Entropy Load Balancing for Quality-Matched Client Provisioning
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Solution Overview
Problem
Existing systems struggle to scale high-quality entropy sources efficiently to meet varying demands across multiple endpoints in large organizations, leading to potential downtime and wastage of resources, especially when demand exceeds supply.
Innovation Solution
An entropy service load balancing system that combines and optimizes entropy streams from multiple sources, including on-premises and off-premises providers, using an 'entropy market maker' to distribute resources based on demand and quality, ensuring high throughput and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-quality entropy sources are used to meet security demands, then security quality is improved, but resource availability deteriorates due to limited supply
Solution Approach 1:
The system segments entropy sources by quality levels and segments client requests by security requirements. Different quality-tier entropy sources are matched to different security-level applications, allowing limited high-quality entropy to be distributed efficiently among multiple clients with varying needs rather than being consumed by a single application.
Solution Approach 2:
The patent implements local quality by providing different quality levels of entropy to different clients based on their specific requirements. High-security applications receive high-quality entropy while lower-security applications receive adequate but lower-quality entropy, optimizing the overall utilization of limited entropy resources across the system.
2Productivity
If entropy demand exceeds supply in large organizations, then service coverage is improved, but system reliability deteriorates due to blocking or repeated numbers
Solution Approach 1:
The system dynamically adjusts entropy allocation based on real-time demand and supply conditions. When demand exceeds supply, the load balancer dynamically redirects requests to alternative entropy sources or adjusts quality levels to maintain service continuity, preventing blocking and ensuring system availability while maximizing service coverage.
Solution Approach 2:
The patent introduces an entropy load balancer as an intermediary between entropy sources and clients. This mediator manages the mismatch between limited entropy supply and high demand by implementing intelligent routing, quality matching, and load distribution, thereby maintaining both service coverage and system reliability even when demand exceeds supply.
3Reliability
If high-quality entropy is provided to all clients, then security quality is improved, but resource efficiency deteriorates due to wastage on low-security applications
Solution Approach 1:
The system applies local quality by matching entropy quality levels to the specific security requirements of each client. High-quality entropy is allocated only to high-security applications that genuinely need it, while lower-quality entropy suffices for low-security applications, thereby eliminating wastage while maintaining adequate security quality where required.
Solution Approach 2:
The patent changes the quality parameter of entropy provided to different clients based on their security requirements. By adjusting the entropy quality parameter dynamically according to application needs, the system avoids the wastage associated with providing uniformly high-quality entropy to all clients while ensuring that security-critical applications receive appropriate quality levels.
4Quantity of substance
If multiple entropy sources are distributed without coordination, then resource availability is improved, but system complexity deteriorates due to lack of load balancing and quality management
Solution Approach 1:
The entropy load balancer serves multiple functions simultaneously: it performs load balancing across multiple entropy sources, manages quality matching between sources and clients, monitors system state, and implements intelligent routing. This multi-functionality consolidates what would otherwise be separate complex systems into a single coordinated platform, increasing entropy availability while managing rather than increasing overall system complexity.
Solution Approach 2:
The system implements feedback mechanisms where the load balancer continuously monitors the state of entropy sources and client demands, adjusting its routing and allocation decisions in real-time. This feedback-driven coordination enables multiple entropy sources to work together efficiently without requiring complex manual configuration or management, as the system self-regulates based on observed conditions.
Data Source
AI summary
Systems and methods are disclosed herein for providing entropy to clients. An example method includes receiving a first datagram comprising a first request for entropy and a set of requirements comprising an indication of an endpoint device, an indication of a number of bits of entropy, and an indication of a quality of the entropy. The example method further includes determining, by routing circuitry, whether a first entropy source from a set of entropy sources meets the set of requirements and determining, by priority circuitry, a first priority value based on the first request. The example method further includes determining, by the priority circuitry, whether the first priority value is a greatest available priority value and causing generation, by entropy quality circuitry, of an admixture of a plurality of entropy sources from the set of entropy sources. The example method further includes providing the admixture to the endpoint device.