Intelligent Provisioning Engine for Cloud Resource Optimization
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Solution Overview
Problem
In cloud computing environments, there is a challenge in making inter-data center and inter-cloud provisioning decisions due to the need for intelligent, policy-driven management of service resources, which is hindered by the lack of effective tools for real-time data analysis and integration across distinct systems.
Innovation Solution
An intelligent provisioning engine that accesses a service intelligence repository to collect configuration information, identify constraints and policies, and gather real-time data from monitoring systems to determine a provisioning plan, integrating with ancillary systems for scalable and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data collection and analysis is implemented across multiple systems, then provisioning decision quality is improved, but system complexity and data integration difficulty increase
Solution Approach 1:
The patent introduces an intelligent provisioning engine as an intermediary component that collects real-time data from multiple monitoring systems (service monitoring, load balancing, risk engines) and processes it centrally. This mediator architecture allows complex data integration without increasing the complexity of individual source systems, as the provisioning engine handles the integration logic and data correlation in a centralized manner.
2Productivity
If comprehensive real-time monitoring data is collected from multiple sources, then resource utilization optimization is improved, but data processing time and computational overhead increase
Solution Approach 1:
The patent implements preliminary action by pre-defining rules and policies in the service intelligence repository before real-time data arrives. The provisioning engine evaluates incoming real-time data against these pre-established rules and policies, allowing rapid decision-making without requiring complex real-time analysis. This approach optimizes resource utilization by having decisions ready in advance while minimizing data processing time during actual provisioning events.
3Adaptability or versatility
If inter-system provisioning capabilities are implemented, then service scalability is improved, but coordination and management difficulty increase
Solution Approach 1:
The patent implements a universal provisioning engine that can manage multiple types of service resources across different systems through a single integrated platform. The engine handles diverse provisioning scenarios (scaling, load balancing, risk management) using common rules and policies stored in the service intelligence repository. This multi-functional approach enables service scalability across inter-system boundaries while reducing coordination difficulty by providing a unified management interface rather than separate coordination mechanisms for each system.
Data Source
AI summary
Embodiments of the present invention provide an approach for intelligent service resource provisioning among distinct systems in a networked computing environment (e.g., a cloud computing environment). Specifically, the embodiments of the present invention provide an intelligent provisioning engine (hereinafter engine) that accesses a service intelligence repository that comprises configuration information pertaining to a set of service resources available on a set of systems. The engine may also receive/identify a set of rules pertaining to any constraints on the set of service resources as well as a set of policies pertaining to provisioning the set of service resources. Still yet, the engine can collect real-time data pertaining to operational characteristics of the set of service resources. Based on the information/data collected, the engine may determine a plan for provisioning the set of service resources and integrate the plan with ancillary systems/engines (e.g., scaling, provisioning, monitoring, etc.) for implementation of the plan.