Metric Mapper for Interoperability of Distinct Workload Models
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Solution Overview
Problem
Information processing systems face inefficiencies due to the incompatibility of metrics across different workload and resource models, leading to excessive development of standalone models that fail to interoperate, resulting in time and expense inefficiencies.
Innovation Solution
A metric mapper is implemented within an information processing system to establish associations between metrics from various models, enabling interoperability by mapping metrics across multiple models and levels, and facilitating the matching of service requirements with service capabilities despite metric incompatibilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple distinct models are developed for different workloads and resources, then model-specific accuracy is improved, but system complexity and development expense increase
Solution Approach 1:
A metric mapper is introduced as an intermediary component that translates between incompatible metrics from different models. The metric mapper establishes mappings between source metrics (from workload models) and target metrics (from resource models), enabling interoperability without requiring changes to the original models. This resolves the contradiction by maintaining model-specific accuracy while reducing system complexity through centralized metric translation.
Solution Approach 2:
The metric mapper serves multiple functions: it maps metrics between different models, validates metric compatibility, and enables cross-model queries. By making this component universal and multi-functional, the system avoids developing separate translation mechanisms for each model pair, thereby reducing overall system complexity while preserving the ability to work with multiple distinct models.
2Adaptability or versatility
If multiple standalone models are developed for different workloads and resources, then model-specific functionality is improved, but development time and expense increase
Solution Approach 1:
The metric mapper acts as a pre-built intermediary that handles metric translation between models. By establishing metric mappings in advance and storing them in a metric mapper database, the system avoids time-consuming ad-hoc metric translations during model development and integration. This preserves model-specific functionality while significantly reducing development time.
Solution Approach 2:
Metric mappings are established and stored in the metric mapper database before actual model integration occurs. This preliminary action of pre-defining metric relationships allows models to be developed independently and then quickly integrated without time-consuming metric alignment work, thus preserving versatility while reducing development time.
3Adaptability or versatility
If metric mappings are established between multiple models, then interoperability is improved, but processing overhead increases
Solution Approach 1:
The metric mapper creates and stores copies of metric definitions and mappings in a centralized database. Instead of performing complex real-time metric translations, the system uses pre-computed metric mappings stored in the database. This copying approach maintains interoperability while reducing processing overhead by replacing complex calculations with simple lookups.
Solution Approach 2:
Metric mappings are established and cached in advance in the metric mapper database. This preliminary computation of metric relationships allows the system to avoid repeated complex translation operations during runtime. The pre-established mappings reduce processing overhead while maintaining full interoperability between models.
Data Source
AI summary
An apparatus in one embodiment comprises a processing platform comprising one or more processing devices. The processing platform implements a database configured to store metric and level information for each of a plurality of models, and a metric mapper adapted to interface with the database. The metric mapper is configured to utilize the metric and level information stored in the database to establish an association between a first metric characterizing at least one of a workload and a resource in accordance with a first one of the models and a second metric characterizing at least one of a workload and a resource in accordance with a second one of the models. The established association is illustratively applied to relate service requirements specified at least in part using the first metric of the first model to service capabilities specified at least in part using the second metric of the second model.