Predictive Auto-Scaling for Messaging Services
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Solution Overview
Problem
Asynchronous messaging protocol (AMP) messaging infrastructure faces challenges in scaling beyond a certain volume without provisioning new broker clusters, leading to message backlogs and increased latency due to mismatches between message production and consumption rates, which reactive auto-scaling often fails to address quickly enough.
Innovation Solution
Implementing predictive auto-scaling to forecast message volumes and scale resources in advance, combined with reactive auto-scaling to monitor and adjust resource utilization in real-time, ensuring efficient allocation and utilization of resources to prevent message backlogs and maintain low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive auto-scaling is used to scale AMP messaging infrastructure, then resource allocation can be adjusted based on current load, but the scaling response is too slow to prevent message backlogs during unexpected message spikes
Solution Approach 1:
The patent applies preliminary action by predicting future message volumes using historical data and trends before the actual load occurs. The auto-scaling system proactively provisions resources in advance based on predicted demand, rather than reacting after the load has already caused backlogs. This allows the system to scale resources before unexpected message spikes impact message processing reliability.
2Productivity
If new broker clusters are provisioned to scale beyond certain volume, then message processing capacity increases, but the complexity of provisioning and managing multiple clusters increases
Solution Approach 1:
The patent applies dynamics by implementing an auto-scaling system that dynamically adjusts the number and capacity of broker instances based on real-time and predicted message volumes. Instead of manually provisioning fixed broker clusters, the system continuously adapts resource allocation to match actual and forecasted demand, enabling seamless scaling without manual intervention or complex cluster management.
Solution Approach 2:
The patent applies universality by creating a unified auto-scaling control system that manages diverse messaging workloads across different broker clusters through a single intelligent platform. This universal controller handles prediction, scaling decisions, and resource allocation across multiple clusters, simplifying management complexity while maintaining the ability to scale processing capacity across the entire infrastructure.
3Productivity
If message production rate increases or consumption rate decreases, then message volume changes, but mismatches between production and consumption rates cause message backlogs
Solution Approach 1:
The patent applies feedback by implementing a closed-loop auto-scaling system that continuously monitors message production and consumption rates, compares actual performance against predicted performance, and adjusts broker resources accordingly. The system uses historical data and real-time metrics to detect mismatches between production and consumption rates, then automatically scales resources to restore balance and prevent backlogs, maintaining message processing consistency despite varying throughput demands.
Data Source
AI summary
Technologies are disclosed for providing predictive auto-scaling for resources of a network-accessible message processing service. Control metrics are analyzed in order to predict a future volume of messages that will be received and processed by the message processing service. Based upon the prediction, resources, in the form of computing resources, are allocated to the message processing service. Reactive auto-scaling of the resources can also be used in conjunction with predictive auto-scaling. For reactive auto-scaling, an enqueue rate at a queuing service for messages is monitored and resources are reactively auto-scaled accordingly.