PID-Based Load Balancing for Containerized Workloads
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Solution Overview
Problem
Current load balancing techniques in cloud computing environments are inefficient as they either rely on static algorithms that do not account for dynamic system states or dynamic algorithms that may lead to inefficiencies due to information exchange between computing units.
Innovation Solution
A method and apparatus for dynamic load balancing of containerized workloads using real-time metrics such as processing cores, memory, network latency, and response time, with a proportional-integral-derivative (PID) controller to adjust weights for selecting the most suitable endpoint for user requests, ensuring efficient distribution of workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static load balancing algorithms are used, then device complexity is reduced, but productivity decreases due to inability to adapt to dynamic system states
Solution Approach 1:
The patent implements a feedback mechanism where the load balancer receives real-time performance metrics from endpoints (such as response time, throughput, error rates) and dynamically adjusts routing decisions based on this feedback. This allows the system to adapt to changing conditions without requiring complex static algorithms, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The load balancing system transitions from static algorithmic approaches to dynamic adaptation by continuously monitoring endpoint performance metrics and adjusting routing weights in real-time. This dynamic approach enables the system to respond to changing system states while maintaining operational simplicity through automated metric-based adjustments.
2Productivity
If dynamic load balancing algorithms with information exchange are used, then productivity is improved, but device complexity increases due to communication overhead between computing units
Solution Approach 1:
The patent introduces an intermediary component (the load balancer) that centralizes the information exchange process. Instead of direct communication between all computing units, the load balancer acts as a mediator that collects performance metrics from endpoints and distributes routing decisions, reducing the communication overhead and complexity associated with peer-to-peer information exchange.
Solution Approach 2:
The system implements a unidirectional feedback loop where endpoints report performance metrics to the load balancer, which then adjusts routing decisions. This feedback mechanism enables dynamic workload distribution without requiring complex bidirectional communication between all system components, thereby improving productivity while controlling device complexity.
3Adaptability or versatility
If real-time metrics collection and PID controller adjustments are implemented, then adaptability is improved, but device complexity increases due to continuous monitoring and control mechanisms
Solution Approach 1:
The patent employs a PID (Proportional-Integral-Derivative) controller that continuously monitors performance metrics and automatically adjusts routing weights based on the feedback received. This feedback-driven approach enables the system to adapt to changing conditions while automating the control process, reducing the need for manual intervention and simplifying the overall system architecture despite the sophisticated control mechanism.
Solution Approach 2:
The load balancing system implements self-service through automated metric collection and PID-based weight adjustment. The system monitors its own performance and autonomously optimizes routing decisions without external intervention, improving adaptability while managing complexity through self-regulation mechanisms.
Data Source
AI summary
System and methods are described for performing load balancing by continually collecting real-time metrics values from a plurality of endpoints in a cloud computing system, the real-time metrics values representing current performance measurements of processing by the endpoints, and using the collected real-time metrics values by a controller to continually determine a current weight value for each endpoint, the current weight value representing a probability that the endpoint will be selected to process a user request. The method includes receiving the user request for the cloud computing system to perform requested processing; selecting an endpoint of the cloud computing system to process the user request based at least in part on the current weight values of the endpoints; and sending the user request to the selected endpoint.