Cluster-Aware Storage Provisioning for Fault Minimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cluster management tools in networked computing environments, such as cloud computing, rely heavily on manual configuration by experts, which can lead to data path faults, inefficiencies, and increased costs due to the complexity of modern enterprise storage systems, and lack comprehensive end-to-end performance optimization and availability solutions.
Innovation Solution
An automated method and system for cluster-aware resource provisioning that determines storage environment characteristics, identifies workload requirements, analyzes policies and best practices to optimize storage configurations, and generates a data path plan minimizing errors in workload processing, incorporating intelligent analytics for planning and provisioning storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration by experts is used, then storage environment can be configured, but data path faults and errors may be introduced
Solution Approach 1:
The system performs self-configuration by automatically discovering storage resources, analyzing workload requirements, and generating optimized data path configurations without human intervention. The automated provisioning system evaluates multiple possible configurations and selects the optimal one based on predefined policies and best practices, eliminating manual configuration errors while maintaining system reliability.
Solution Approach 2:
The patent replaces manual expert configuration (mechanical human operation) with an automated software-based provisioning system. The system uses algorithms to analyze storage characteristics, workload requirements, and policy constraints to automatically generate and validate data path configurations, substituting human expertise with computational analysis to eliminate data path faults.
2Ease of operation
If manual configuration by experts is used, then storage environment can be configured, but time consumption increases
Solution Approach 1:
The system performs preliminary configuration by automatically discovering storage resources and analyzing workload requirements before actual provisioning. The automated system pre-evaluates multiple configuration options based on policies and best practices, generating an optimized data path plan in advance, which significantly reduces the time required compared to manual expert configuration.
Solution Approach 2:
The patent transforms the configuration process from manual parameter setting to automated parameter optimization. The system dynamically adjusts configuration parameters by analyzing storage characteristics, workload demands, and policy constraints, automatically generating optimal data path configurations that would take experts significant time to determine manually.
3Productivity
If automated provisioning is implemented, then configuration time is reduced, but system complexity increases
Solution Approach 1:
The automated provisioning system is segmented into distinct functional modules: storage resource discovery, workload requirement analysis, policy evaluation, configuration generation, and validation. Each module handles a specific aspect of the provisioning process, making the complex system manageable and maintainable while enabling rapid automated configuration.
Solution Approach 2:
The patent introduces an automated provisioning system as an intermediary layer between storage resources and workloads. This intermediary automatically manages the complexity of configuration by analyzing multiple factors and generating optimized data paths, shielding users from system complexity while maintaining high provisioning speed.
4Productivity
If automated provisioning is implemented, then efficiency is improved, but comprehensive optimization requires analyzing multiple factors
Solution Approach 1:
The automated provisioning system performs multiple functions simultaneously: it discovers storage resources, analyzes workload requirements, evaluates policies, generates configurations, and validates data paths. This multi-functional approach enables comprehensive optimization across all factors while maintaining efficient automated operation, as the system handles diverse optimization criteria through a unified provisioning framework.
Data Source
AI summary
Embodiments of the present invention provide an approach for providing cluster-aware (storage) resource provisioning in a networked computing environment (e.g., a cloud computing environment) based upon policies, best practices, and/or storage cluster/environment configurations. In a typical embodiment, a set of characteristics (e.g., computing resources/components, etc.) of a storage environment will be determined. A set of requirements for a set of workloads to be processed by the components of the storage environment will then be identified. A set of policies and a set of best practices will then be determined to identify a configuration of the storage environment to optimize the processing of the set of workloads according to the set of requirements. Based on the configuration, a plan will be generated that indicates a data path through the set of computing resources that minimizes a potential for error in processing the set of workloads.