Automated Resource Matching for Cloud Request Artifacts
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Solution Overview
Problem
Conventional information processing systems lack an efficient mechanism for identifying and assigning service requests to appropriate enterprise resources in cloud-based systems, leading to inefficiencies in resource utilization.
Innovation Solution
An information processing system with a processing platform that includes an artifact details analysis module, an enterprise resource identification module, and an artifact-resource matching module, utilizing a rules engine with customer and team member profile data, predictive machine learning, and data-science-driven statistical modeling to automate the matching process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual matching approaches are used to assign service requests to enterprise resources, then system complexity is reduced, but productivity and matching accuracy deteriorate
Solution Approach 1:
The system enables self-service through automated resource matching where the artifact-resource matching module automatically analyzes request artifacts, identifies suitable enterprise resources, and performs assignments without manual intervention. The system uses machine learning models and statistical modeling to autonomously complete the matching process, eliminating the need for manual matching while significantly improving productivity and matching accuracy.
2Productivity
If automated matching mechanisms are implemented, then productivity improves, but device complexity increases
Solution Approach 1:
The system segments the complex automated matching process into distinct functional modules: artifact details analysis module for analyzing request artifacts, enterprise resource identification module for identifying suitable resources, and artifact-resource matching module for performing the actual matching and assignment. This modular segmentation manages system complexity by organizing functions into independent, manageable components while maintaining high productivity through automation.
Solution Approach 2:
The system introduces intermediary components including a rules engine that leverages customer and team member profile data, predictive machine learning models, and data-science-driven statistical modeling. These intermediaries act as intelligent layers between raw data and decision-making, automating the matching process while managing complexity through specialized intermediate processing stages.
3Ease of operation
If conventional mechanisms are used for identifying and assigning service requests, then ease of operation is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing request artifacts, identifying suitable enterprise resources, and assigning resources without requiring manual operational intervention. This maintains ease of operation from the user perspective while dramatically improving resource utilization efficiency through intelligent automated matching based on artifact details and resource capabilities.
Data Source
AI summary
An apparatus in one embodiment comprises a processing platform that includes a plurality of processing devices each comprising a processor coupled to a memory. The processing platform is configured to implement at least a portion of one or more cloud-based systems. The processing platform further comprises an artifact details analysis module configured to determine one or more enterprise resource attributes required for resolving a request artifact, an enterprise resource identification module configured to identify one or more available enterprise resources associated with the one or more resource attributes required for resolving the request artifact, and an artifact-resource matching module configured to determine one of the identified available enterprise resources to assign to the request artifact based on one or more usage parameters attributed to the identified available resources and route the request artifact to the determined available enterprise resource.