Intelligent Load Balancer Using Temporal Window Traffic Prediction
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Solution Overview
Problem
Existing traffic routing algorithms in cloud computing environments, such as 'greedy' approaches, fail to achieve platform-wide optimal outcomes due to their focus on single criteria, while more optimal dynamic approaches are computationally intensive and difficult to implement in real-time scenarios.
Innovation Solution
A temporal window is used to predict incoming traffic using probabilistic distribution models like Poisson or Gaussian, combined with estimated execution times, allowing processor-implemented routines to efficiently solve dynamic routing decisions in real-time contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If greedy algorithms are used for traffic routing, then ease of implementation is improved, but routing optimality deteriorates
Solution Approach 1:
The routing decision process is segmented into discrete time steps within a temporal window. Each time step processes a subset of requests independently, breaking down the complex optimization problem into manageable segments that can be solved efficiently at each step while contributing to the overall optimal solution.
Solution Approach 2:
The system dynamically adjusts routing decisions based on the current state of application nodes and the temporal position within the window. The load balancer continuously updates its routing policy as time progresses through the window, adapting to changing traffic patterns and node states to maintain optimality.
2Reliability
If dynamic approaches are used for traffic routing, then routing optimality is improved, but computational complexity worsens
Solution Approach 1:
The system pre-defines a temporal window structure and probabilistic distribution models before processing traffic. By establishing the optimization framework in advance with predetermined time steps and statistical models, the system reduces the computational burden during real-time execution while maintaining optimal routing decisions.
Solution Approach 2:
The system changes parameters such as the temporal window size, time step intervals, and probabilistic distribution parameters to balance computational complexity with routing optimality. By adjusting these parameters, the system can optimize performance for different traffic conditions while keeping computational requirements manageable.
3Reliability
If dynamic approaches are used for traffic routing, then routing optimality is improved, but real-time capability worsens
Solution Approach 1:
The load balancer performs routing optimizations at periodic intervals corresponding to discrete time steps within the temporal window. This periodic approach allows the system to make optimal routing decisions at regular intervals rather than continuously, achieving real-time capability while maintaining routing optimality through structured periodic optimization cycles.
Data Source
AI summary
Techniques for routing requests on a network are described. In accordance with certain aspects, a temporal window is incremented or moved over time to facilitate dynamic routing decisions. The temporal window may be used to project or estimate incoming request traffic based on a suitable probabilistic distribution model, such as a Poisson or Gaussian probability distribution, applied to the window so as to estimate or predict traffic at different times as the window is incremented. Estimated execution times for incoming requests may also be computed so that arrival and completion times of each request or traffic event can be modeled. Processor-implemented routines may be employed to solve the sub-problems defined by the temporal window incoming traffic estimation and the estimated execution times efficiently, allowing the parent or overall routing decision problem to be solved efficiently using dynamic processes, including in real-time contexts.