Message Broker Configuration via Segmented Resource Provisioning
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Solution Overview
Problem
Cloud application platforms face performance issues due to resource-intensive message brokers, which complicate debugging and hinder self-service goals by requiring complex configuration and resource allocation visibility from developers.
Innovation Solution
A guided and semi-automatic configuration of hardware-isolated message brokers allows developers to provision and configure message broker clusters with minimal manual effort, ensuring performance across multiple software platforms through a system controller that uses on-demand resource provisioning and predefined configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If message brokers are integrated into cloud application platforms, then message routing and queue management capabilities are provided, but network and computing resources are consumed significantly
Solution Approach 1:
The system segments message broker functionality into isolated containers that run separately from application containers. Each message broker container is provisioned with dedicated computing and network resources, preventing resource contention with applications while maintaining full message broker capabilities.
Solution Approach 2:
The system introduces a platform operator intermediary who manages message broker provisioning and configuration. This intermediary layer abstracts the complexity of message broker resource allocation from developers, allowing automated resource provisioning while maintaining control over resource consumption.
2Adaptability or versatility
If message brokers are configured with comprehensive options, then functionality and adaptability are improved, but configuration complexity increases
Solution Approach 1:
The system implements self-service provisioning where the platform automatically configures message brokers based on simple developer inputs. Developers specify basic requirements (throughput, latency, message size), and the system automatically selects appropriate configuration parameters, eliminating the need for developers to manually configure complex message broker settings.
Solution Approach 2:
The system changes the parameter space from detailed configuration options to high-level performance requirements. Instead of exposing numerous configuration parameters, the system accepts simple performance targets (throughput, latency) and automatically translates these into appropriate message broker configurations.
3Productivity
If message brokers consume more resources, then performance capabilities are improved, but debugging visibility and self-service goals are hindered
Solution Approach 1:
The system segments resource allocation by providing developers with visibility into the specific resources allocated to their message broker containers. This segmentation allows developers to understand and manage resource consumption without being overwhelmed by the complexity of the underlying infrastructure.
Solution Approach 2:
The system implements feedback mechanisms that provide developers with visibility into message broker resource usage and performance metrics. This feedback loop enables developers to make informed decisions about message broker configuration and troubleshoot issues without requiring deep infrastructure knowledge.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing guided provisioning and configuration of a message broker cluster. One of the methods includes maintaining a message broker platform system configured to host one or more message broker clusters in a cloud computing environment of a distributed computing system. A first software platform system configured to host user-provided computing tasks in the distributed computing system receives a computing task, provisions computing resources in an underlying cloud computing infrastructure, and launches one or more instances of the computing task using the provisioned computing resources in the underlying cloud computing infrastructure. The message broker platform system binds the computing task in the first software platform system to the message broker cluster in the message broker platform system.