Telco Use Cases for Google Cloud Dataproc – managed Spark & Hadoop for Mobile Network Performance Data

As a Google Cloud Platform certified architect I really should blog some more about my actual usage of GCP. One of my favourite tools is Dataproc as it provides a managed Spark & Hadoop environment and enables a lambda architecture suitable for complex network event processing and function remediation.

A mobile radio network is a dynamical system that can be modelled ergodically. Meaning that the radio network performance in geometrical space should be observed and modelled over a period of time. Storing this sort of data requires a geospatial datastore and a timeseries datastore. It is a huge amount of data stored as a nested map. This is why Dataproc’s ability to provide a probabilistic approach to testing a deterministic system is really useful in a remediating / self-healing mobile network.

Apache Spark RDDs

Apache Spark provides the parallel processing of the variant datastores as Resilient Distributed Datasets (RDDs). Modelling the baseline data for geospatial topology, coverage and time-based trials is not trivial. But the fundamental processing of huge datasets for improved RAN distribution is highly challenging but eventually highly beneficial.

Guaranteeing Network Slices: The Role of 5G WAN Optimisation for Small Cells

The City of Sacramento has deployed 300+ small cells as part of a 5G Fixed Wireless Access deployment with Verizon. These deployments can only provide partial 5G coverage of a city like Sacramento. This is because of the relative transmission range of 26Ghz and 28Ghz spectrum. In fill is required with further small cells and coverage fill is required at mid-band Spectrums like 3.5Ghz.

Sacramento 5G FWA Coverage

The most effective way of delivering backhaul to multiple small cells sites is to use SD-WAN technologies over either Ethernet or microwave links. WAN Optimisation requires an intelligent-path control mechanism for improving application delivery and WAN efficiency. This intelligent path control and management of VPN tunnels needs to be integrated into the network slice management control plane function in order to guarantee the mission critical services.

The Network Slice Management control plane needs to manage end to end the latencies and traffic shaping. To do this the SD-WAN component for small cell backhaul must be an integral part of the end to end network orchestration.

Master Orchestrator Problems

The challenge for telcos face is how to integrate technology specific orchestrators. A 5G SD-WAN small cell solution could involve four unique orchestrators:

  1. small cell orchestrator
  2. with a 5G core orchestrator
  3. a network slice orchestrator (NSSMF)
  4. and multiple existing SD-WAN orchestrators

Most telcos have already deployed a SD-WAN products, involving multiple SD-WAN CPE vendors, where each CPE vendor provides a bespoke orchestrator. Industry examples include, the Cisco Viptela SD-WAN solution which uses a vManage network management solution within the orchestration / management plane and the Nokia Nuage SD-WAN solution that follows the same pattern.

To break this predominance of orchestrators (with lots of compensating logic) it is important to seek integration by API direct to the control plane. To be successful telcos may wish to examine how a vendor agnostic Network as a Service may improve their 5G orchestration strategy.

A 5G Data Fabric

Most 5G deployments will not be greenfield. But a successful 5G deployment is not limited to simply deploying new radio on existing sites. It requires a new approach to telecom IT that can both simplify the telco’s estate and prepare for the new business opportunities of 5G. A complete data fabric (for 5G or for everything) will support both the business opportunities and the network complexities of 5G.

A data fabric includes all of the necessary data services for operating a mobile network and providing connectivity and ‘beyond connectivity’ services. This means offering the many different persistence storage toolsets to your business logic layer (as represented by micro-services in docker). An application can then utilise the most appropriate persistence technology for their requirements. For example, this could mean exposing a RDBMS for structured data, a Graph for modelling topologies and document storage for persisting YANG documents.

Data Fabric for a Micro Service Architecture

The business value of the data fabric is that it allows the clever telco to disassociate their software requirements’ from the data plane. Thus enabling a micro-service architecture that can manage a virtualised network; and then on top of that expose services to their customers.

Key data fabric use cases for 5G include:

  • A network planning architecture for geo-planning cell site deployments including in-building
  • A network topology architecture that can model a highly complex network and enable Self Organising Networks
  • A time series streaming architecture that can model events coming off a network and a customer’s deployments and enable effective Machine Learning driven autonomic improvements
  • A network orchestration architecture for a virtualised network (full or partial)
  • A network slice management and guarantee architecture (with support for a blockchain based service level guarantee)
  • A subscriber data management architecture for unified value added services and subscription

The following is my description of a logical data fabric for a 5G implementation. I am publishing it because it can help network operators to push their software vendors to decouple the software’s logic from its data persistence. Below are all the logical tools needed:

  1. RDBMS for ACID based transactions that are useful for physical inventories, managing subscription updates (less so reads) and all other structure data
  2. Graph database for modelling network topologies, relationships and dependencies. Very useful for machine learning, root cause analysis and spotting previously unknown interconnected loops between items
  3. Wide column database for dealing with unstructured extensible datasets that include all the different devices supported on a 5G network. Very useful within IoT and customer network experience.
  4. OLTP NoSQL database for offline analytical processing including network topology efficiency modelling and network performance analysis as part of an ITIL Problem / Change Management process
  5. Document datastore for managing Infrastructure as Code and Virtual Network Function deployment descriptors in the form of YANG documents. Useful in blockchain contracts and services.
  6. In Memory Datastore for fast reads and data caches
  7. Geo-spatial database for modelling RAN deployments and radio propagation. Incredibly important as RAN efficiencies have a major bottom line impact. Increasingly need to support in-building information for small cell deployments. Needs to work together with other radio technologies including 5G.
  8. Time series database for performance monitoring which can be implemented within the customer network experience function of a wide column database and with use of Grafana and Prometheus

