Data Lake Connection Autoscaling for Load-Responsive Resource Use
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Solution Overview
Problem
Cloud resource usage for data lakes is often sub-optimal due to unmanaged load, leading to increased costs.
Innovation Solution
A system and method for autoscaling data lake connections based on collected metrics, automatically adjusting the number of processes connecting to the data lake to optimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of processes connecting to the data lake is increased to handle higher data loads, then productivity is improved, but resource usage efficiency deteriorates due to sub-optimal utilization of cloud resources
Solution Approach 1:
The system dynamically adjusts the number of processes connecting to the data lake based on real-time load metrics. The autoscaling mechanism monitors data lake load and automatically scales the number of processes up or down to match actual demand, ensuring optimal resource utilization while maintaining required productivity levels.
Solution Approach 2:
The system implements a feedback loop where load metrics from the data lake are continuously collected and used to automatically adjust the number of processes. This closed-loop control ensures that resource allocation responds to actual workload conditions, preventing both over-provisioning and under-provisioning of cloud resources.
2Loss of energy
If manual management of process scaling is implemented, then resource usage can be controlled, but adaptability deteriorates due to inability to respond automatically to varying data loads
Solution Approach 1:
The system enables self-service autoscaling where the data lake infrastructure automatically manages its own process scaling based on monitored load metrics. The autoscaling mechanism autonomously adjusts process numbers without requiring manual intervention, combining efficient resource control with automatic adaptability to changing workload conditions.
3Productivity
If cloud resources are over-provisioned to ensure adequate processing capacity, then productivity is maintained during peak loads, but resource usage efficiency deteriorates during low-utilization periods
Solution Approach 1:
The system transitions from static over-provisioning to dynamic resource allocation. The autoscaling mechanism continuously monitors load metrics and adjusts the number of processes in real-time, ensuring adequate processing capacity during peak loads while reducing resource consumption during low-utilization periods.
Solution Approach 2:
The system changes the parameter of process quantity dynamically based on load conditions. By monitoring data lake load metrics and adjusting the number of processes accordingly, the system adapts resource consumption to actual workload requirements, avoiding both over-provisioning and under-provisioning.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for autoscaling data lake connections. One or more metrics defining a load on one or more data sources included in a data lake are collected. A plurality of processes configured to connect to the data lake to retrieve data therefrom are automatically scaled, as a function of the one or more metrics.