Cognitive Load Balancer Using Sender Status Analytics
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Solution Overview
Problem
Conventional load balancing algorithms lack end-to-end knowledge of communication and workload streams, leading to inefficient workload distribution and potential network overload, as they do not consider sender system status-related information when making load balancing decisions.
Innovation Solution
Implementing a cognitive load balancing facility that uses real-time analytics and sender system status-related information to dynamically redirect workloads to the most appropriate target resources, optimizing workload flow and improving overall performance by considering network load, performance, and sender system health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional load balancing algorithms are used, then workload distribution is simple and device complexity is low, but workload distribution efficiency deteriorates and network overload occurs due to lack of end-to-end knowledge
Solution Approach 1:
The patent introduces a cognitive load balancing facility as an intermediary component between the sender system and target resources. This facility collects sender status-related information, analyzes it using analytics engines, and makes informed load balancing decisions. The intermediary nature allows the system to gain end-to-end knowledge without requiring fundamental changes to existing load balancing algorithms, thus improving workload distribution efficiency while maintaining manageable complexity through a dedicated intermediary layer.
Solution Approach 2:
The patent implements feedback mechanisms where the cognitive load balancing facility continuously collects sender status-related information, analyzes current workload conditions, and dynamically adjusts workload distribution decisions. The analytics engines process real-time data about sender system status, network conditions, and target resource availability, creating a closed-loop feedback system that optimizes workload distribution efficiency based on current system state while managing complexity through automated feedback processing.
2Reliability
If sender system status-related information is collected and analyzed, then workload distribution accuracy improves and data loss is reduced, but information processing requirements and system complexity increase
Solution Approach 1:
The patent segments the information processing function into distinct components: a cognitive load balancing facility that collects sender status-related information, analytics engines that process the information, and load balancing algorithms that make distribution decisions. This segmentation allows the system to handle complex information processing tasks through specialized sub-components, improving workload distribution accuracy while managing overall system complexity through functional decomposition.
Solution Approach 2:
The cognitive load balancing facility and analytics engines automatically collect, process, and analyze sender status-related information without requiring external intervention. The system self-services by autonomously making informed load balancing decisions based on analyzed data, reducing the need for manual configuration and management while improving workload distribution accuracy through continuous automated monitoring and adaptation.
3Loss of time
If dynamic workload redirection is implemented, then response time improves and performance is optimized, but processing overhead and system complexity increase
Solution Approach 1:
The cognitive load balancing facility performs preliminary actions by continuously collecting and analyzing sender status-related information before workload distribution decisions are made. The analytics engines pre-process data about system conditions, target resource availability, and network state, enabling the load balancing algorithm to make rapid, informed redirection decisions that improve response time while managing processing overhead through advance preparation.
Solution Approach 2:
The patent implements dynamic workload redirection where the cognitive load balancing facility continuously adapts distribution decisions based on real-time analysis of sender status-related information and system conditions. The load balancing control dynamically adjusts target resource selection, workload routing, and distribution strategies in response to changing system state, improving response time through adaptive behavior while managing complexity through automated dynamic adjustment mechanisms.
Data Source
AI summary
Workload processing is facilitated in a data processing environment including a sender system, a load balancer and a plurality of target resources. The sender system sends workloads to the load balancer, and the load balancer distributes the workloads to the plurality of target resources for processing. Facilitating workload processing includes receiving, by the load balancer, sender status-related information which is indicative of a workload capacity issue from the sender system's view related, at least in part, to the sending of the workloads to the load balancer. The load balancer distributes one or more workloads of the sender system to one or more target resources of the plurality of target resources in a manner based, at least in part, upon the received sender status-related information.