I try and fit components together logically so that they can make the most of what the technology offers. I work predominantly in the OSS world on new access technologies like 5G and implementations like the Internet of Things. I want to achieve not just the deployment of these capabilities but to also to let them operate seamlessly. The following is my view of the opportunity of closed-loop remediation.
For closed-loop remediation there are two main tenets: 1. you can stream all network event data in a machine learning engine and apply an algorithm like K-Nearest Neighbour 2. you can expose remediation APIs on your programmable network.
All of this requires a lot of technology convergence but: What’s actually needed to make everything convergent?
Let’s start with Streaming. Traditionally we used SNMP for event data, traps & alarms and when that didn’t work we deployed physical network probes. Now it’s Kafka stream once implementations where a streams of logs of virtualised infrastructure and virtualised functions are parsed in a data streaming architecture into different big data persistence.
The Machine Learning engine, I’m keenest of FlinkML at the moment, works on the big data persistence providing the largest possible corpus of event data. The ML K-NN can analyse network behaviour and examine patterns that are harder for human operation teams to spot. It can also predict timed usage behaviours and scale the network accordingly.
I am increasingly looking at Openstack and Open Source Mano as a NFVO platform orchestrating available virtualised network functions. The NFVO can expose a customer facing service or underlying RFSs. But to truly operate the ML should have access to the RFS layer. This is the hardest part and is dependent upon the underlying design pattern implementation of the Virtual Network Functions. This though is a topic for another blog post.
Mobile Edge Computing (MEC) is a key piece of the 5G architecture (or 5G type claims on a 4G RAN). MEC can already make a huge difference in video latency and quality for video streaming multiple feeds within a sporting environment. For example Intel, Nokia and China Mobile video streams of the Grand Prix at Shanghai International Circuit.
A 5G mobile operator will be introducing virtualised network functions as well as mobile edge computing infrastructure. This creates both opportunities and challenges. The opportunities are the major MEC use cases included context-aware services, localised content and computation, low latency services, in-building use cases and venue revenue uplift.
The challenges include providing the Mobile Edge Compute Platform in a virtualised 5G world. Mobile operators are not normally IaaS / PaaS providers so this may become a challenge.
The ETSI 2018 group report Deployment of Mobile Edge Computing in an NFV environment describes an architecture based on a virtualised Mobile Edge Platform and a Mobile Edge Platform Manager (MEPM-V). The Mobile Edge Platform runs on NFVI managed by a VIM. This in turn hosts the MEC applications.
The ETSI architecture seems perfectly logical and reuses the NFVO and NFVI components familiar to all virtualisations. In this architecture the NFVO and MEPM-V act as what ETSI calls the Mobile Edge Application Orchestrator” (MEAO) for managing MEC applications. The MEAO uses NFVO for resource orchestration and for the element manager orchestration.
The difficulty still lies in implementing the appropriate technologies to suit the MEC use cases. Openstack (or others) may provide the NFVI and Open Source Mano (or others) may provide the NFVO; however what doesn’t exist is the service exposure, image management and software promotion necessary for a company to on-board MEC.
If MEC does take off what is the likelihood that AWS, GCP and Azure will extend their footprint into the telecom operators edge?
The Internet of Things requires multiple Reference Architectures which can map capabilities to specific technology domains. This is a challenge because there is no single unifying industry definition for the Internet of Things. For the purpose of this presentation it is assumed that:
- “Things” have semantic representation in the Internet
- “Things” can be acted upon in a structured manner (e.g., status, capabilities, location, measurements) or can report in structured data or can communicate directly with other “Things”
- “Things” may be active (e.g., Zigbee sensor) or passive (e.g. RFID tag)
- Different “Things” may use multiple protocols to communicate with each other and the internet
There are many different usable protocols for communication with M2M devices for the Internet of Things. Specific protocols are more appropriate for different devices (e.g. memory & power profiles) and specific protocols are more appropriate for different communication needs (e.g. State Transfer Model & Event Based Model)
The GSM Association (GSMA) put the M2M market size at $1.2tn revenue with 12 billion connected mobile devices by 2020. These numbers alone are enough to excite the most conservative of operators and wobble the subscriber-centric business models that currently prevail. The existing model adopted by MNOs that the more subscribers it has, the more successful and profitable it is considered to be, is about to be tested by this massive new market. This is mainly because the Average Revenue Per User (ARPU) in the M2M business is on average below ten cents per device, but on the other hand the connection density can be virtually endless. So success will depend on how dynamically the CSP reacts to provide new and flexible platforms to support the every-day new devices, applications and verticals that M2M will address.
Because of the low ARPU and massive market multiplier many MNOs should be prepared for a shake-up of their OSS which will have to fulfil and provision at bulk and at low cost.
IPv6 addressing will also make M2M services not just a mobile proposition, but applications that can work seamlessly across both mobile and wired broadband connections. eUICCs and wifi hand-off will have to be included in the new OSS. Furthermore Near Field Communication will require its own billing model.
Never before has a reference architecture been so required for M2M.
All of this does not just apply to the MNOs anymore.