over a first preceding period of time; the historical data model generating a second threshold, the historical data model being generated by applying a machine learning model to historical data stored during processing of previous requests for the plurality of services from the organization over a second preceding period of time, the current threshold being the average of the first threshold and the second threshold. The method further includes performing the requested service when the current threshold is not exceeded; and denying the request when the current threshold is exceeded.