Auto-scaling Software-defined Monitoring Platform for SDN Service Assurance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud-based software-defined networking (SDN) services face management traffic storms due to real-time, dynamic changes and rapid growth, overwhelming traditional static event monitoring systems, which are unable to adapt to the unpredictable and continuous traffic patterns.
Innovation Solution
An auto-scaling software-defined monitoring (SDM) platform that uses an SDM controller to monitor event data, measure quality of service (QoS) performance metrics, and determine when to perform auto-scaling operations, such as adding virtual machine capacity or migrating virtual machines, to maintain acceptable network event processing throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static event monitoring systems are used, then system simplicity is maintained, but the systems cannot adapt to dynamic SDN traffic patterns causing throughput degradation
Solution Approach 1:
The monitoring system transitions from a static configuration to a dynamic one by implementing auto-scaling capabilities. Virtual machine instances can be automatically instantiated, migrated, or terminated based on real-time QoS performance metrics and throughput thresholds, allowing the system to adapt its resource allocation to matching traffic patterns without manual intervention
Solution Approach 2:
The monitoring system performs self-service through automated decision-making based on measured QoS metrics. When throughput falls below a threshold, the system automatically triggers scaling operations (instantiating new virtual machines or migrating existing ones) without requiring external control, enabling the system to serve itself and maintain performance autonomously
2Productivity
If monitoring resources are increased to handle traffic storms, then network event processing throughput is maintained, but resource overhead increases
Solution Approach 1:
The system dynamically adjusts the quantity of monitoring resources (virtual machine instances) based on real-time throughput measurements. Resources are scaled up when throughput degradation is detected and scaled down when performance recovers, ensuring adequate processing capacity while minimizing resource overhead during normal operation
Solution Approach 2:
The system changes operational parameters (number of virtual machine instances) in response to measured QoS metrics. By monitoring throughput and comparing it against thresholds, the system adjusts resource parameters dynamically, maintaining productivity while optimizing resource consumption based on actual workload demands
3Adaptability or versatility
If static monitoring capacity is allocated, then resource management is simple, but the system becomes obsolete under unpredictable SDN growth patterns
Solution Approach 1:
The system achieves self-service automation by automatically measuring QoS metrics, comparing throughput against thresholds, and executing scaling decisions without human intervention. This automation enables the system to respond to unpredictable SDN growth patterns autonomously, maintaining adaptability while reducing operational complexity
Solution Approach 2:
The system implements feedback loops where QoS performance metrics are continuously measured and fed back to the control logic. This feedback mechanism enables automatic adjustment of monitoring resources based on actual system performance, allowing the system to adapt to unpredictable growth while maintaining manageable automation through closed-loop control
Data Source
AI summary
Concepts and technologies disclosed herein are directed to an auto-scaling software-defined monitoring (“SDM”) platform for software-defined networking (“SDN”) service assurance. According to one aspect of the concepts and technologies disclosed herein, an SDM controller can monitor event data associated with a network event that occurred within a virtualized IP SDN network that is monitored by a virtualized SDM resources platform. The SDM controller can measure, based upon the event data, a quality of service (“QoS”) performance metric associated with the virtualized SDM resource platform. The SDN controller can determine, based upon the QoS performance metric, whether an auto-scaling operation is to be performed. The auto-scaling operation can include reconfiguring the virtualized SDM resources platform by adding virtual machine capacity for supporting event management tasks either by instantiating a new virtual machine or by migrating an existing virtual machine to a new hardware host.