Predictive Reserved Instance Dashboard for Cloud Cost Optimization
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Solution Overview
Problem
Cloud computing organizations face challenges in predicting and optimizing the number of reserved instances on hyper-scaler platforms, leading to potential cost overruns due to uncertainty in usage patterns and fluctuations in demand.
Innovation Solution
A dashboard system utilizing machine learning algorithms, including deterministic and probabilistic models, to predict the optimal number of reserved instances based on past usage data and user-inputted future business needs, allowing for real-time visualization and adjustment of reservations to minimize costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the number of reserved instances is increased, then cost savings from reserved instances are improved, but resource waste from over-reservation increases
Solution Approach 1:
The system performs preliminary actions by analyzing historical usage data and predicting future usage patterns before making reservation decisions. The predictive model forecasts future instance requirements, allowing organizations to reserve the optimal number of instances in advance, thereby capturing cost savings while avoiding over-reservation waste.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual usage against predictions and adjusting future reservation recommendations. This closed-loop approach allows the system to learn from past predictions and improve accuracy, ensuring that reserved instances closely match actual usage patterns and minimizing both waste and missed savings opportunities.
2Loss of substance
If the number of reserved instances is decreased, then resource waste is reduced, but cost savings from reserved instances are lost
Solution Approach 1:
The system performs preliminary analysis of historical usage patterns and future business needs before determining reservation quantities. By predicting future usage requirements in advance, the system recommends reservation levels that maximize cost savings while minimizing waste, preventing both over-reservation and under-reservation scenarios.
Solution Approach 2:
The system dynamically adjusts reservation parameters based on changing usage patterns, business requirements, and predictive model outputs. By continuously optimizing the reservation quantity parameter, the system adapts to varying demands and ensures cost-effective resource allocation without excessive waste or missed savings opportunities.
3Measurement precision
If predictive modeling is implemented, then reservation accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary predictive modeling layer that sits between historical usage data and reservation decisions. This intermediary component processes complex patterns and relationships in usage data, translating them into actionable reservation recommendations, thereby improving accuracy while managing complexity through modular architecture.
Data Source
AI summary
An example system and method to provide a dashboard for users to analyze and review their hyper-scaler usage and spending and offer optimizations to predict optimal use of reserved and unreserved instances on various hyper-scaler platforms. While hyper-scaler platforms offer flexibility for users to scale their use on a platform, there is a potential risk of rapid cost overruns in large enterprise organizations that may be difficult to control and predict. In some examples, the system can determine an optimal number of reserved instances using past usage data and/or prediction data from a user may be used by the system to make forward predictions about reserving an optimal number of instances and minimizing hyper-scaler resource use.