Realm Risk Model for Dynamic Resource Distribution
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Solution Overview
Problem
Traditional approaches to distributing traffic across multiple resources in a pod do not account for variability in performance and requirements of individual realms, leading to overburdened and underutilized resources, resulting in inefficient resource utilization and potential performance degradation.
Innovation Solution
A realm risk model is used to assign risk scores based on traffic and response time trends, allowing for dynamic distribution of realms across resources to maximize resource utilization and minimize risk, ensuring that higher-risk realms receive sufficient resources and are less likely to impact others, thereby optimizing pod performance and capacity planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traffic is distributed across multiple physical servers using load balancing, then the website can handle higher traffic volume, but resource utilization becomes inefficient with some servers overburdened and others underutilized
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring realm performance metrics and adjusting risk scores in real-time. The load balancing system dynamically reassigns realms between resources based on changing conditions, transforming the static resource allocation into an adaptive system that responds to performance variations, thereby optimizing resource utilization while maintaining traffic handling capacity
Solution Approach 2:
The system changes the parameter of resource assignment by introducing risk scores that combine multiple performance metrics (error rates, response times, traffic patterns). This parameter transformation enables the load balancer to make informed decisions about realm distribution, assigning realms to resources based on composite risk assessments rather than simple round-robin or random distribution, thus improving both traffic handling and resource efficiency
2Adaptability or versatility
If a fixed number of resources are pooled into a pod to serve variable number of realms, then resource sharing is achieved, but variability in realm performance requirements causes uneven resource distribution
Solution Approach 1:
The patent applies local quality by assigning unique risk scores to each realm-resource assignment based on that specific pairing's performance characteristics. Instead of treating all realms uniformly, the system evaluates each realm's error rates, response times, and traffic patterns individually, allowing tailored resource allocation decisions that account for local performance variations and maintain overall system reliability
Solution Approach 2:
The system implements continuous feedback loops by monitoring realm performance metrics and updating risk scores in real-time. This feedback mechanism allows the load balancer to detect performance degradation or anomalies and automatically adjust realm assignments to maintain consistent service levels across all realms, resolving the reliability issue caused by variable performance requirements
3Ease of operation
If traditional load balancing distributes realms across resources without considering individual realm variability, then simple distribution is achieved, but some resources become overburdened while others remain underutilized
Solution Approach 1:
The system enables self-service by allowing the load balancing mechanism to automatically calculate risk scores and make realm assignment decisions without manual intervention. The automated monitoring and reassignment process eliminates the need for manual load balancing configuration while achieving optimized resource utilization, maintaining operational simplicity through automation rather than complexity
Data Source
AI summary
Methods, computer readable media, and devices for distributing risk of multiple realms across multiple resources based on a realm risk model are disclosed. One method may include determining a time score based on an average response, a traffic score based on an average client request rate, and a risk score based on the time score and the traffic score for a plurality of realms, distributing the plurality of realms across a fixed number of resources based on the risk scores of the plurality of realms, and in response to a change in a risk score of a realm, redistributing the plurality of realms across the fixed number of resources based on a difference between a maximum risk score and a minimum risk score.