Dynamic Invocation Control via Residual Capability Estimation
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Solution Overview
Problem
In cloud computing environments, software components often face challenges in managing invocations due to unpredictable workloads, leading to overloading or underutilization, especially in legacy systems lacking ingress/egress limiting mechanisms, which results in performance degradation and resource wastage.
Innovation Solution
A method that associates source components with corresponding rates for invoking a target component, monitors invocations, and estimates a serving probability to dynamically enable or deny invocations based on the target component's residual capability, optimizing the sharing of resources among source components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If source components are allowed to invoke target components freely without rate limiting, then the target component can serve all requests, but the target component becomes overloaded and performance degrades
Solution Approach 1:
The patent implements dynamic rate limiting by continuously monitoring the target component's residual capability and adjusting the source rates accordingly. The system transitions from static rate limits to dynamic adaptation based on real-time conditions, allowing the target component to handle varying workloads while maintaining performance stability through the serving probability mechanism.
Solution Approach 2:
The patent changes the parameter of rate limiting from fixed values to probabilistic serving probabilities. By estimating the residual capability of the target component and converting it to serving probabilities, the system dynamically adjusts invocation rates based on current system state, resolving the contradiction between throughput and stability.
2Reliability
If strict rate limiting is applied to source components, then the target component is protected from overloading, but source components experience invocation denials and resource wastage
Solution Approach 1:
The patent implements a feedback mechanism where the target component's residual capability is continuously monitored and fed back to adjust source rates. This feedback loop allows the system to protect the target component from overloading while minimizing invocation denials by adapting rates based on real-time capacity information rather than applying fixed limits.
Solution Approach 2:
The system transforms strict rate limiting parameters into probabilistic serving probabilities. Instead of binary allow/deny decisions based on fixed thresholds, the patent uses continuous probability values that reflect the target component's actual capacity, thereby reducing unnecessary denials while maintaining protection.
3Device complexity
If source rates are allocated statically among source components, then resource allocation is simple to manage, but the system cannot adapt to changing workload conditions
Solution Approach 1:
The patent enables the system to self-adjust rate allocations automatically based on monitored workload conditions and target component capacity. Instead of requiring manual reconfiguration, the system autonomously adapts source rates by estimating residual capability and calculating appropriate serving probabilities, achieving high adaptability without proportionally increasing management complexity.
Solution Approach 2:
The patent transitions from static rate allocation to dynamic adaptation. Source rates are continuously adjusted based on real-time monitoring of invocations and target component capacity, allowing the system to adapt to changing workload conditions while maintaining manageable complexity through automated processes.
4Measurement precision
If the system monitors all invocations to estimate residual capability, then accurate rate control is achieved, but the monitoring overhead increases system complexity
Solution Approach 1:
The patent makes the monitoring system multi-functional by using invocation monitoring not only for rate control but also for estimating residual capability. This universal approach consolidates multiple functions into a single monitoring infrastructure, achieving accurate estimation without proportionally increasing system complexity.
Data Source
AI summary
A solution is proposed for controlling invocations of a target component by multiple source components in a software application. A corresponding method comprises associating a plurality of source components in a software application with one or more corresponding source rates for invoking a target component in the software application; monitoring corresponding invocations of the target component by a number of instances of the plurality of source components; receiving an enablement request for a new invocation of the target component from a current instance of a current source component; verifying an enablement of the new invocation; estimating a serving probability indicative of a residual capability of the target component to serve the new invocation; and enabling the new invocation according to the serving probability.