N-Tier Simulation for Cloud Resource Demand Prediction
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Solution Overview
Problem
Existing cloud resource allocation methods are inadequate in addressing unforeseen and highly variable demand fluctuations, failing to timely match resources with demand patterns that have not been previously seen, leading to inadequate performance and reliability in cloud computing environments.
Innovation Solution
The implementation of an n-tier simulation method that uses a combination of predictive models to forecast future resource demands by progressively looking ahead in time, extracting features from predicted events, and inputting them into a demand level prediction model to identify potential spikes in resource usage, allowing for proactive resource provisioning to avoid demand surges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cloud resource allocation methods are used, then resource allocation is simple and fast, but the system cannot handle unforeseen demand fluctuations and maintains poor reliability
Solution Approach 1:
The system performs preliminary simulation and prediction of demand patterns before actual resource allocation decisions are made. By using predictive models to forecast future resource demands and simulate various scenarios in advance, the system prepares allocation strategies proactively, enabling reliable handling of unforeseen demand fluctuations while maintaining manageable complexity through structured prediction frameworks.
Solution Approach 2:
The patent introduces predictive models and simulation engines as intermediary components between current resource states and future allocation decisions. These intermediaries analyze historical data, generate demand forecasts, and evaluate multiple allocation scenarios before executing actual resource provisioning, thereby enhancing reliability without directly complicating the core allocation mechanism.
2Adaptability or versatility
If n-tier simulation with multiple predictive models is implemented, then unforeseen demand patterns can be predicted, but the computational complexity and processing time increase significantly
Solution Approach 1:
The predictive system is divided into multiple tiers, each handling specific aspects of demand prediction. The first tier processes historical data and identifies basic patterns, while subsequent tiers build upon these results to predict more complex unforeseen demand fluctuations. This segmentation allows the system to achieve high adaptability through multiple prediction layers while managing processing time by distributing computational tasks across tiers rather than executing all predictions simultaneously.
Solution Approach 2:
The system applies predictive modeling at varying depths depending on the situation. For routine demand patterns, lighter prediction models are used, while more comprehensive n-tier simulation is activated only when unforeseen fluctuations are detected or anticipated. This partial application of complex prediction mechanisms maintains adaptability for unusual scenarios while reducing average processing time through selective model deployment.
3Measurement precision
If historical data is used for training predictive models, then predictions are accurate for known patterns, but the models fail to predict previously unseen demand scenarios
Solution Approach 1:
The system performs preliminary training of predictive models on historical data to establish baseline accuracy for known demand patterns. However, it also incorporates preliminary simulation of hypothetical unforeseen scenarios during the model development phase, enabling the models to learn not only from actual historical patterns but also from simulated anomalies, thereby improving both precision for known patterns and adaptability to unforeseen situations.
Solution Approach 2:
The predictive models are designed to be dynamic and continuously adaptable. While initially trained on historical data for accuracy, the models incorporate mechanisms to detect when actual demand deviates from historical patterns and automatically adjust their parameters and predictions in real-time. This dynamic adaptation allows the system to maintain precision for known patterns while becoming increasingly effective at predicting unforeseen scenarios as it learns from new data.
Data Source
AI summary
In a multi-tiered simulation configuration, a combination of predictive models is executed such that each tier in the multi-tiered simulation configuration executes at least one predictive model to produce a corresponding set of predicted events, and a predicted event from a first tier in the configuration forms an input to a next tier in the configuration. Using a subset of a selected set of predicted events outputted from a corresponding selected tier in the multi-tiered simulation configuration, a set of features is extracted, each feature in the set of features having an effect on an outcome of the simulated process. The set of features is used in a demand level prediction model to predict a threshold demand, wherein reaching the threshold demand in an actual utilization of a computing resource is indicative of a likelihood of an unforeseen rise in a demand for the computing resource after a period.