copyright by www.itproportal.com
A network that can fix and optimize itself without human intervention could become a reality soon – but not without some training.
A network that can fix and optimize itself without human intervention could become a reality soon – but not without some training. With the help of machine learning and artificial intelligence, software-defined networks can learn to help with network management by using operational data. Initial application of AI to WAN operations includes security functions such as DDoS attack mitigation as well as near real-time, automated path selection, and eventually AI-defined network topologies and basic operations essentially running on ‘auto-pilot’.
Enhancing IT operations with artificial intelligence (AI), including configuration management, patching, and debugging and root cause analysis (RCA) is an area of significant promise – enough so that Gartner has defined the emerging market as “AIOps”. These platforms use big data and machine learning to enhance a broad range of IT operations processes, including availability and performance monitoring, event correlation and analysis, IT service management, and automation (Gartner “Market Guide for AIOps platforms,” August 2017).
Gartner estimates that by 2022, 40 percent of all large enterprises will combine big data and machine learning functionality to support and partially replace monitoring, service desk and automation processes and tasks, up from five percent today.
Limits of automation and policy for NetOps
Given the traditional split between APM (application performance management) and NPM (network performance management), even the best network management tools aren’t always going to help trace the root cause of every application and service interruption. There can be interactions between network and application that give rise to an issue, or a router configuration and issue with a service provider that’s impacting application performance.
Network operations personnel might respond to an incident by setting policies in the APM or NPM systems that will alert us when an unwanted event is going to happen again. The issue with policy-based management is that it is backwards looking. That’s because historical data is used to create into policies that should prevent something from happening again. Yet, policy is prescriptive; it doesn’t deal with unanticipated conditions. Furthermore, changes in business goals again more human intervention if there isn’t a matching rule or pre-defined action.
On the whole, SD-WAN services represent an improvement over management of MPLS networks. Still, the use of an SD-WAN isn’t without its own challenges. Depending on the number of locations that have to be linked, there can be some complexity in managing virtual network overlays. The use of on-demand cloud services adds another layer of complexity. Without sufficient monitoring tools, problems can escalate and result in downtime. At the same time, adding people means adding cost, and potentially losing some of the cost efficiencies of SD-WAN services.
AI is way forward for SD-WAN management
What would AIOps bring to SD-WAN management?
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Starting with a programmable SD-WAN architecture is an important first step towards a vision of autonomous networking. Programmable in this case means API-driven, but the system also needs to leverage data from the application performance and security stack as well as the network infrastructure as inputs into the system so that we can move from simple alerting to intelligence that enables self-healing, managing and optimization with minimal human intervention.
Monitoring all elements in the system in real time (or at least near real time) will require storing and analyzing huge amounts of data. On the hardware side, cloud IaaS services have made that possible. Acting on the information will require artificial intelligence in the form of machine learning.
Use Cases for AI in SD-WAN
There are a variety of ways to apply machine learning algorithms to large datasets from supervised to unsupervised (and points in between) with the result being applications in areas such as:
copyright by www.itproportal.com
A network that can fix and optimize itself without human intervention could become a reality soon – but not without some training. With the help of machine learning and artificial intelligence, software-defined networks can learn to help with network management by using operational data. Initial application of AI to WAN operations includes security functions such as DDoS attack mitigation as well as near real-time, automated path selection, and eventually AI-defined network topologies and basic operations essentially running on ‘auto-pilot’.
Enhancing IT operations with artificial intelligence (AI), including configuration management, patching, and debugging and root cause analysis (RCA) is an area of significant promise – enough so that Gartner has defined the emerging market as “AIOps”. These platforms use big data and machine learning to enhance a broad range of IT operations processes, including availability and performance monitoring, event correlation and analysis, IT service management, and automation (Gartner “Market Guide for AIOps platforms,” August 2017).
Gartner estimates that by 2022, 40 percent of all large enterprises will combine big data and machine learning functionality to support and partially replace monitoring, service desk and automation processes and tasks, up from five percent today.
Limits of automation and policy for NetOps
Given the traditional split between APM (application performance management) and NPM (network performance management), even the best network management tools aren’t always going to help trace the root cause of every application and service interruption. There can be interactions between network and application that give rise to an issue, or a router configuration and issue with a service provider that’s impacting application performance.
Network operations personnel might respond to an incident by setting policies in the APM or NPM systems that will alert us when an unwanted event is going to happen again. The issue with policy-based management is that it is backwards looking. That’s because historical data is used to create into policies that should prevent something from happening again. Yet, policy is prescriptive; it doesn’t deal with unanticipated conditions. Furthermore, changes in business goals again more human intervention if there isn’t a matching rule or pre-defined action.
On the whole, SD-WAN services represent an improvement over management of MPLS networks. Still, the use of an SD-WAN isn’t without its own challenges. Depending on the number of locations that have to be linked, there can be some complexity in managing virtual network overlays. The use of on-demand cloud services adds another layer of complexity. Without sufficient monitoring tools, problems can escalate and result in downtime. At the same time, adding people means adding cost, and potentially losing some of the cost efficiencies of SD-WAN services.
AI is way forward for SD-WAN management
What would AIOps bring to SD-WAN management?
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Starting with a programmable SD-WAN architecture is an important first step towards a vision of autonomous networking. Programmable in this case means API-driven, but the system also needs to leverage data from the application performance and security stack as well as the network infrastructure as inputs into the system so that we can move from simple alerting to intelligence that enables self-healing, managing and optimization with minimal human intervention.
Monitoring all elements in the system in real time (or at least near real time) will require storing and analyzing huge amounts of data. On the hardware side, cloud IaaS services have made that possible. Acting on the information will require artificial intelligence in the form of machine learning.
Use Cases for AI in SD-WAN
There are a variety of ways to apply machine learning algorithms to large datasets from supervised to unsupervised (and points in between) with the result being applications in areas such as:
Enhancing performance of applications over the internet network with optimized route selection.Looking more closely at security as a use case, how would AI and ML be able to augment security of SD-WANs? While the majority of enterprises are still trying to secure their networks with on-premise firewalls and DDoS mitigation appliances, they are also facing attacks that are bigger and more sophisticated. According to statistics gathered by Verisign last year:
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