Load Balancer Forecasting Cached Session Reuse
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Solution Overview
Problem
Current load balancing systems fail to effectively utilize reusable user sessions stored in cache, leading to inefficiencies as they do not adequately minimize the number of unused and inactive sessions in distributed computing environments.
Innovation Solution
Implement load balancing techniques that incorporate forecasting and overbooking by identifying cached sessions and distributing loads based on forecasted loads, which include calculating the average load on servers, cached session values, and reconnection probabilities to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user sessions are cached for reuse, then resource utilization improves, but the number of unused and inactive sessions increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the state of user sessions between active and cached based on inactivity thresholds. When sessions remain inactive beyond a specified threshold, their state changes from active to cached, allowing the system to optimize resource utilization while managing the quantity of active sessions effectively.
Solution Approach 2:
The load balancing system implements dynamics by continuously monitoring session activity and adapting session states in real-time. Sessions transition dynamically between active and cached states based on usage patterns, enabling the system to respond to changing load conditions and user behavior without manual intervention.
2Ease of operation
If load balancing is based on current load analysis, then session distribution is simple, but cached sessions are not effectively utilized
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring session activity, load conditions, and cache status. This feedback loop enables the load balancer to make informed decisions about session distribution, determining whether to route new sessions to active servers or reuse cached sessions based on real-time system state and performance metrics.
Solution Approach 2:
The system applies preliminary action by pre-caching inactive user sessions before they are completely terminated. This allows the load balancer to quickly restore and reuse these sessions when the same users reconnect, reducing authentication overhead and improving response time without requiring complex real-time session creation.
3Productivity
If forecasting and overbooking techniques are implemented, then resource allocation is optimized, but system complexity increases
Solution Approach 1:
The load balancing system applies self-service by automatically performing forecasting and overbooking calculations without requiring external intervention. The system monitors its own performance metrics, predicts future load conditions, and makes autonomous decisions about session caching and distribution, reducing the need for manual configuration and complex external control mechanisms.
Data Source
AI summary
Methods and systems for establishing user sessions between a client and a server or server farm can be carried out by a load balancing agent executing on a computer in communication with the client, server or server farm. The load balancing agent can intercept a connection request generated by an application executing on a client and responsively select a server on which to establish a user session. Selecting the server can be based on overbooking a particular server and choosing a server with a predetermined amount of forecasted load. Forecasted load can be determined by summing the current load on a server with a current load calculated by multiplying: the average load on the user sessions hosted by the server; by the number of cached user sessions stored on the server; and by the probability that a cached user session reconnects.