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A New Approach to Network Modeling and Management

By Special Guest
Dr. Stefan Dietrich, Vice President of Product Strategy, Glue Networks Inc.
March 28, 2016

Today’s businesses are dependent on applications that demand ever-changing bandwidth and latency requirements from the underlying network. This is particularly true for wide area networks (“WANs”) that: (i) are geographically diverse; (ii) use a plethora of technologies from different services providers; and (iii) are under strain from increasing use of video and cloud application services. Hybrid WAN architectures with advanced application level traffic routing are of particular interest. They combine the reliability of private lines for critical business applications with the cost-effectiveness of broadband/Internet connectivity for non-critical traffic.

Most of today’s network management tools are insufficient to deploy such architectures at scale over the existing network. Most of them still apply blocks of configuration data to network devices to enable features that in turn enable an overall network policy. To allow adjustment of configuration data to address differences in hardware and OS/firmware levels, those scripts are using “wildcards” replacing certain configuration data. These scripts are heavily tested, carefully curated and subject to stringent change management procedures. The tiniest mistake can bring a network down, resulting in potentially disastrous business losses.

With the deployment of hybrid WAN architectures and application-specific routing, network operations teams are experiencing the limits to this approach. Even if the existing hardware already supports all the functionality required, existing network configurations that reflect past user requirements are rarely well understood. As each business unit is asking for specific requirements to ensure that their applications run optimally on the network, networks need to be continuously updated and optimized. Such tasks range from a simple adjustment of the configuration parameters to more complex changes of the underlying network architecture, such as removing and installing upgraded circuits, replacing hardware or even deploying new network architectures.

For said tasks, significant involvement by senior network architects is required to determine potential risk of unintentional consequences on the existing network, but waiting for the next change maintenance window may no longer be an acceptable option. Businesses are not concerned with the details; they want the networks to simply “work.”

What’s Next?

The question is where to go from here. Traditional network management tools are mature and well understood. Network architects and implementation teams are familiar with them, including all of the limitations and difficulties, and any potential change of these tools is immediately vetted against the additional learning curve required vis-à-vis potential benefits in managing the network.

Ideally, network policies should be defined independently of implementation or operational concerns. It starts with mapping of the required functionality into a logical model, assembling these models into one overall network policy, verifying interdependencies and inconsistencies, and deploying and maintaining them consistently throughout the network life cycle.

In reality, while the industry has launched a variety of activities to improve network management, those initiatives are still maturing. For example, YANG is a data modeling language for the NETCONF network configuration protocol. OpenStack Networking (Neutron) is providing an extensible framework to manage networks and IP addresses within the larger realm of cloud computing, focusing on network services such as intrusion detection systems (IDS), load balancing, firewalls and virtual private networks (VPN) to enable multi-tenancy and massive scalability. But neither approach can pro-actively detect interdependencies or inconsistencies, and both require network engineers to dive into programming, for example, to manage data entry and storage.

As a result, some vendors are offering fully integrated solutions, built on appliances managed through a proprietary network management tool. This approach allows businesses to deploy solutions quickly, at the cost of additional training, limited capability for customization and new hardware purchases.  

To achieve a breakthrough, compelling new network management capabilities have to be focused on assembling complete network policies from individual device-specific features, detecting inconsistencies and dependencies, and allowing deployment and ongoing network management. Simply updating wildcards in custom configuration templates and deploying them onto devices is no longer sufficient.

In the future, network architectures or routing protocol changes may need to be changed on live production networks. Managing such changes at large scale is difficult or even unfeasible. Especially in large organizations where any change will always have to be validated by e.g. security. This creates unacceptable delays for implementation.

Abstracting Networks to Model Features and Policies

To develop an end-to-end approach on how to create complete network policies from abstract device-based network features, including interdependency checking, deployment and secure lifecycle management, requires taking a step back. Network features and related policies can be mapped using these four constructs:

  • Features – Provide the configuration settings for a single device at a time, enabling functionality that the device can provide by itself. A good example is the configuration of a device-specific routing table where the device should forward incoming traffic.
  • Domains – Configuration settings should consistently be applied across multiple devices. A good example is a QoS configuration which may be different by business units, hence, different QoS domains would allow network engineers to assign QoS policies across all devices associated with specific business units in each region.
  • Globals – These are configuration settings that apply throughout the network and are the same for every device in the network. A good example is NTP (network time protocol) where the central architecture team is defining the only NTP servers permissible for the network.
  • Custom – Not everything may be practical to model in a general feature or domain concepts, especially specific exceptions to single devices only – for example, a specific set of Access Control Lists (ACLs) needed on a single device. For these cases where no other dependencies with other features exists, just applying configuration data to a device may be acceptable. 

