Micro Workload Modeling for IT Resource Allocation
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Solution Overview
Problem
Managing complex IT infrastructure becomes increasingly difficult and costly as new platforms and resources are integrated, particularly in cloud computing and software-defined data centers, due to challenges in accurately provisioning resources for workloads with unknown resource sizing.
Innovation Solution
The micro workload modeling framework analyzes workloads to determine functional patterns, resource consumption demand profiles, and micro workload distributions, converting these into resource requirements for efficient allocation of resources on IT infrastructure, using normalized units of resource consumption metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional resource provisioning methods are used for workloads with unknown resource sizing, then resources may be over-provisioned to ensure availability, but resource waste and increased costs occur
Solution Approach 1:
The system performs preliminary analysis of workloads to determine functional patterns and resource consumption demand profiles before actual resource allocation. By analyzing workload characteristics in advance and converting them to micro workload distributions, the system establishes resource requirements proactively, avoiding both over-provisioning and under-provisioning during runtime.
2Measurement precision
If detailed analysis of each workload is performed to accurately determine resource requirements, then resource allocation precision improves, but processing time and complexity increase
Solution Approach 1:
The system segments workloads into functional patterns (e.g., compute-intensive, I/O-intensive, memory-intensive) and analyzes each pattern type separately to determine resource consumption demand profiles. This segmentation approach simplifies the analysis complexity while maintaining measurement precision, as recurring patterns can be analyzed once and reused for multiple workloads of the same type.
Solution Approach 2:
The system transforms workload characteristics into standardized parameters through functional pattern classification and micro workload distribution conversion. By changing the representation parameters from raw workload data to normalized micro workload units, the system enables efficient comparison and resource allocation without performing complex analysis on each individual workload.
3Adaptability or versatility
If IT infrastructure expands with new platforms and resources, then system capability and flexibility improve, but management complexity and costs increase
Solution Approach 1:
The system creates a universal management framework that handles diverse IT infrastructure resources (compute, storage, networking, memory) through a common approach. By classifying workloads into functional patterns and using standardized micro workload distributions, the same resource allocation methodology applies across different platform types and resource categories, reducing management complexity while maintaining infrastructure flexibility.
Data Source
AI summary
A method includes selecting a given workload associated with at least one application type, and analyzing the given workload to determine a set of functional patterns describing resource structures for implementing functionality of the at least one application type. The method also includes determining resource consumption demand profiles for each of the set of functional patterns and determining micro workload distributions for each of the resource consumption demand profiles, a given one of the micro workload distributions comprising a number of micro workloads, each micro workload comprising a normalized unit of resource consumption metrics. The method further includes converting the micro workload distributions for each of the resource consumption demand profiles into a set of resource requirements for running the given workload on an information technology infrastructure, and allocating resources of the information technology infrastructure to the given workload based on the set of resource requirements.