Service Maintenance Window Identification via Resource Clustering
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Solution Overview
Problem
Conventional systems lack the ability to programmatically determine optimal change windows for maintenance and updates across multiple services sharing resources, leading to unintended impacts on customers and increased complexity due to limited visibility and shared resource utilization in microservice architectures.
Innovation Solution
A computer-implemented method that queries time series performance data to identify low resource consumption time slots, annotates them with service tags, creates clusters using dynamic clustering, and generates a common change window by eliminating duplicate hosts, thereby recommending the least impactful time for maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional random selection of change window is used, then simplicity of scheduling is maintained, but service availability and customer impact are worsened
Solution Approach 1:
The system performs preliminary analysis of historical service usage patterns, resource consumption data, and customer behavior before selecting a change window. This advance preparation enables identification of optimal time slots that minimize customer impact while maintaining scheduling simplicity.
Solution Approach 2:
The system continuously collects feedback from service monitoring data, customer usage patterns, and resource utilization metrics. This feedback loop enables dynamic optimization of change window selection, improving service availability while maintaining operational simplicity through automated decision-making.
2Loss of time
If change window is selected without historical behavior analysis, then decision-making speed is maintained, but maintenance precision and service quality are worsened
Solution Approach 1:
Historical service behavior data, resource consumption patterns, and customer usage metrics are pre-analyzed and stored in accessible formats. This preliminary processing enables rapid retrieval and analysis during change window selection, maintaining decision-making speed while improving maintenance precision through data-driven insights.
Solution Approach 2:
Manual analysis and decision-making processes are replaced with automated computational systems that analyze historical data patterns. This substitution enables both rapid decision-making and high precision by leveraging computational power to process complex historical behavior data instantaneously.
3Device complexity
If administrator has limited visibility into service components, then system complexity is reduced, but measurement precision and impact assessment are worsened
Solution Approach 1:
Automated monitoring agents and data collection intermediaries are deployed across service components to gather detailed usage and resource consumption data. These intermediaries provide comprehensive measurement capabilities without requiring administrators to directly access or understand complex system internals, maintaining low perceived complexity while achieving high measurement precision.
Solution Approach 2:
Manual inspection and assessment methods are replaced with automated computational analysis systems. These systems precisely measure resource consumption patterns, service usage metrics, and potential impact of changes without requiring administrator expertise in system internals, thereby maintaining simplicity while improving measurement accuracy.
4Productivity
If shared resources are used across multiple services, then resource utilization efficiency is improved, but identification of optimal change window becomes more difficult
Solution Approach 1:
The system merges data from multiple services that share common resources into unified analysis models. By combining usage patterns, resource consumption data, and service dependencies across all affected services, the system identifies change windows that optimize resource utilization while minimizing cumulative impact on all services simultaneously.
Solution Approach 2:
The change window identification system is designed to handle multiple services and shared resources universally. It performs multi-functionality by analyzing diverse service types, resource configurations, and usage patterns through a single integrated approach, thereby managing complexity while maintaining efficient resource utilization across the entire service portfolio.
Data Source
AI summary
Systems and techniques for identifying a common change window for one or more services implemented on one or more hosts include querying time series performance data for each host of a service to identify time slots of low resource consumption on the host, annotating the time slots with service tags, where the service tags identify host information and service information, creating groups of time slots using the service tags, using dynamic clustering to create clusters of hosts using the groups of time slots, and generating at least one common change window by eliminating duplicate hosts from the clusters of the hosts.