Burstable Compute Workload Classification via Historical Utilization Analysis
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Solution Overview
Problem
Service providers face challenges in determining whether workloads are suitable for burstable VM instance types, leading to inefficient resource allocation and potential throttling or underutilization of computing resources.
Innovation Solution
A cloud-based workload optimization service that identifies suitable workloads for burstable instance types by analyzing historical utilization data, extracting feature data, and training classification models to determine the optimal instance type for each workload, thereby recommending or migrating workloads to more appropriate instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If burstable VM instance types are used to reduce resource allocation costs, then resource utilization is improved, but workload performance may be throttled during high-usage periods
Solution Approach 1:
The system performs preliminary classification of workloads using trained machine learning models to identify which workloads are suitable for burstable instance types. This advance classification allows workloads to be placed on appropriate instance types before execution, preventing performance throttling while optimizing resource utilization.
Solution Approach 2:
The system uses historical utilization data as feedback to train classification models that continuously improve their ability to identify suitable workloads for burstable instances. This feedback loop enables the system to learn from past performance patterns and make more accurate classifications, balancing resource utilization with workload performance requirements.
2Measurement precision
If classification models are trained using historical utilization data, then workload suitability accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated machine learning model training and evaluation. The classification models automatically learn from historical utilization data and improve their accuracy without requiring manual intervention. This automation reduces the operational complexity despite the increased system capabilities.
Solution Approach 2:
The system uses historical utilization data as a copy of past workload behavior patterns to train classification models. By learning from these historical copies, the system can predict future workload characteristics and suitability for burstable instances without needing to physically test each workload, reducing complexity while maintaining accuracy.
3Reliability
If fixed VM instance types are used to guarantee workload performance, then reliability is improved, but resource utilization decreases due to overprovisioning
Solution Approach 1:
The system introduces dynamics by classifying workloads into different categories based on their suitability for burstable versus fixed instance types. This dynamic classification allows the system to adaptively allocate workloads to the most appropriate instance type, achieving both performance reliability and resource utilization optimization without static overprovisioning.
Solution Approach 2:
The system changes the parameter of instance type selection from a static fixed assignment to a dynamic classification-based assignment. By using machine learning models to evaluate workload characteristics and change the instance type parameter accordingly, the system achieves both performance guarantees and improved resource utilization.
Data Source
AI summary
Techniques for a cloud-based workload optimization service to identify customer workloads that are optimized to run on burstable instance types. The techniques include identifying workloads that are successfully running on burstable instance types, and using historical-utilization data for those workloads to train classification models. The optimization service can extract feature data from the historical-utilization data, where the feature data represents utilization characteristics that are indicative of burstable workloads. The feature data is then used to train classification models to receive utilization data for candidate workloads, and determine whether the candidate workloads would be optimized for burstable instance types. The optimization service can then migrate suitable workloads to burstable instance types, and/or provide users with recommendations that their workloads are optimized or suitable for burstable instance types.