Computational Workload Forecasting With Host-and-Sibling Resource Models
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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, as existing methods fail to capture the interdependencies among computing resources at sufficient granularity.
Innovation Solution
Implementing multi-layer forecasting using time-series analysis to generate workload forecasts that account for relationships among computational resources by applying trained time-series models to historical and current data, identifying interdependencies and generating forecasts for both host and sibling nodes, and utilizing models like ARIMA and TBATS to predict future resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive monitoring techniques are used to track computational resources, then resource utilization can be observed, but errors and outages occur before remedial action can be taken
Solution Approach 1:
The system performs preliminary actions by generating workload forecasts using time-series analysis before actual resource exhaustion occurs. The forecasting module predicts future resource utilization patterns, enabling proactive capacity planning and resource allocation adjustments before errors or outages happen, thus resolving the contradiction between reliability and response time.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual resource utilization and comparing it against forecasted values. This feedback loop enables the system to detect deviations early and trigger remedial actions, improving both reliability and response time by moving from reactive to proactive management.
2Reliability
If multi-layer forecasting with time-series analysis is implemented, then proactive workload management is enabled, but system complexity increases
Solution Approach 1:
The forecasting system is segmented into distinct modular components: data collection module, time-series analysis module, forecast generation module, and resource allocation module. Each component handles a specific aspect of the forecasting process, making the overall complex system manageable and maintainable while enabling reliable proactive workload management.
Solution Approach 2:
The time-series analysis framework is designed to be universal and applicable to multiple resource types (CPU, memory, storage, network) and multiple forecasting scenarios. This multi-functionality reduces complexity by using a single unified approach rather than separate specialized systems for each resource type.
3Measurement precision
If forecasts are generated for both host and sibling nodes, then interdependencies are captured, but computational overhead increases
Solution Approach 1:
The system applies local quality by generating forecasts at different levels of detail for different nodes. Host nodes receive comprehensive multi-layer forecasts capturing all interdependencies, while sibling nodes receive appropriately scoped forecasts based on their specific relationships to the host. This selective approach improves precision where needed while reducing unnecessary computational overhead for less critical nodes.
Solution Approach 2:
The system implements partial action by selectively forecasting interdependencies based on their significance. Not all sibling nodes require full forecasting analysis - the system identifies and focuses computational resources on the most influential relationships, achieving sufficient precision without the excessive computational cost of analyzing all possible node interactions.
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.