Predictive ML Model for Dynamic Cloud Bandwidth Scheduling
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Solution Overview
Problem
Current Quality of Service (QoS) management in cloud environments during software application migration lacks dynamic adaptability to rapidly changing bandwidth conditions, leading to potential SLA breaches and negative impacts on business continuity.
Innovation Solution
A predictive machine learning model is trained on historic network usage and business context data to prioritize software application activities based on predicted resource requirements and contextual scenarios, ensuring optimal scheduling and resource allocation to meet SLA targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional QoS management is used during cloud migration, then system complexity remains low, but the system cannot dynamically adapt to rapidly changing bandwidth conditions leading to SLA breaches
Solution Approach 1:
The patent implements dynamic QoS management by continuously monitoring network bandwidth conditions and automatically adjusting priority rankings of software application activities in real-time. The system transitions from static to dynamic resource allocation, allowing the cloud environment to adapt to rapidly changing bandwidth conditions during migration operations, thereby preventing SLA breaches while maintaining operational flexibility.
Solution Approach 2:
The patent applies preliminary action by predicting future bandwidth requirements and potential SLA breaches before they occur. The system uses historical data and current trends to forecast resource needs, allowing administrators to proactively adjust priority rankings and resource allocation before bandwidth conditions deteriorate, thus preventing SLA violations rather than reacting to them after occurrence.
2Reliability
If static priority ranking is used for software application activities, then scheduling is simple, but SLA targets cannot be met during bandwidth fluctuations
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring network bandwidth usage, application performance metrics, and SLA compliance status. This real-time feedback loop allows the system to dynamically adjust priority rankings of software application activities based on current conditions, ensuring SLA targets are met during bandwidth fluctuations while maintaining automated control to manage scheduling complexity.
Solution Approach 2:
The patent applies parameter changes by dynamically modifying priority rankings and resource allocation parameters based on real-time bandwidth conditions and predicted future states. The system adjusts these parameters automatically in response to changing network conditions, allowing flexible adaptation to maintain SLA compliance without requiring complex manual scheduling interventions.
3Productivity
If manual resource allocation is used during migration, then automation level is low, but rapid response to bandwidth changes is impossible
Solution Approach 1:
The patent implements self-service automation by enabling the QoS management system to automatically monitor, predict, and adjust resource allocation without manual intervention. The system autonomously responds to bandwidth changes by dynamically reprioritizing software application activities, achieving rapid response times during cloud migration while maintaining high levels of automation to handle the complexity of real-time resource management.
4Reliability
If bandwidth is allocated without prediction, then resource allocation is straightforward, but SLA breaches occur during high-demand periods
Solution Approach 1:
The patent applies preliminary action by using predictive analytics to forecast future bandwidth requirements and potential SLA breaches before they occur. The system analyzes historical data and current trends to predict resource needs during high-demand periods, allowing proactive adjustment of priority rankings and resource allocation to prevent SLA violations before bandwidth conditions deteriorate.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring actual bandwidth usage against predicted requirements and SLA thresholds. This real-time feedback allows the system to detect deviations from expected performance and automatically adjust resource allocation to maintain SLA compliance during high-demand periods, balancing predictive capabilities with adaptive response.
Data Source
AI summary
A system and method for bandwidth management are provided. In embodiments, a method includes: training, by a computing device, a predictive machine learning (ML) model based on historic network usage data of software applications in a cloud environment and historic business context data; assigning, by the computing device, priority rankings to software application activities of the cloud environment using the predictive ML model based on predicted resource requirements for the software application activities of the cloud environment and predicted contextual scenarios that impact the predicted resource requirements using an input of real-time network usage data of the cloud environment and real-time business context data; and initiating, by the computing device, scheduling of the software application activities based on the priority rankings.