12 Reasons Why Cloud OSS hasn’t happened so far

I am regularly asked why there are so few Cloud OSS, or OSS as a Service, options when AWS / GCP and Azure all have IoT plays. I have also wondered why no systems integrator has deployed ONAP on AWS (or other). The following are the main reasons why I think such an option has not yet become popular for CSPs & vendors.

12 Reasons Why Cloud OSS hasn’t happened so far:

  1. Network operators are risk averse
    • That’s a very good thing as CSPs protect your data in flight and at rest. Security is critical for CSPs. However, this does not mean that a Cloud OSS cannot be used just that the appropriate security measures need to be in place
  2. Network operators have customers that are even more risk averse
    • That’s a very good thing too and CSPs have to take account of their customers requirements. However, a private cloud or a public cloud can be secured in the same way as a private data centre. The OSS must make sure that it is not persisting customer data or exposing network functions.
  3. Cloud OSS creates another attack vector and dude we’ve got enough of those
    • We sure do. But internally hosted OSS is itself a risk / attack vector. The benefit of Cloud OSS is that it should allow a simplification / reduction of the number of OSS stacks within the CSP
  4. OSS must be internal because of Data regulation and on-shoring / safe-harbouring of data
    • OSS systems should not be persisting customer data (EVER even Static IP addresses!). So, data regulation requirements will only have limited application. OSS data must be secured at rest and in transit. The low latency requirements of OSS will require near hosting.
  5. Few network operators have sufficient levels of virtualised network functions
    • This is changing rapidly and 5G technologies will be predominantly virtualised
  6. The cost of the OSS is always a low proportion of the costs of the network
    • This is true but does not stop the need to gain greater platform efficiencies.
  7. Moving to the cloud will not wipe away the legacy
    • Of course, it won’t but it will help focus of the future and pass management of VNFs to a single master. PNF management will always be a challenge.
  8. The OPEX model is not always beneficial
    • This is true but OSS stovepipes are not cheap. Best of breed SaaS will help spread the cost and not create a lock in to a single technology version.
  9. It’s the OSS, those guys don’t move quickly
    • A classic refrain but not a reason not to move to a Cloud OSS
  10. The streaming data pipe will be too fat and the latency will be too slow to fix items quickly
    • This is a genuine concern and will required a data pipeline architecture with streaming inside the network and OSS components residing outside. Intent based programming with specific levels of management at the different layers will be key to answering the low latency requirement. Especially when control is part of a network slice management function.
  11. The BSS will never be in the Cloud
    • Salesforce, GCP, AWS, Pega, Oracle Cloud, Azure are all changing that model. Especially in the IoT space.
  12. The OSS will never be in the Cloud
    • Watch this space….

 

Bringing IT (OSS) all together

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?

ClosedLoop

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.

 

 

 

5G, Iaas and Mobile Edge Computing

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.

MECETSI

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?