MEF17: LSO APIs, SD-WANs and automation

I attended last week’s MEF17 conference in Orlando, where the telecom industry association announced its MEF 3.0 Transformational Global Services Frameworkfor defining, delivering, and certifying agile, assured, and orchestrated communication services across a global ecosystem of automated networks”. MEF members have been busy defining a set of APIs for Lifecycle Services Orchestration(LSO), with a strong focus on inter-carrier orchestration for delivering end-to-end services across multiple domains and these were discussed in many conference sessions and demonstrated by vendors and service providers in the Proof of Concept Showcase.

It was no surprise to hear everyone talking about delivering SD-WAN and other virtualized services, but because these are largely software-driven constructs, it’s critical that the industry adopt open standards in order to support multi-vendor, multi-carrier deployments. This is where the MEF plans to take a leading role, using its base of standards for inter-carrier Ethernet services as a springboard. Here’s a link to the MEF 3.0 video that describes the association’s global services framework, which encompasses not only service definitions and APIs but also an automated, self-service certification platform and a broad community of vendors, service providers, open source projects and other standards bodies.

Automation was the other hot topic at the event. In his keynote, MEF CTO Pascal Menezes stressed the importance of telemetry-driven analytics and machine learning (“AI analytics”) for automating service orchestration at the connectivity layer and for virtualized, overlay services. He also talked about using visibility and analytics to make networks application aware for intent-based service orchestration.

I am keen to track the MEF’s progress in 2018 as it works to define APIs that facilitate automation at both the service and network layers and I’m hoping we’ll see tangible results in this area prior to next year’s MEF18 event.

 

Four key building blocks for insight-driven network automation

The migration of content, applications and services to the cloud is driving network  behaviors characterized by constantly shifting traffic flows, complex end-to-end paths and unpredictable bandwidth demand. Small, agile DevOps teams are engaged in cloud-native application and service deployments that are highly dynamic, distributed and diverse. These trends are creating serious operational challenges for both cloud-native webscalers and network service providers (NSPs), which are exacerbated by the dramatic expansion of the potential attack surface beyond the traditional security perimeter. In addition, a proliferation of weakly secured IoT devices has created a platform that hackers are exploiting to launch massive scale botnet-based DDoS attacks.

My colleague Tim Doiron and I just published an ACG Research white paper on “Powering Intelligent Network Services With Real-Time Visibility, Analytics and Automation” that describes how NSPs and webscalers can use real-time visibility, analytics and automation to overcome these challenges by taking advantage of the latest advances in network infrastructure hardware and software. The paper examines the four key building blocks that enable operators to realize the benefits of real-time, insight-driven network automation:

The data plane should be fully instrumented in both hardware and software, capable of extracting visibility data used for tracking, identifying and characterizing traffic flows. Data plane fabrics based on custom ASICs will continue to play a vital role by providing embedded support for packet flow classification mechanisms, flexible packet header and payload pattern matching for filtering functions, built-in flow replication and the ability to support millions of granular ACL rules.

The control plane should be fully programmable and evolve to incorporate a sophisticated orchestration layer implementing multiple applications for real-time network automation use cases in response to detected failures, anomalies, performance bottlenecks, sub-optimal utilization and security threats.

Multi-domain, multi-layer visibility is critical for dynamic traffic flows traversing complex end-to-end paths while the underlying network topology shifts in response to changing conditions. Another critical visibility requirement is identifying servers and end points not just by IP address, but by application, service or subscriber, which is a non-trivial problem in today’s vast and complex Internet. NSPs and webscalers also need a map of the “supply chain of the Internet”, which tracks the relationships and dependencies between cloud-native applications and services, CDNs and peering and transit networks.

Big Data analytics plays the critical role of extracting actionable insights in real-time by ingesting petabytes of data, including streaming telemetry for visibility into network paths, traffic flows and performance, but also data collected from a wide array of other sources that provides visibility into the identity of applications, services and subscribers.

In combination, these four key building blocks enable operators to deploy intelligent networks that use visibility and analytics to drive closed-loop feedback for insight-driven network automation:

To learn more, read the white paper and tune into the upcoming Light Reading webinar “Real-Time Visibility & Analytics to Enable Actionable Intelligence & Network Automation“, to be held Thursday, November 9, 2017, 12:00 p.m. New York / 6:00 p.m. London.

 

 

Real-time network visibility & analytics for operational intelligence

When I launched my network analytics practice for ACG just over a year ago, I decided that my initial research needed to focus on the value of real-time network visibility and Big Data analytics for operational intelligence. SDN, virtualization and the widespread adoption of cloud-scale technologies are enabling new techniques, including streaming telemetry, for instrumenting networks and gaining real-time visibility into traffic flows and network state. At the same time, streaming analytics allows network operators to immediately turn insights into action within seconds or minutes instead of hours or days. Big Data also supports the large-scale data sets needed to apply machine learning techniques for predictive analytics and AI-based network automation.

The ROI for real-time operational intelligence is compelling across a wide array of use cases, including: rapid root cause analysis and reduced mean time to repair (MTTR); immediate detection of security threats inside the network perimeter; real-time performance monitoring for on-the-fly traffic engineering; continuous KPI monitoring for service assurance; and the holy grail: closed-loop feedback for analytics-driven automation. The potential gains are huge and the industry is witnessing a new wave of innovation that will enable us to reinvent how networks are deployed and operated, and how services are delivered and managed.

Network operators are leveraging new real-time visibility and analytics technologies in three separate, but interconnected, domains:

  • Telecom network and communication services
  • Cloud-scale services delivered via the Internet
  • Hyperscale data center infrastructure

Therefore, my research in operational intelligence has separate tracks for covering developments in each domain, although there is overlap between tracks. For example, new telecom services are being delivered via the cloud, and SD-WANs are telecom services that use the Internet to connect users to applications in the cloud. The cloud-scale services track looks at visibility and analytics from the perspective of the network operator who is delivering or consuming services over networks that the operator doesn’t own or operate, whereas the hyperscale data center track looks at the role of visibility and analytics to manage that infrastructure, which is used for delivering cloud-scale services.

As a result, my research spans three separate, but interrelated, markets:

  • Telecom services
  • Cloud-scale services
  • Enterprise IT

While today these are three distinct markets, over the next decade I expect the lines will blur as the industry converges on delivering the majority of applications and services via public and hybrid clouds. Picture one vast market – cloud-scale services – segmented by application type: consumer, enterprise IT, communications, IoT, etc. At this point, the network simply provides access and transport for user devices, machines and sensors to connect with applications running in the cloud.

As an industry, we need to solve many technical problems in order to get there, with security being the most significant challenge, but today’s breakthroughs in real-time network visibility and Big Data analytics will play a key role in realizing this vision.