Predictive Cloud Service Provisioning Using Workload Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing complexity and demand of workloads in cloud computing make it labor-intensive and time-consuming to efficiently allocate and provision cloud services that meet performance, security, and cost constraints, as existing methods lack automation in predicting and optimizing cloud service offerings.
Innovation Solution
A system that obtains contextual information about existing workloads, analyzes it to identify and predict new or modified cloud services, and automatically generates or modifies service offerings to optimize workload support, using a state engine and policy engine to ensure compliance with constraints, and presents these offerings to end-users for selection and provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud services are allocated and provisioned manually to meet increasing workload complexity, then service customization and compliance with constraints can be achieved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system performs predictive analytics on workload patterns and infrastructure data to proactively identify and prepare optimal cloud service configurations before they are needed. This preliminary analysis enables automated provisioning recommendations that reduce manual intervention while maintaining compliance with performance, security, and cost constraints
Solution Approach 2:
The system implements self-service automation where the cloud management platform automatically analyzes infrastructure data, predicts workload requirements, and provisions services without manual intervention. The automated service allocation system evaluates multiple constraints and makes provisioning decisions autonomously, significantly reducing labor intensity while maintaining service customization capabilities
2Productivity
If predictive analytics are implemented to forecast workload patterns, then resource allocation efficiency improves, but system complexity and computational requirements increase
Solution Approach 1:
The predictive analytics system is divided into modular components: workload pattern analysis modules, infrastructure data processing modules, and service recommendation engines. Each module handles specific aspects of the predictive process independently, reducing overall system complexity while maintaining comprehensive analytics capabilities for improved resource allocation
3Measurement precision
If comprehensive infrastructure data is collected and analyzed, then accuracy of service predictions improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing and filtering of infrastructure data to identify only the most relevant features for prediction accuracy. By pre-processing data to extract key performance indicators and filtering out redundant information, the system maintains high prediction accuracy while reducing the computational burden and processing time required for comprehensive data analysis
Data Source
AI summary
Aspects of the present disclosure involve system and methods for the automatic prediction and offering of cloud computing services to end users. The disclosed systems generate a predictive cloud service offering that is presented to end users via an interface, such as a graphical-user interface. The predictive cloud services may be consumed by the end-users to support various workloads.