Deep Packet Inspection Workload Distribution for Variable Traffic
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Solution Overview
Problem
Analyzing data traffic using deep packet inspection techniques requires significant computing resources, especially during peak times, leading to inefficiencies and high costs due to the need for surplus resources to handle varying data volumes.
Innovation Solution
A system comprising multiple data analyzing units connected via a distribution unit that manages workload distribution and maintains analysis status, allowing seamless and non-redundant data analysis by redistributing work without losing the state of analysis, and dynamically generating or terminating units as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surplus computing resources are provided to handle peak data traffic, then data analysis reliability is improved, but system cost increases
Solution Approach 1:
The system dynamically adjusts the number of active data analyzing units based on the current data traffic volume. During peak times, more units are activated to maintain analysis reliability, while during low-traffic periods, units are deactivated to reduce resource consumption. This dynamic adaptation resolves the contradiction between maintaining reliability and reducing resource quantity.
Solution Approach 2:
The system changes the operational parameters of data analyzing units based on traffic conditions. By monitoring data volume and adjusting the number of active units accordingly, the system optimizes the balance between reliability and resource usage. The parameter being changed is the number of active analyzing units, which directly affects both reliability and resource consumption.
2Speed
If data analyzing units are kept idle to handle sudden traffic increases, then response speed is improved, but resource waste increases
Solution Approach 1:
The system maintains standby data analyzing units that can be quickly activated when traffic increases. These units are prepared in advance but not fully operational, allowing the system to respond rapidly to traffic spikes without permanently keeping resources idle. This preliminary preparation resolves the contradiction between response speed and resource waste.
Solution Approach 2:
The system dynamically transitions data analyzing units between idle and active states based on real-time traffic monitoring. This dynamic state change allows the system to have units ready for quick activation (improving response speed) while keeping them in a low-power idle state when not needed (reducing resource waste).
3Productivity
If workload is redistributed between data analyzing units, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism where data analyzing units report their workload status and the distribution unit monitors traffic patterns. Based on this feedback, the distribution unit intelligently redistributes workloads to optimize resource utilization. This automated feedback loop resolves the contradiction by handling the complexity of workload management through systematic monitoring and adjustment rather than manual intervention.
Solution Approach 2:
The distribution unit acts as an intermediary between data sources and data analyzing units, managing workload redistribution. This intermediary component handles the complexity of coordinating multiple units, tracking their status, and optimally assigning tasks, thereby improving resource utilization while containing system complexity within a dedicated management component.
4Measurement precision
If deep packet inspection is performed on all data packets, then analysis precision is improved, but computing resource consumption increases
Solution Approach 1:
The system applies deep packet inspection selectively rather than to all packets. The distribution unit directs only certain data packets to data analyzing units based on traffic patterns and unit availability, performing partial inspection that maintains precision for critical packets while reducing overall computing resource consumption.
Data Source
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AI summary
A system (12) for analyzing data traffic has two data analyzing units (18) and a distribution unit (16). The data analyzing units (18) perform deep packet inspection and transmit an analysis status (S) of the data analysis to the distribution unit (16). The distribution unit (16) stores the analysis status (S) and information on the analysis status (S) and sends the analysis status (S) or information on it to a data analyzing unit (18), which in turn distributes the workload of the data analysis. Further, a method for analyzing data traffic is shown.