Idle Period Detection for Managed Database Cluster Management

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Solution Overview

Problem

Cloud service providers managing databases face challenges in configuring data processing resources effectively due to unknown or changing query workloads, leading to inefficiencies and increased costs.

Innovation Solution

Implementing techniques to detect idle periods in processing clusters for managed databases, allowing providers to proactively perform management actions such as pausing or resizing clusters, without disrupting service.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data processing resources are configured to handle unknown or changing query workloads, then system availability is maintained, but resource utilization efficiency deteriorates

Engineering Contradiction:

Improvesystem availabilityVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system dynamically adjusts processing cluster configuration based on real-time workload monitoring. The leader node continuously detects workload changes and automatically scales or reconfigures compute nodes, transitioning the system from static to dynamic resource allocation to maintain both availability and efficiency

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary actions by pre-configuring processing clusters with sufficient resources to handle potential workload peaks, then later releases these resources during idle periods. This allows the system to maintain availability during high-demand periods while improving efficiency during low-demand periods

Inventive Principle:

Principle #10Preliminary action

2Reliability

If data processing resources are over-provisioned to handle peak workloads, then system reliability is improved, but operational costs increase

Engineering Contradiction:

Improvesystem reliabilityVSAvoidoperational costs

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system implements periodic monitoring and adjustment of resource allocation. The leader node periodically assesses workload conditions and adjusts the number of active compute nodes accordingly, cycling between higher resource states during peaks and lower resource states during valleys, thereby reducing overall operational costs while maintaining reliability

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system changes operational parameters by dynamically adjusting the number of active compute nodes and their configuration based on workload metrics. This parameter adjustment allows the system to match resource consumption with actual demand, reducing energy loss and operational costs during low-utilization periods

Inventive Principle:

Principle #35Parameter changes

3Loss of energy

If data processing resources are under-provisioned to reduce costs, then operational costs are lowered, but service disruption risk increases

Engineering Contradiction:

Improveoperational costsVSAvoidservice disruption risk

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system uses dynamic resource allocation where the leader node continuously monitors workload and automatically provisions additional compute nodes when demand increases. This dynamic response allows the system to operate with fewer resources during low-demand periods (reducing costs) while ensuring adequate resources are available during high-demand periods (maintaining reliability)

Inventive Principle:

Principle #15Dynamics

4Reliability

If processing clusters are continuously active to handle unknown workloads, then service availability is maintained, but resource waste increases

Engineering Contradiction:

Improveservice availabilityVSAvoidresource waste

Core Design Contradiction:

ReliabilityVSLoss of substance

Solution Approach 1:

The system takes preliminary action by maintaining a ready-to-deploy configuration of processing clusters that can be rapidly activated when workload demands increase. These clusters can be pre-configured but not fully activated, allowing the system to avoid continuous operation of resources that would otherwise be wasted during idle periods

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transitions processing clusters between active and inactive states dynamically based on workload conditions. The leader node monitors for idle periods and places clusters in a low-power or suspended state during these periods, then reactivates them when workload increases, thereby eliminating resource waste while maintaining service availability

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12222908B2Detecting idle periods at network endpoints for management actions at processing clusters for managed databases

Publication Date: 2025.02.11 AMAZON TECH INC

AI summary

Idle periods may be for management actions at processing clusters for managed databases. A leader node of a processing cluster for a managed database may monitor a network endpoint at a proxy service associated with a database managed by the database service. An idle period for the database may be detected. A management action for the processing cluster may be determined to be performed during the detected idle period. The leader node may cause the determined management action to be performed.