Workload Planner Headroom Compliance Automation
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Solution Overview
Problem
Existing workload planners require manual user intervention to resolve headroom and service level expectation compliance discrepancies, which can lead to erroneous results and inefficient workload management in storage systems, especially due to workload spikes and recurring peak usage patterns.
Innovation Solution
A method and apparatus that utilize a workload planner to automatically determine headroom and service level expectation compliance by selectively removing buckets of key performance indicator data through a set of rules, creating a redacted time series to recalculate compliance, thereby enabling automated resolution of discrepancies without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual user intervention is used to resolve headroom and service level compliance discrepancies, then user control and decision-making are maintained, but erroneous results occur and workload management becomes inefficient
Solution Approach 1:
The workload planner automatically performs compliance assessments by applying rules to identify and exclude anomalous workload periods. The system self-corrects compliance discrepancies without requiring manual user intervention, thereby eliminating human errors while maintaining assessment accuracy and improving operational efficiency.
2Measurement precision
If all workload data including peak usage periods is considered in compliance assessment, then comprehensive evaluation is achieved, but erroneous non-compliance results occur due to workload spikes
Solution Approach 1:
The system extracts and excludes anomalous workload periods from the compliance assessment data set. By identifying workload spikes and recurring peak usage patterns that do not represent normal operational conditions, the system removes these outliers to prevent erroneous non-compliance results while maintaining measurement precision for genuine compliance evaluation.
Solution Approach 2:
The system converts potentially harmful workload spike data into beneficial information by using it to train anomaly detection algorithms. These identified anomalies then serve as exclusion criteria, transforming what would be harmful misleading data into a useful mechanism for improving compliance assessment reliability.
3Productivity
If automated rule-based exclusion of workload data is implemented, then manual errors are eliminated and efficiency improves, but device complexity increases
Solution Approach 1:
The workload planner is segmented into distinct functional modules: anomaly detection module, rule application module, data exclusion module, and compliance assessment module. Each module performs a specific function in the automated workflow, making the overall complex system manageable through clear separation of concerns while maintaining high productivity through automation.
Data Source
AI summary
A workload planner is used to determine headroom and SLE (Service Level Expectation) compliance of a storage system. The historical workload of the storage system over a set of monitoring intervals is obtained, and optionally a new workload to be moved to the storage system is added to the historical workload. Headroom compliance for components of the storage system, and service level expectation compliance on a per-storage group basis are determined by the workload planner. If a headroom or service level expectation violation is determined for one or more of the monitoring intervals, a set of rules are applied to selectively remove monitoring intervals from the time series. The redacted time series is then used to determine headroom and service level expectation compliance. If no violations are found, based on the redacted time series, the storage system meets headroom and SLE requirements.