Automated Capacity Controller for Cloud Resource Prediction
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Solution Overview
Problem
Cloud computing systems face service interruptions due to unpredictable user demands, leading to resource exhaustion, as existing management systems are often reactive and lack proactive capacity management.
Innovation Solution
An automated capacity controller that leverages historical usage data to predict future demand, using techniques like curve fitting and ARIMA models, to prioritize resource allocation and trigger remedial actions such as server rehabilitation and offer restrictions, thereby preventing capacity shortages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive capacity management is used to respond to failures and demand, then system simplicity is maintained, but service reliability deteriorates due to unpredictable resource exhaustion
Solution Approach 1:
The system performs preliminary actions by predicting future capacity demands using historical data and machine learning models before resource exhaustion occurs. The capacity manager proactively identifies potential shortages and triggers remedial actions in advance, such as migrating workloads or provisioning additional resources, thereby preventing service outages without requiring complex real-time reactive management.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring historical usage data, service levels, and capacity metrics. This feedback is fed into predictive models that generate forecasts about future demand patterns. The capacity manager uses these feedback loops to adjust capacity allocation proactively, improving service reliability through data-driven decision-making rather than simple reactive responses.
2Reliability
If proactive capacity prediction is implemented using historical data and machine learning, then capacity availability is improved, but computational complexity increases
Solution Approach 1:
The system employs self-service principles by automatically using historical usage data to train and refine predictive models without requiring manual intervention. The capacity manager autonomously analyzes patterns, generates forecasts, and executes remedial actions based on predicted capacity trends. This automation improves capacity availability while managing computational complexity through efficient use of existing data and standardized machine learning algorithms.
3Quantity of substance
If multiple remedial actions are taken to address capacity shortages, then capacity shortage is reduced, but operational complexity increases
Solution Approach 1:
The system takes preliminary actions by identifying capacity shortages before they impact service levels. The predictive models forecast when capacity will be exhausted, allowing the capacity manager to proactively execute remedial actions such as workload migration or resource provisioning in advance. This approach reduces the need for complex emergency responses and simplifies operational management by planning ahead rather than reacting to crises.
Data Source
AI summary
Techniques for automated capacity managed in distributed computing systems are disclosed herein. In one embodiment, a method includes receiving predicting one or more future usage levels of a computing resource in the distributed computing system based on received data representing historical usage levels of the computing resource and determining whether a currently available capacity of the computing resource in the distributed computing system is depleted beyond a threshold time period based on the one or more future usage levels. In response to determining that the currently available capacity of the computing resource in the distributed computing system is depleted before the threshold time period, the method includes immediately rebooting, reimaging, or performing other recovery actions on one or more out-for-repair hosts that provide the computing resource.