Distributed Counter Auto-Scaling for Network Clusters
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Solution Overview
Problem
Existing auto-scaling architectures that rely on centralized monitoring systems suffer from delays, reduced scalability, and increased complexity, which can lead to inadequate performance in meeting demand and maintaining system efficiency.
Innovation Solution
Implementing a distributed monitoring system that allows instances to autonomously update metrics and make scaling decisions based on predefined policies, eliminating the need for third-party monitoring hardware and software, and enabling auto-scaling operations to be performed within a cluster of instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized monitoring system is used to monitor instance metrics, then the system can perform monitoring functions, but delays occur in auto-scaling response and the system complexity increases
Solution Approach 1:
The patent segments the centralized monitoring function by distributing monitoring capabilities to individual instances through peer counters. Each instance maintains its own counter locally, eliminating the need for centralized metric collection and reducing response delays while maintaining monitoring accuracy.
Solution Approach 2:
Instances perform self-monitoring by maintaining peer counters locally without requiring external centralized monitoring systems. This self-service approach eliminates network communication delays and reduces system complexity while ensuring reliable metric tracking.
2Reliability
If a centralized monitoring system is used, then monitoring can be performed, but the scalability of the distributed architecture is reduced
Solution Approach 1:
The monitoring capability is segmented and distributed to individual instances rather than centralized. Each instance independently maintains peer counters, enabling the system to scale horizontally without being constrained by a single centralized monitoring device.
Solution Approach 2:
The monitoring function is extracted from the centralized system and embedded directly into individual instances through peer counters. This extraction eliminates the scalability bottleneck of centralized monitoring while preserving reliable metric tracking across distributed instances.
3Reliability
If a centralized monitoring system is implemented, then instance metrics can be monitored, but the system complexity and maintenance requirements increase
Solution Approach 1:
The complex centralized monitoring infrastructure is extracted and replaced with simple peer counters embedded in each instance. This eliminates the need for separate monitoring hardware and software while maintaining reliable metric monitoring, significantly reducing system complexity.
Solution Approach 2:
Instances perform their own monitoring through peer counters without requiring external centralized monitoring systems. This self-service mechanism eliminates the complexity of centralized monitoring architecture while ensuring reliable metric tracking.
Data Source
AI summary
Disclosed herein are systems, devices, and methods for providing auto-scaling in a cluster of device instances. In one embodiment, a method is disclosed comprising updating, using a distributed counter, a metric associated with one or more instances executing a network application; identifying that the metric has exceeded a threshold defined in a scaling policy based on comparing the distributed counter to the scaling policy; identifying a command to execute in response to the metric exceeding the threshold; and executing the command to modify the one or more instances.