Multi-Layer Workload Forecasting for Proactive Cloud Resource Allocation
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Solution Overview
Problem
Cloud service providers face challenges in proactively managing computational workloads due to reactive monitoring techniques, leading to errors and outages before remedial actions can be taken.
Innovation Solution
Implementing multi-layer forecasting using time-series analysis to generate workload forecasts that capture relationships among computational resources at various levels of granularity, utilizing models like Holt-Winters Exponential Smoothing, ARIMA, and TBATS to predict future resource consumption and detect anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive monitoring techniques are used, then system simplicity is maintained, but errors and outages occur before remedial action can be taken
Solution Approach 1:
The patent implements multi-layer forecasting that performs preliminary analysis of computational workload trends across multiple layers (host nodes, virtual machines, applications) before errors occur. By predicting future workload patterns and detecting anomalies proactively, the system enables remedial actions to be taken before outages happen, transforming reactive monitoring into proactive prevention while maintaining manageable system complexity through automated multi-layer analysis.
2Reliability
If multi-layer forecasting is implemented, then proactive error detection is achieved, but computational resource consumption increases
Solution Approach 1:
The patent segments the monitoring system into multiple independent layers (host node layer, virtual machine layer, application layer), each performing focused analysis on its specific components. This segmentation allows proactive error detection across the entire system while optimizing resource consumption by processing data at appropriate granularities - not all layers require the same computational intensity, enabling selective resource allocation based on workload characteristics and error probability at each layer.
3Measurement precision
If detailed time-series analysis is performed, then forecasting accuracy improves, but data processing time increases
Solution Approach 1:
The patent applies partial time-series analysis by selecting and processing only the most critical temporal patterns and parameters at each layer rather than performing exhaustive analysis on all data. The multi-layer approach allows the system to process data at different granularities - finer time-resolution analysis for critical layers and coarser analysis for less critical layers - achieving sufficient forecasting accuracy while reducing overall data processing time through selective, prioritized analysis.
Data Source
AI summary
Techniques for multi-layer forecasting of computational workloads are disclosed. A system identifies a level of granularity associated with a request to forecast a computational workload for a particular entity. The system obtains attribute data of computational resources at the specified level of granularity. The system determines whether computational resources, not specified in the request, should be included in a workload forecast. The system applies a time-series forecast model to time-series data obtained from computational resources associated with the request. The system presents one or more workload forecasts for computational workloads associated with the request.