Network Management Data Collection via Distributed Clients
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Solution Overview
Problem
Network management data collection is resource-intensive, requiring high-end servers and frequent hardware upgrades due to the high processing capacity and memory usage of Network Management (NM) servers, which can be costly and inefficient.
Innovation Solution
Distributed network management data collection using underutilized computers within the network as Network Management Data Collection Clients (NMDCCs), where the NM server dynamically allocates compressed program files via web services to these clients for data collection, allowing idle resources to be utilized effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-end servers are used for network management data collection, then data collection capability is improved, but hardware cost and complexity increase
Solution Approach 1:
The patent divides the network management data collection function into multiple segments by deploying NMDCC clients across different network devices. Instead of concentrating all data collection on a single high-end server, the workload is segmented and distributed to multiple lower-cost devices throughout the network, reducing both hardware complexity and cost while maintaining data collection capability.
Solution Approach 2:
The patent implements multiple copies of the network management data collection function by deploying NMDCC clients on various network devices. Each client holds a copy of the necessary software components and independently performs data collection, eliminating the need for a single expensive server and reducing overall system complexity.
2Productivity
If high-end servers are used for network management data collection, then data collection capability is improved, but cost increases
Solution Approach 1:
The patent segments the data collection workload across multiple network devices, allowing the use of lower-cost hardware components distributed throughout the network rather than investing in expensive high-end servers. This segmentation approach maintains data collection capability while significantly reducing hardware investment requirements.
Solution Approach 2:
The patent employs inexpensive software components that can be dynamically deployed and removed from network devices. These lightweight, disposable software modules replace the need for expensive, permanent hardware investments, as the same functional capability can be achieved through multiple low-cost software instances on standard network devices.
3Adaptability or versatility
If hardware is upgraded for scalability and performance, then network management capability is improved, but cost and complexity increase
Solution Approach 1:
The patent introduces dynamic scalability by allowing the NMDCC client to be deployed on any network device regardless of its original hardware capabilities. The system dynamically adapts to different network environments by installing software components that provide the necessary scalability and performance features without requiring physical hardware upgrades, thus maintaining versatility while avoiding upgrade complexity.
Solution Approach 2:
The patent achieves scalability through parameter changes in the software architecture rather than hardware upgrades. By modifying software configuration parameters and deploying additional software instances on existing network devices, the system can scale its data collection capacity without changing the physical hardware, thereby avoiding the complexity and cost associated with hardware upgrades.
4Productivity
If multiple high-end servers are deployed for network management, then data collection capability is improved, but memory waste increases
Solution Approach 1:
The patent extracts the data collection functionality from centralized high-end servers and implements it as distributed NMDCC clients on individual network devices. This extraction eliminates the need for multiple expensive servers and their associated excess memory capacity, as each distributed client only allocates the minimum necessary memory resources for its specific data collection tasks, thereby reducing overall memory waste while maintaining data collection capability.
Data Source
AI summary
Methods and systems for network management data collection have been provided. Underutilized computers in the network are allocated as a Network Management Data Collection Client (NMDCC) to collect network management data. The NMDCC receives program files from the network management server. The received program files are downloaded by the NMDCC in a compressed manner via a web service. The downloaded program files are executed for collecting the Network Management Data (NMD). The collected NMD is updated back on the network management server. The downloaded program files are discarded when the NMDCC is not collecting the network management data.