Multi-Model Resource Prediction System for Cloud Latency
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Solution Overview
Problem
Computing environments, such as cloud computing, face challenges in managing finite resources due to fluctuating demand, leading to issues like increased latency and suboptimal allocation of resources during peak and off-peak times.
Innovation Solution
A system that uses multiple models, including machine-learning models, to predict resource requests over a future time-period, allowing for accurate provisioning adjustments by selecting the most accurate model based on seasonality, short-term trends, and historical data to generate nuanced predictions for resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple prediction models are executed to improve prediction accuracy, then resource allocation accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the prediction task into three distinct models: a first model for long-term trends (spanning weeks to months), a second model for medium-term patterns (days to weeks), and a third model for short-term fluctuations (hours to days). Each model is trained on different time horizons and produces predictions for specific intervals, allowing the system to achieve high accuracy without requiring a single overly complex model.
Solution Approach 2:
The system adds a temporal dimension to the prediction architecture by executing models at different time scales. The first model operates on a long-term dimension (weeks to months), the second on a medium-term dimension (days to weeks), and the third on a short-term dimension (hours to days). This multi-dimensional approach allows accurate resource allocation predictions while keeping each individual model relatively simple.
2Reliability
If resource provisioning is adjusted frequently to meet demand changes, then service quality is improved, but system stability deteriorates
Solution Approach 1:
The system performs preliminary resource provisioning adjustments by executing prediction models that forecast demand weeks to months in advance. The first model predicts long-term resource requirements, allowing administrators to provision resources before peak demand occurs. This preventive approach ensures service quality is maintained while avoiding frequent reactive adjustments that would destabilize the system.
Solution Approach 2:
The system implements periodic resource adjustment cycles corresponding to different time horizons. The first model updates resources on a monthly or quarterly basis for long-term planning, the second model adjusts on a weekly basis for medium-term optimization, and the third model makes hourly or daily adjustments for short-term fluctuations. This structured periodic approach maintains service quality while providing system stability through predictable adjustment rhythms.
Data Source
AI summary
Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.