Configurable Connection Reset for Dynamic Load Balancing
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Solution Overview
Problem
Existing load balancing systems lack flexibility and efficiency in routing service requests, as they typically throttle requests based on predetermined schedules without considering dynamic server performance or client preferences, leading to inefficient resource utilization and potential service denials.
Innovation Solution
A system that analyzes real-time dynamic server performance data using user-configured threshold failure criteria to identify active and inactive servers, assigns configurable wait times to inactive servers, and routes requests based on per-client distribution configurations, ensuring that service requests are directed to available servers with prioritization and rotation methodologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a load balancer throttles requests based on a predetermined schedule, then the system structure is simple and easy to implement, but the resource utilization becomes sub-optimal and lacks flexibility
Solution Approach 1:
The load balancer dynamically adjusts request routing based on real-time server status and client preferences. Instead of using a fixed predetermined schedule, the system continuously monitors server performance data and adapts the distribution rotation accordingly, allowing the routing strategy to change dynamically based on current system state and client-specific configurations.
Solution Approach 2:
The system changes the parameter of request distribution from a static predetermined schedule to a dynamic configuration based on multiple variables including server status, client preferences, and performance metrics. This allows the routing parameters to be adjusted in real-time to optimize resource utilization while maintaining implementation feasibility through modular architecture.
2Ease of operation
If a load balancer uses a predetermined schedule for request distribution, then the system is simple to operate, but the resource utilization becomes sub-optimal
Solution Approach 1:
The load balancer implements feedback mechanisms by continuously monitoring server performance data and using this information to adjust request routing decisions. The system collects feedback on server status, response times, and load conditions, then uses this feedback to dynamically optimize the distribution rotation, improving resource utilization while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The system performs preliminary actions by pre-configuring client-specific distribution configurations and server status monitoring frameworks. These preliminary setups enable the load balancer to make optimized routing decisions without requiring complex real-time calculations, thus maintaining operational simplicity while improving resource utilization through pre-planned optimization strategies.
3Speed
If requests are routed without considering dynamic server performance, then the routing process is fast and efficient, but the service reliability decreases due to potential service denials
Solution Approach 1:
The load balancer performs preliminary status checks on servers before routing requests. By proactively identifying active and inactive servers using user-configured threshold failure criteria, the system ensures that requests are only routed to capable servers, preventing service denials while maintaining efficient routing through pre-assessed server availability.
Solution Approach 2:
The system implements self-service mechanisms where servers automatically report their status and the load balancer autonomously makes routing decisions based on this data. This self-service approach eliminates the need for manual intervention while ensuring reliable routing, as the system automatically adapts to changing server conditions and redirects traffic away from inactive servers without impacting routing speed.
4Reliability
If the load balancer considers per-client distribution configurations and real-time server status, then the service flexibility and reliability improve, but the system complexity increases
Solution Approach 1:
The load balancer segments the system into modular components: a status component for monitoring servers, a selection component for choosing routing strategies, and a routing component for executing requests. Each component handles specific tasks independently, allowing the system to manage complex per-client configurations and real-time status monitoring through divided responsibilities, thus improving reliability without proportionally increasing overall system complexity.
Solution Approach 2:
The load balancer implements a universal architecture that handles multiple client preferences and server types through a single configurable framework. The system uses universal data structures and routing logic that can accommodate different client-specific distribution configurations and server statuses, reducing the need for separate specialized components and thereby limiting the increase in system complexity while maintaining high service reliability.
Data Source
AI summary
Examples provide a configurable connection reset system for customized distribution of incoming traffic to a plurality of servers. The connection reset component routes incoming service requests to active servers based on server performance, configurable server failure criteria, user-selected inactive threshold wait time-period for each server and per-client distribution configurations, including server priorities and user-selected distribution methodology or combination of distribution methodologies. A configurable wait time-period is assigned to each inactive server removed from a distribution rotation for the incoming traffic. A next received request to the next available server for the identified client. An inactive server is updated to active status and returned to the distribution rotation after expiration of the configurable wait time-period.