Cloud Service Purchase Strategy Optimization
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Solution Overview
Problem
Customers face difficulties in making informed purchase decisions for cloud services due to varying prices, performance, and attributes over time, especially when considering spot instances that may be interrupted, making it challenging to optimize service selection.
Innovation Solution
A system and method that generates real-time purchase strategies based on price and performance data from multiple cloud service providers, using a database populated with historical and current information, to help customers make informed decisions by analyzing and predicting optimal purchase parameters such as spot instance termination risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If spot instances are used to reduce costs, then price is improved, but reliability deteriorates due to potential early termination
Solution Approach 1:
The system performs preliminary analysis of spot instance pricing patterns and termination risks before making purchase decisions. By predicting future spot prices and termination probabilities in advance, the system can proactively adjust bidding strategies and prepare fallback options, thereby reducing costs while mitigating reliability risks associated with spot instance termination.
Solution Approach 2:
The system continuously monitors spot instance performance, pricing changes, and termination events, using this feedback to dynamically adjust bidding strategies. This real-time feedback loop enables the system to learn from past termination patterns and optimize future spot instance selections, balancing cost savings with service continuity requirements.
2Measurement precision
If real-time analysis of multiple cloud services is performed to optimize selection, then purchase decision quality is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of cloud service selection into distinct analytical modules: price analysis, performance evaluation, termination risk assessment, and recommendation generation. Each module processes specific aspects independently and outputs structured results that are integrated to form comprehensive purchase recommendations, making the overall system more manageable and maintainable.
Solution Approach 2:
The system introduces an intermediary analysis layer between raw cloud service data and purchase decisions. This intermediary layer standardizes and normalizes data from multiple cloud providers, applies consistent evaluation criteria, and transforms diverse service attributes into comparable metrics, thereby simplifying the decision-making process while maintaining high decision quality.
3Measurement precision
If historical and real-time data are collected and analyzed to predict spot instance behavior, then prediction accuracy is improved, but information processing requirements increase
Solution Approach 1:
The system extracts and focuses on the most critical predictive features from large volumes of historical and real-time data, such as pricing patterns, termination frequencies, and load trends. By identifying and analyzing only the most relevant features rather than processing all available data, the system achieves high prediction accuracy while reducing computational overhead and data processing requirements.
Solution Approach 2:
The system performs preliminary data preprocessing and feature extraction in advance, organizing historical data into structured formats and pre-computing statistical patterns. This preliminary preparation reduces the processing burden during real-time analysis, enabling accurate predictions with lower computational resources during critical decision-making moments.
Data Source
AI summary
A system and method is provided for generating and using purchase strategies based on the price, performance, and/or other information related to cloud services to optimize the selection of such services. The purchase strategies may comprehensively describe various cloud services in real-time so that customers may purchase cloud services using up-to-date, real-time information. The purchase strategies may, for example, describe pricing, performance, availability, and/or other attributes of various cloud services. A purchase agent may use the purchase strategies, one or more purchase rules, and/or other information to generate a purchase specification that specifies one or more cloud service instances that should be purchased. The purchase agent may leverage unique properties of spot instances to make favorable purchase decisions. For example, the system may determine bid prices that should be made to obtain certain spot instances.