Network Tool Optimizer for Selective Cloud-Based Packet Analysis
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Solution Overview
Problem
Network administrators face challenges in efficiently monitoring and analyzing network packet traffic due to limited access and resource wastage, as they often purchase excess monitoring tools to cover peak loads, leading to idle resources during lower load conditions.
Innovation Solution
The implementation of network tool optimizer devices that utilize cloud-based virtual machine tool platforms to selectively scan and analyze network packet traffic, identifying subsets of interest and forwarding them to cloud-based servers for processing, allowing for remote analysis and dynamic resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entities purchase a wide variety of network tools to cover all potential analysis and threat discovery needs, then the network monitoring capability is improved, but resource wastage occurs when tools remain idle during lower load conditions
Solution Approach 1:
The patent implements dynamic resource allocation by transitioning network analysis tools from static local deployment to dynamic cloud-based virtual machine platforms. The system selectively scans network traffic, identifies subsets of interest, and dynamically provisions cloud-based analysis tools only when needed, allowing resources to be activated or deactivated based on real-time network conditions and threat levels.
Solution Approach 2:
The patent creates a universal cloud-based platform that can perform multiple network analysis functions through virtual machine tools. Instead of requiring separate dedicated tools for each analysis type, a single cloud platform provides multi-functional capability to handle traffic analysis, threat detection, and various network monitoring tasks, reducing the total number of tools needed.
2Productivity
If entities purchase enough tool bandwidth capacity to cover peak loads, then the network tool capacity is sufficient during high traffic periods, but processing capability goes unused during lower load conditions
Solution Approach 1:
The system implements dynamic bandwidth allocation by using cloud-based virtual machine platforms that can scale processing capacity up or down based on real-time network load. During peak traffic periods, additional cloud-based analysis capacity is provisioned to handle the increased load, while during lower load conditions, resources are released back to the cloud provider, eliminating the need to permanently purchase excess capacity.
Solution Approach 2:
The cloud-based platform provides self-service capacity management, automatically provisioning and deprovisioning analysis resources based on detected network conditions. The system monitors traffic patterns and dynamically adjusts the allocation of cloud-based virtual machine tools without requiring manual intervention or pre-purchasing of peak capacity.
3Loss of energy
If cloud-based virtual machine tool platforms are used for selective scanning, then resource efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces a network tool optimizer as an intermediary device that manages the complexity of coordinating between local network traffic sources and cloud-based virtual machine tools. This intermediary handles traffic selection, cloud platform communication, and result aggregation, shielding users from the underlying complexity while enabling resource-efficient cloud-based analysis.
4Reliability
If all network packet traffic is forwarded to analysis tools, then complete monitoring coverage is achieved, but processing overhead and resource consumption increase
Solution Approach 1:
The system extracts and forwards only the essential elements of network traffic—specifically, selected subsets of packets identified as being of interest—rather than forwarding complete traffic copies to all analysis tools. This selective extraction reduces processing overhead by focusing computational resources only on relevant traffic portions while maintaining monitoring coverage for critical events.
Solution Approach 2:
The system implements partial action by forwarding only necessary traffic subsets to cloud-based analysis tools rather than complete traffic streams. This approach provides sufficient monitoring coverage for security and analysis purposes while avoiding the excessive processing overhead of analyzing all network packets, achieving the right balance between coverage and resource consumption.
Data Source
AI summary
Network tool optimizer devices and related methods are disclosed that provide selective scanning of network packet traffic using cloud-based virtual machine tool platforms. Rather than require local network analysis tool resources, the disclosed embodiments identify subsets of packet traffic of interest, and these subsets are forwarded to a cloud-based server system where cloud-based virtual machine tool platforms are used to process the subsets of traffic of interest. Results from this processing are then provided back to adjust the operation of the network tool optimizers. Some further embodiments use local capture buffers and remote cloud replay buffers to stored subsets of traffic locally for later communication to cloud server systems where cloud-based tools analyze replays of the captured network traffic. Some further embodiments also use results from cloud-based tools to initiate local virtual machine tool platforms that are used to further analyze traffic of interest.