Virtual Network Function Manager Self-Healing Design
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Solution Overview
Problem
As virtual networks grow in complexity and scale, managing failures and ensuring high availability and scalability becomes cumbersome, requiring significant user involvement for redeployment and redesign, which can be time-consuming and inefficient.
Innovation Solution
Implementing lifecycle management techniques that empower virtual network function managers (VNFMs) to autonomously update and manage the virtual network, allowing self-organization, self-healing, and self-scaling, reducing the need for user intervention by creating backup instances and dynamically adjusting the network design in response to failures and policy changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual network management is performed manually with user involvement for redeployment and redesign, then reliability can be maintained through careful oversight, but ease of operation deteriorates due to cumbersome procedures and time-consuming interventions
Solution Approach 1:
The system implements self-service through automated failure detection and recovery mechanisms. When a failure is detected in the virtual network, the system automatically triggers a redesign and redeployment process without requiring manual user intervention. The virtual network function managers autonomously coordinate the recovery actions, including selecting alternative data centers, redesigning the network topology, and redeploying virtual network functions, thereby resolving the contradiction between maintaining reliability and reducing operational complexity.
Solution Approach 2:
The system applies preliminary action by pre-configuring backup data centers and maintaining standby virtual network function instances. Before failures occur, the system establishes redundant infrastructure and prepares recovery configurations in advance. When failures are detected, these pre-prepared resources can be rapidly activated, enabling quick service restoration without time-consuming manual setup, thus improving both reliability and ease of operation.
2Productivity
If virtual network scales up to handle increasing demands, then productivity improves through enhanced computing resources, but device complexity worsens making design and management more cumbersome
Solution Approach 1:
The system implements dynamics by enabling the virtual network to automatically adapt its configuration based on demand fluctuations and failure conditions. The network topology, resource allocation, and data center selections are not static but dynamically adjusted through automated algorithms that respond to real-time system state. This dynamic behavior allows the network to scale efficiently while maintaining manageable complexity through rule-based automation rather than fixed complex designs.
Solution Approach 2:
The system applies universality by creating a multi-functional management framework where the same automated management mechanisms handle multiple tasks including failure detection, network redesign, resource allocation, and coordination across diverse data centers. This universal approach consolidates what would otherwise require separate specialized systems, reducing overall complexity while supporting scaled productivity through a single integrated management architecture.
3Reliability
If backup instances are created for high availability, then reliability improves through fault tolerance, but loss of substance worsens due to additional resource consumption
Solution Approach 1:
The system applies local quality by creating backup instances selectively rather than uniformly across all virtual network functions. The automated management system analyzes the criticality and failure risk of different network functions, deploying backup resources only where they provide the most value for improving reliability. This localized approach to redundancy optimizes the balance between service availability and resource consumption by concentrating backup capacity on high-priority functions.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting the number and distribution of backup instances based on system conditions, failure patterns, and resource availability. Rather than maintaining a fixed level of redundancy, the automated management system modifies backup parameters in response to changing demands and failure rates, optimizing the trade-off between reliability improvement and resource consumption over time.
Data Source
AI summary
This disclosure describes lifecycle management (LCM) techniques for improving high availability (HA) and scalability in a virtual network. The techniques include empowering virtual network function managers (VNFMs) to provide LCM to other VNFMs in the virtual network. For example, a VNFM instance in the virtual network may autonomously update and/or improve the virtual network design, such as by deploying additional VNFM instances. A VNFM network may be able to self-organize, such as by designating a primary cluster and/or autonomously holding an election. A VNFM instance may also heal and/or redeploy another VNFM instance. The present virtual network LCM techniques may allow a virtual network to be self-sustaining while providing HA.