Heterogeneous Element Recommendation Engine for IT Infrastructure
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Solution Overview
Problem
Scheduling application workloads on heterogeneous elements of information technology infrastructure is increasingly difficult due to the growing diversity of applications and workload sizes, as well as the increasing heterogeneity of hardware elements and fabrics, which challenges conventional schedulers that require specific job statements rather than abstract hardware-agnostic service level objective expressions.
Innovation Solution
A method involving a heterogeneous element recommendation engine that aligns application workload requests with effective hardware and fabric configurations, transforming hardware-agnostic service level objective expressions into detailed job requests, allowing the scheduler to optimally utilize heterogeneous IT infrastructure resources, including alignment, re-alignment, learning, and upgrade functions to improve resource utilization and productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional schedulers are used that require specific job statements, then scheduling can be performed with simple hardware configurations, but the system cannot effectively utilize heterogeneous IT infrastructure resources or interpret hardware-agnostic service level objective expressions
Solution Approach 1:
The patent introduces a recommendation engine as an intermediary component between the scheduler and heterogeneous IT infrastructure. This engine translates hardware-agnostic service level objective expressions into specific hardware configuration recommendations, enabling the system to adapt to diverse infrastructure without increasing scheduler complexity. The recommendation engine acts as a mediator that handles the complexity of hardware heterogeneity while preserving the simplicity of the original scheduler.
Solution Approach 2:
The system segments the scheduling function into two independent components: the original scheduler that handles workload allocation and a new recommendation engine that handles hardware configuration translation. This segmentation allows each component to specialize in its specific task, with the recommendation engine managing the complexity of heterogeneous hardware while the scheduler maintains its simple, efficient workload distribution logic.
2Adaptability or versatility
If hardware-agnostic service level objective expressions are used, then application diversity is supported, but conventional schedulers cannot process these abstract expressions
Solution Approach 1:
The recommendation engine serves as an intermediary that bridges the gap between hardware-agnostic service level objective expressions and conventional scheduler requirements. It parses and interprets abstract expressions, translating them into concrete hardware configuration specifications that the scheduler can process, thereby enabling support for application diversity without compromising scheduling operational simplicity.
Solution Approach 2:
The system performs preliminary translation of hardware-agnostic service level objective expressions into specific hardware configuration recommendations before the scheduler executes workload allocation. This preliminary action converts abstract requirements into actionable scheduling parameters, enabling the scheduler to process diverse application types efficiently without directly interpreting complex abstract expressions.
3Productivity
If the system monitors and dynamically modifies hardware configurations, then resource utilization improves, but the complexity of configuration management increases
Solution Approach 1:
The system implements a feedback mechanism where the recommendation engine continuously monitors workload performance and infrastructure resource availability, then dynamically adjusts hardware configuration recommendations accordingly. This feedback loop enables automatic optimization of resource utilization without manual intervention, improving productivity while the system self-manages configuration complexity through automated monitoring and adjustment.
Solution Approach 2:
The recommendation engine provides self-service configuration management by automatically monitoring system state and modifying hardware configuration recommendations based on current workload requirements and resource availability. This self-service capability improves resource utilization while minimizing the need for external configuration management, as the system autonomously adapts to changing conditions.
Data Source
AI summary
A method includes selecting a given hardware configuration for a given application workload based on aligning an application workload specification template with a first hardware configuration template in a repository comprising a plurality of hardware configuration templates, the application workload specification template being generated by parsing and interpreting hardware-agnostic service level objective expressions of an application request. The method also includes scheduling the given application workload to run on information technology (IT) infrastructure utilizing the given hardware configuration, the given hardware configuration comprising a first set of a plurality of heterogeneous elements of the IT infrastructure, monitoring the IT infrastructure, and modifying the given hardware configuration for the given application workload based on aligning the application workload specification template with a second hardware configuration template in the repository responsive to said monitoring, the modified hardware configuration comprising a second set of the plurality of heterogeneous elements of the IT infrastructure.