Cloud Wastage Templates for Telemetry-Based Inefficiency Detection
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Solution Overview
Problem
Enterprise companies face inefficiencies in cloud deployments due to lack of visibility and standardization, compounded by the complexity of cloud providers and services, necessitating tools for identifying and optimizing cloud resources.
Innovation Solution
Implementing systems and methods that use machine learning, disaggregation algorithms, and domain-specific templates to analyze cloud inefficiencies, automate resource management, and apply reinforcement learning and game theory for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If cloud infrastructure is made remote to reduce operational costs, then cost efficiency is improved, but visibility and detectability of inefficiencies deteriorates
Solution Approach 1:
The patent introduces a cloud efficiency analyzer as an intermediary system that sits between the enterprise and remote cloud infrastructure. This analyzer collects telemetry data from cloud computing systems, processes it through disaggregation algorithms and machine learning classifiers, and generates actionable efficiency reports. The intermediary enables visibility into remote cloud inefficiencies without requiring direct access to the cloud infrastructure itself.
2Adaptability or versatility
If multiple cloud providers and services are adopted to increase versatility, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent segments the cloud efficiency analysis function into distinct modular components: telemetry data collectors for different cloud providers, disaggregation algorithms for data processing, machine learning classifiers for state determination, and efficiency calculators for scoring. This segmentation allows the system to handle multiple cloud providers independently while maintaining a unified analysis framework, reducing overall system complexity.
Solution Approach 2:
The cloud efficiency analyzer is designed as a universal platform that can analyze multiple cloud providers and services through a common architecture. The system uses standardized telemetry collection mechanisms, unified disaggregation algorithms, and general machine learning classifiers that work across different cloud environments, enabling one system to serve multiple cloud providers without requiring provider-specific analysis tools for each component.
3Measurement precision
If manual monitoring and problem identification is performed initially, then detection accuracy is improved, but time consumption and labor requirements increase
Solution Approach 1:
The patent implements self-service automation where the cloud efficiency analyzer autonomously collects telemetry data, processes it through disaggregation algorithms, classifies cloud states using machine learning models, identifies inefficiencies, and generates recommendations without human intervention. The system continuously monitors cloud infrastructure and automatically detects problems, eliminating the need for manual monitoring while maintaining high detection accuracy through sophisticated automated algorithms.
Solution Approach 2:
The system incorporates continuous feedback loops where telemetry data is constantly collected and analyzed, efficiency scores are calculated and compared against benchmarks, and recommendations are generated and implemented. This feedback mechanism enables the system to learn from actual cloud usage patterns and improve its detection accuracy over time while operating automatically, reducing both time consumption and labor requirements.
Data Source
AI summary
Systems and methods are provided for identifying cloud inefficiencies. The method includes obtaining telemetric log data for services, distinct from the server, executing on cloud computing systems. The method also includes determining disaggregation data for the services based on the telemetric log data by applying disaggregation algorithms. The method also includes forming feature vectors based on the telemetric log data. The method also includes identifying software of service types and cloud wastage templates by inputting the feature vectors to trained classifiers, wherein the cloud wastage templates follow conventions of a domain specific language (DSL) that describe the cloud computing systems. Each classifier is a machine-learning model trained to identify cloud wastages for predetermined states of the cloud computing systems. The method also includes determining cloud states of computing resources used by the services based on the disaggregation data. The method also includes cataloging cloud inefficiencies using the cloud wastage templates.