Workload Resource Usage Predictive Model for Hyperconverged Clusters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Legacy techniques for managing computing cluster resources in distributed virtualization systems fail to account for dynamic workload characteristics, leading to inefficient resource allocation and lack of interactivity in long-range planning, resulting in underutilization of resources and inadequate feedback for system administrators.
Innovation Solution
Implementing a workload resource usage predictive model that maps workload parameters to resource usage metrics to facilitate time-based resource planning, allowing for autonomous hardware deployment recommendations and 'What-If' scenario analysis, thereby optimizing resource allocation and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If legacy techniques implement resource planning based on measurements pertaining to underlying resources in isolation, then resource allocation can be determined, but the planning fails to account for dynamic workload characteristics leading to inefficient resource allocation
Solution Approach 1:
The patent transforms resource planning from static measurements to dynamic predictions by changing the parameters from historical resource usage data to future workload predictions. The workload modeling component uses parameters such as workload type, growth rate, and seasonal variations to predict future resource needs, enabling the system to adapt to dynamic workload characteristics while maintaining efficient resource allocation.
Solution Approach 2:
The system performs preliminary actions by predicting future workload characteristics before actual resource allocation decisions are made. The workload modeling component analyzes historical data and trends to forecast future resource requirements, allowing administrators to plan resource capacity in advance rather than reacting to current usage patterns alone.
2Reliability
If administrators deploy additional nodes to satisfy peak resource usage over long time horizons, then resource availability is ensured, but resources may be idle or underused for extended periods
Solution Approach 1:
The patent applies dynamics by making resource allocation flexible and time-dependent rather than static. The system continuously updates workload predictions based on changing conditions and adjusts resource recommendations accordingly. This allows the system to ensure resource availability when needed while avoiding permanent over-provisioning that would lead to sustained underutilization.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual workload patterns and comparing them against predictions. This feedback loop allows the system to learn from past allocations and improve future predictions, ensuring that resource availability is maintained while minimizing periods of underutilization through data-driven adjustments.
3Measurement precision
If legacy techniques assess peak resource usage in a given planning time horizon, then resource increases can be determined, but the techniques do not account for user-specified workload changes
Solution Approach 1:
The patent changes the parameters from fixed peak usage measurements to dynamic workload scenario modeling. Administrators can specify different workload scenarios with varying growth rates, seasonal patterns, and event-driven changes. The system adjusts resource predictions based on these parameter changes, providing precise measurements that adapt to different workload assumptions rather than relying on a single peak assessment.
4Productivity
If long-range resource planning is performed without workload modeling, then resource capacity can be provisioned, but the planning lacks interactivity and provides inadequate feedback for administrators
Solution Approach 1:
The patent implements feedback by providing administrators with interactive workload modeling capabilities that show the impact of different assumptions on resource requirements. The system presents scenario-based predictions, confidence intervals, and sensitivity analyses that give administrators actionable insights. This feedback mechanism maintains planning efficiency while dramatically improving ease of operation through intuitive interaction and clear presentation of results.
Data Source
AI summary
Systems for computing cluster management. One embodiment commences upon receiving a set of observed workload parameters corresponding to one or more observable workloads that run in a computing cluster. While the workloads are running, workload stimulus and cluster response observations are taken and used to generate a workload resource usage predictive model based on mappings or correlations between the observable workloads parameters and observed resource usage measurements. A set of planned workloads are applied to the workload resource usage predictive model to predict a set of corresponding predicted resource usage demands. The predicted resource usage demands are then mapped to a set of recommended hardware to form resource deployment recommendations that satisfy at least some of the corresponding resource usage demands while also observing a set of hardware model compatibility constraints. The resource deployment recommendations that satisfy the set of hardware model compatibility constraints are displayed in a user interface.