Predictive Hardware Load Balancing via User Profile Learning
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Solution Overview
Problem
Current load balancers in cloud or hybrid cloud environments rely on static policies and imprecise data, leading to inefficiencies and unreliability in workload and traffic distribution.
Innovation Solution
A predictive hardware load balancing method that uses a learning model to associate users with user profiles based on their attributes, allocating system resources accordingly to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static policies are used for load balancing, then the system is simple to implement, but the reliability and efficiency of workload distribution deteriorates
Solution Approach 1:
The patent implements dynamic load balancing by transitioning from static policies to a dynamic system that continuously monitors user behavior patterns, resource utilization metrics, and workload characteristics. The load balancer adapts its decisions in real-time based on observed data, allowing the system to respond to changing conditions and optimize reliability without requiring complex manual reconfiguration.
Solution Approach 2:
The system incorporates feedback mechanisms where user behavior data, resource usage statistics, and workload performance metrics are continuously collected and fed back into the load balancing algorithm. This feedback loop enables the system to learn from past decisions and adjust future workload distribution automatically, improving reliability through data-driven optimization rather than complex static rule sets.
2Productivity
If imprecise data is used for load balancing decisions, then the system requires less data processing, but the efficiency of traffic distribution deteriorates
Solution Approach 1:
The system performs preliminary data collection and processing by continuously monitoring and storing user behavior patterns, resource utilization metrics, and workload characteristics before load balancing decisions are made. This pre-processing of data allows the system to make efficient, data-driven decisions without requiring extensive real-time data analysis, thereby improving traffic distribution efficiency while controlling processing time through proactive data preparation.
3Adaptability or versatility
If dynamic resource allocation based on user profiles is implemented, then the adaptability to user needs improves, but the device complexity increases
Solution Approach 1:
The load balancing system implements self-service by automatically creating user profiles based on observed behavior patterns and autonomously making resource allocation decisions without manual intervention. The system self-adjusts workload distribution based on learned user preferences and real-time conditions, providing high adaptability to user needs while avoiding the complexity of manual configuration and rule-setting by allowing the system to manage itself through automated machine learning processes.
Data Source
AI summary
A method for predictive hardware load balancing based on user behavior is presented. The method receives, by a processor, a request from a user to access a cloud computing system. In response to the request, the method associates the user with a user profile using a learning model that uses machine learning to characterize attributes of the user and uses the attributes of the user to determine which user profile of a plurality of user profiles to associate with the user. Each of the user profiles is associated with a set of attributes and a set of system resources. The method allocates system resources to the user based on the user profile.