Workload Allocation in Data Center Storage Systems
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Solution Overview
Problem
In data centers with multiple storage systems, workload allocation is challenging due to the need to balance performance impact and deployment compliance, often requiring significant configuration or reconfiguration efforts.
Innovation Solution
A method and apparatus that select storage systems based on a combination of performance impact scores and deployment scores to optimize workload allocation, considering both performance and deployment compatibility, thereby minimizing configuration efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workload allocation is based on performance impact scores alone, then storage system performance is optimized, but deployment compliance and configuration effort are not considered
Solution Approach 1:
The patent introduces deployment scores as an additional parameter to complement performance impact scores. This dual-parameter approach transforms the workload allocation decision-making process by incorporating deployment compliance metrics, configuration effort estimates, and policy adherence factors alongside traditional performance metrics, thereby resolving the contradiction between performance optimization and deployment complexity
Solution Approach 2:
The workload allocation system acts as an intermediary that evaluates multiple storage systems against both performance and deployment criteria. It mediates between the competing requirements of performance optimization and deployment simplicity by selecting storage systems that achieve an optimal balance between these two dimensions through a unified scoring mechanism
2Ease of manufacture
If workload allocation considers deployment scores and compliance, then configuration effort is reduced, but the complexity of the selection process increases
Solution Approach 1:
The patent merges multiple evaluation criteria including deployment compliance, configuration effort, policy adherence, and performance impact into a unified workload allocation system. This consolidation integrates previously separate decision-making factors into a single comprehensive framework that automatically evaluates storage systems across all dimensions simultaneously
Solution Approach 2:
The workload allocation system performs self-evaluation by automatically calculating deployment scores, assessing configuration requirements, and determining policy compliance without manual intervention. The system autonomously compares multiple storage systems against the unified criteria and selects the optimal target, eliminating the need for complex manual selection processes
3Reliability
If storage systems are selected based on combined performance impact and deployment scores, then workload allocation optimality is improved, but the calculation and evaluation process becomes more complex
Solution Approach 1:
The patent implements preliminary evaluation by pre-calculating deployment scores, configuration effort estimates, and policy compliance metrics for each storage system before the actual workload allocation decision. This advance preparation creates a ready-to-use evaluation framework that simplifies the final selection process while ensuring comprehensive assessment across all critical dimensions
Data Source
AI summary
A method and apparatus for assigning an allocated workload in a data center having multiple storage systems includes selecting one or more storage systems to be assigned the allocated workload based on a combination of performance impact scores and deployment scores. By considering both performance impact and deployment effort, the allocated workload is able to be assigned with a view not only toward storage system performance, but also with a view toward how deployment on a particular storage system would comply with data center policies and the amount of configuration effort it would take to enable the workload to be implemented on the target storage system. This enables workloads to be allocated within the data center while minimizing the required amount of configuration or reconfiguration required to implement the workload allocation within the data center.