Self-Optimizing Networks With Predictive Resource Scaling
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Solution Overview
Problem
Existing network computing systems face inefficiencies due to varying resource demands, leading to system failures, energy wastage, and increased latency, as they either overload resources during high traffic or underutilize them during low traffic periods.
Innovation Solution
Implementing a self-optimizing network system that uses machine learning to predict resource needs based on historical data and real-time metrics, allowing for dynamic scaling of computing resources to match demand, thereby preventing failures and optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of computing resources are maintained to support network operations during high-traffic periods, then system reliability is improved, but energy consumption increases and resources are wasted during low-traffic periods
Solution Approach 1:
The patent implements dynamic scaling of computing resources by training a machine-learning model to predict future network traffic patterns and proactively adjusting the number of virtual machines deployed. This allows the system to transition from static resource allocation to dynamic adaptation, scaling resources up before traffic peaks and scaling down after peaks subside, thereby maintaining reliability while reducing energy waste during low-traffic periods
Solution Approach 2:
The system performs preliminary action by using the machine-learning model to predict future network traffic demands and proactively provisioning computing resources before actual traffic peaks occur. This preemptive approach ensures resources are available when needed (maintaining reliability) while avoiding the continuous operation of excess resources that would waste energy during low-traffic periods
2Loss of energy
If computing resources are scaled down during low-traffic periods, then energy wastage is reduced, but system failures may occur when traffic increases
Solution Approach 1:
The machine-learning model performs preliminary analysis of traffic patterns and predicts future demand peaks, enabling the system to scale resources down during low-traffic periods while proactively scaling up before predicted peaks occur. This predictive approach prevents system failures by ensuring resources are available when needed, while still allowing energy savings during low-traffic periods
Solution Approach 2:
The system implements feedback mechanisms where the machine-learning model continuously learns from actual traffic patterns and system performance data, refining its predictions and adjusting resource allocation decisions. This feedback loop ensures that resource scaling decisions maintain system reliability while optimizing energy usage based on learned patterns
3Reliability
If redundant computing resources are maintained to prevent system failures, then reliability is improved, but response latency increases due to resource overhead
Solution Approach 1:
The patent replaces static redundant resource allocation with dynamic resource provisioning based on machine-learning predictions. Instead of maintaining fixed overhead resources, the system scales computing resources dynamically according to predicted demand, eliminating the need for permanent redundant resources that cause latency while ensuring availability when needed
4Productivity
If the number of virtual machines is increased during high-traffic periods, then network performance is maintained, but resource allocation efficiency decreases
Solution Approach 1:
The machine-learning model performs preliminary prediction of traffic peaks and enables proactive resource provisioning, allowing the system to scale virtual machines up before demand increases and scale them down after peaks subside. This ensures network performance is maintained during high-traffic periods while avoiding the continuous operation of excess resources that would reduce allocation efficiency and waste energy
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-optimizing networks. In some implementations, a method for self-optimizing networks includes obtaining information indicating performance metrics of a first set of computing resources of a distributed system; generating information representing usage of a wireless network at a first point in time; providing the information to a machine learning model trained to predict network events at a time subsequent to the first point in time; determining that at least one particular network event predicted in the output is addressable by using a second set of computing resources of the distributed system; transmitting a signal configured to adjust the distributed computing system to deploy the second set of computing resources.