Using a combination of these constructs, any network policy can be built. Inherent interdependencies can be flagged by network engineers early, so that a network management system can deploy them in the correct sequential order, optimally applying these features to individual devices as well as across the network to create the target policy. Abstracting network functionality into these types of models allows network engineers to re-focus on the actual network architecture and focus less on the mechanics of the management of configuration data. These lead to a number of inherent benefits:

  • Device anonymity: The configuration of a device is now a result of the functionality of how this device should perform, by itself or in concert with other devices. As a result, the actual hardware itself, its specific OS/firmware or even the manufacturer no longer matters, as long as the device is capable of performing the desired functionality.
  • Design, collaborate, validate: Network architecture and design (NetOps) is logically separated from implementation and maintenance (DevOps). For example, architects can define the features, domains and global settings needed for a given network infrastructure, assemble them into logical groups and resolve any interdependencies. They can then be tested and validated by e.g. the security team. The assembled features, domains and globals are handed over to the operational team, who will deploy them onto the network and manage them over their lifecycle.
  • Creating a network community: The modeling of networks through the logical constructs allows for a wide exchange of best-practice reference designs based on common user requirements. Different teams of architects can exchange information about the models they use for specific network functionalities without having to revert to low-level configuration settings. This opens the possibilities of creating network engineering communities that exchange specific models based on their desired use cases with clearly defined interdependencies and conflict resolution against other models.

Control and Automate Your Network

To create a next-generation network management tool requires the development of a sophisticated network-aware orchestration engine that is able to detect any interdependencies, resolve them and deploy network policies automatically over the network.

To achieve this, a number of non-technical challenges have to be considered:

  • Network engineers are typically not programmers, nor do they aspire to be. Their primary focus is on proper device configurations and ensuring the device is performing as intended. Any next-generation tools have been designed with a network engineering focus in mind, allowing network engineers to use the system with a much shorter learning curve and minimal programming expertise.
  • User confidence and acceptance that the logical network model will indeed result in the correct configuration of all devices in the network. Many network engineers are still most comfortable with command line interface (“CLI”) created from scripts and templates.
  • Obtain buy-in from NetOps and DevOps teams, who may be skeptical to trust device configuration to a new management tool.

From a technical perspective, the next generation of management tools should include:

  • Zero-touch provisioning to make the onboarding of new devices into the system as fluid as possible, allowing generalist IT staff to install routers and trigger device provisioning automatically.
  • Functionality that limits or flags unauthorized manual device configuration changes with automatic remediation when needed.
  • Management for the high degree of customization needed.
  • Step-by-step verification of device provisioning actions with automatic revert on errors.
  • Configuration preview, allowing dry-runs of new configurations to understand all changes that may have to be performed, even on other network devices when needed.

Transform Your Network

Tools that provide complete abstraction of network functions while providing deeply integrated model interdependency verification, deployment previews and layer-by-layer provisioning are a good example of how enterprises can truly transform their networks. For example, replacing an existing device with a newer model, even if it’s from a different vendor, can be detected and automatically provisioned. Such solutions that can resolve any potential conflicts and interdependencies, even across vendors, are becoming increasingly important as network devices are virtualized on common platforms, and the individual strength of vendor-specific solutions are combined into one multi-vendor solution

This full-stack approach will allow better integration with clearly defined handoff points between architecture and implementation teams, leading to faster implementation of business requirements, higher reliability, elimination of configuration errors and faster identification and recovery from network outages.

About the Author: Dr. Stefan Dietrich brings to Glue Networks more than 20 years of experience defining innovative strategies and delivering complex technology solutions. Before joining Glue Networks, Stefan was Managing Director of Technology Strategy at AXA Technology Services, introducing advanced new technologies to AXA globally, and held senior IT management positions at Reuters and Deutsche Bank. Stefan received a Ph.D. in Aerospace Engineering and Computer Science from the University of Stuttgart and served as a Postdoctoral Fellow and faculty member at Cornell University.




Edited by Maurice Nagle


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