Dynamic Service Chain Configuration via Big Data Analytics
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Solution Overview
Problem
Current network management systems face challenges in dynamically configuring network services to optimize traffic flow and service chaining based on real-time user behavior and network state data, leading to inefficiencies in service function placement and scalability.
Innovation Solution
An analytics-driven approach that utilizes a Big Data Analytics platform to generate dynamic situation profiles, determining similarity indices with characteristic profiles to trigger control signals for dynamic network design changes, enabling real-time optimization of service chains and function placement within virtualized infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static network configuration is used, then device complexity is reduced, but adaptability to changing network conditions deteriorates
Solution Approach 1:
The patent implements dynamic service chain configuration that automatically adapts to changing network conditions, user behavior patterns, and service requirements. The system transitions from static pre-configured service chains to dynamically generated configurations based on real-time analytics, allowing the network to respond flexibly to varying demands while maintaining manageable complexity through automated orchestration.
Solution Approach 2:
The system employs self-service mechanisms where the network automatically generates and configures service chains based on analytics-driven insights without requiring manual intervention. The automated service chain generator analyzes network state, user behavior, and service requirements to autonomously determine optimal service function placements and configurations, reducing operational complexity while enhancing adaptability.
2Productivity
If manual network configuration is used, then ease of operation is maintained, but productivity in optimizing service chains deteriorates
Solution Approach 1:
The patent replaces manual mechanical configuration processes with automated analytics-driven systems. Machine learning algorithms and data analytics platforms automatically analyze network performance, user behavior, and service requirements to generate optimized service chain configurations, eliminating the need for manual tuning while dramatically increasing optimization productivity and speed.
Solution Approach 2:
The system implements continuous feedback loops where network performance metrics, user behavior analytics, and service chain effectiveness data are constantly monitored and fed back to the service chain generator. This feedback mechanism enables automated iterative optimization, allowing the system to learn from past performance and continuously improve service chain configurations without manual intervention, thereby enhancing both productivity and ease of operation.
3Adaptability or versatility
If service chains are extended with more inline services, then functionality is improved, but loss of time in traffic processing increases
Solution Approach 1:
The system dynamically adjusts service chain parameters including the selection, ordering, and instantiation of service functions based on real-time analytics. By changing parameters such as service function placement, chain length, and resource allocation, the system optimizes the balance between providing comprehensive service functionality and minimizing traffic processing time, ensuring that only necessary services are included in each chain.
Solution Approach 2:
The patent segments service chains into modular service functions that can be independently selected, configured, and optimized. This segmentation allows the system to create customized service chains for different traffic types and requirements, including only the necessary service functions for each specific case, thereby reducing unnecessary processing steps and minimizing traffic processing time while maintaining required functionality.
4Adaptability or versatility
If more services are brought online, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements universal service chain generation mechanisms that can handle diverse service types and requirements through a single automated orchestration platform. The system provides multi-functionality by supporting various service functions (firewall, load balancing, caching, etc.) and can dynamically compose service chains for different scenarios using the same underlying infrastructure, thereby increasing service diversity without proportionally increasing infrastructure complexity.
Solution Approach 2:
The system introduces an automated service chain generator as an intermediary layer between the network infrastructure and service requirements. This intermediary automatically manages the complexity of configuring and orchestrating multiple services by translating high-level service requirements into optimized service chain configurations, allowing diverse services to be brought online while abstracting away the underlying complexity through automated mediation.
Data Source
AI summary
A system and method for dynamically (re)configuring a service network based on profile information obtained from a Big Data Analytics platform. Received dynamic situation profiles relative to network states, subscriber states, etc. may be compared against corresponding characteristic situation profiles. If there is a similarity, a dynamic design change action may be effectuated for changing configuration of at least a part of the service network, e.g., a service chaining mechanism, operating to service user data flows of the subscribers.