Dynamic Sampling Policy for Network Visibility Appliances
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network visibility technologies rely on static sampling policies, which are inefficient as they do not adapt to changes in network traffic, leading to suboptimal utilization of monitoring tools and requiring significant manual intervention.
Innovation Solution
A network visibility appliance dynamically determines a data traffic sampling policy based on real-time network traffic characteristics, such as throughput and metadata, to optimize tool usage and prevent performance constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static sampling policy is used to prevent exceeding tool throughput capacity, then tool performance constraints are avoided, but tool capacity utilization becomes inefficient during lower traffic periods
Solution Approach 1:
The patent implements dynamic sampling policies that automatically adjust sampling rates based on real-time network traffic conditions and tool capacity status. The system transitions from static conservative sampling to adaptive sampling that increases rates during low traffic periods and decreases rates during high traffic periods, thereby simultaneously improving tool utilization while maintaining performance constraint compliance.
Solution Approach 2:
The system employs feedback mechanisms where the network visibility appliance continuously monitors tool throughput capacity and network traffic patterns, then uses this information to dynamically adjust sampling policies. This closed-loop control enables the system to respond to changing conditions and optimize tool utilization while preventing capacity overload.
2Productivity
If manual sampling policy updates are performed to optimize tool usage, then tool capacity utilization can be improved, but significant time and effort are required from network administrators
Solution Approach 1:
The patent enables the sampling policy system to self-adjust and self-optimize automatically without requiring manual intervention from network administrators. The network visibility appliance autonomously monitors traffic patterns, evaluates tool capacity, and modifies sampling policies in real-time, eliminating the need for manual policy updates while maintaining optimal tool utilization.
Solution Approach 2:
Through continuous feedback loops, the system automatically detects when policy optimization is needed and executes adjustments without human involvement. This eliminates the time and effort previously required from administrators to manually analyze traffic patterns and update policies.
3Reliability
If conservative sampling policies are defined to avoid exceeding maximum throughput, then tool performance constraints are maintained, but significant tool capacity remains unused during lower network traffic
Solution Approach 1:
The system transforms static conservative sampling policies into dynamic adaptive policies that automatically adjust sampling rates based on current network conditions. During low traffic periods, the system increases sampling rates to utilize available tool capacity, while during high traffic periods, it decreases rates to maintain throughput constraint compliance, thereby achieving both reliability and adaptability.
Data Source
AI summary
A network visibility appliance automatically and dynamically determines a data traffic sampling policy that it should apply, i.e., a policy for determining which flows the network appliance should forward to one or more tools. The technique can be used to adjust for changes in network traffic to avoid exceeding performance constraints (e.g., maximum throughput) of network analytic tools, while maintaining high efficiency of usage of the tools. In the technique, a policy engine monitors network traffic characteristics in a subscriber throughput table and dynamically determines a sampling policy to apply, so as to decrease and/or increase traffic throughput to a given tool, so that the tool is efficiently used.