Some 5G Data Fabric Use Cases:

RDBMS Database Graph Database Wide Column OLTP Document Data Store In Memory Database Geo-spatial Database
Network Plan & Build and Analysis Y Y Y
Physical Network & Static InventoryY     Y
Virtual Network & Dynamic Inventory   Y     Y    
Fast Read Inventory Y   Y 
Streaming Fast Analysis YY    
Offline Event Analysis Y Y
Subscription Management & EntitlementsY    Y 

In conclusion, most telcos have bought siloed commercial off the shelf products for individual specific use cases. This has meant that the telco has often only used as little as 40% of the intrinsic value of their commercial software licences. The cost of building 5G will be high, and the greater share of the prize will go to the most agile operators. It is therefore incumbent on mobile operators to drive the greatest efficiencies from their software investments.

5G is a great driver for change. The most effective 5G operators will be those that can get their data architecture right first time. Telecom operators must start moving to a data fabric.

Economic Analysis of mmWave Fixed Wireless Access as an Alternative for FTT/x

FWA is not a new idea with 5G and has been available to anybody tethering since 3G. FWA is comparable to Fibre-to-the-Home as both are connectivity solutions for the edge of the network. 5G mmWave (~25Ghz and above) is promising an alternative to FTTH, with 1Gb per second download speeds. It is therefore worth understanding the technologies and engineering necessary to make FWA a viable or better alternative to fibre.

Verizon has targeted FWA as an alternative to FTTx with its 5G Home service launched across Houston, Indianapolis, Los Angeles and Sacramento in October 2018. Verizon estimates the 5G mmWave FWA addressable market to include 30 million premises. To be successful Verizon’s FWA has to be cheaper than the delivery of FTTx and will have to overcome some quite considerable engineering challenges. These include the roll-out of multiple 5G antennas with small-cell front-haul for extended coverage, the deployment of external to home 5G receivers, a distributed core that can host Mobile Service Edge and CDNs close to the 5G Cell Towers, and a new 3GPP Release 16 Core that can support network slicing for the 28Ghz spectrum.

FWA logical architecture

The above diagram shows a logical architecture for a 3GPP Release 16 compliant new mobile core connected through multiple distributed sites connected to radio site gNodeBs delivering FWA service to the home. A new core is not fully necessary, as Verizon are launching already using their channel coding, multiplexing and interleaving technologies. A new mobile core will be advantageous in guaranteeing the QoS for mmWave FWA slices.

The majority cost for FWA is in the delivery of the radio network and mmWave antenna. Higher costs will always be incurred if RAN planning has not been optimised and necessitates 5G small cell in-fill. For this reason mmWave may be better deployed as new sites in a standalone Model 2x configuration. Other costs include upgrading the mobile core but this cost is shared with other 5G use cases. Spectrum licencing is another important cost. Currently mmWave licence spectrum is relatively available, hence lower cost, and more extremely high frequency is being released by national regulators.

To be competitive FWA must be economically viable against fibre delivered to the home. This includes internet peering & CDNs. In regulated territories like the UK that already have Local Loop Unbundling the competitor CSP can consume service from the distributed site. This has been part of the US regulatory framework since the US Telecommunications Act of 1996 that requires ILECs to lease local loops to competitors (CLECs). In an all fibre model the cost of connection is to the premise (FTTP) or home (FTTH). If regulatory dark fibre or open ducts are in place then the competing CSP can consume those services at a regulatory defined price. In the UK that model is only being developed after initial regulatory challenges and in the US the FCC has not extended enforcement of dark fiber offering since 2014. It is therefore suitable for a US mobile carrier to consider 28Ghz as a more efficient distribution mechanism than FTTH if there are no regulated dark fibre or open-duct solutions available. It is also worth considering that the civils part of the delivery of fibre (the dotted FTTH line in the below diagram) can cost as much as 90% of the total service delivery cost.

Simplified FTTH Architecture

A final comparison between FTTH and FWA:

  • Same Costs: Network spine, backhaul and equivalent equipment are the same for FTTH & FWA
  • Higher FWA Costs: The spectrum licence costs are unique to FWA but due to spectrum availability may not be prohibitive, power & cooling costs are higher for FWA and the maintenance cost of FWA should be higher for exposed antennae
  • Higher FTTH Costs: The only cost that is higher with FTTH is the civils part of delivery. This cost can be very high because of the complexity of getting wayleaves and permissions and digging up roads.
  • In conclusion, FWA should be more efficient and cheaper service to deliver as long as the network planning is accurate and does not necessitate continual modification based on further cell deployments.


Monitoring Micro-Service Applications across Hybrid Clouds using Istio service mesh multi-clusters, Kiali observability, Zipkin tracing, Prometheus events and Grafana visualisations

Most enterprises have complex application deployments across their own internal data centres and commercial clouds. I am using Google Cloud Platform and AWS in this example. Where I work, we traditionally monitored logs and configured alarms for network and infrastructure monitoring. This approach was disjointed and slow to react. The enterprise moved to cloud hosting with elastic scalability a few years ago which led to multiple stove pipes of monitoring capability and a heavy dependency on VPC interconnects. We wanted to move to a multi-cloud environment whilst maintaining the benefits of a centralised technology operations centre.

We quickly realised that we had specific workloads running in different environments with no common mechanism for monitoring & reporting. This led us to examine open-source monitoring architectures based on Netflix’s Keystone Pipeline. Our requirements were for a universal data visualisation and observation of our application based on Grafana, Zipkin and Kiali.

Logical architecture and open source technologie

This architecture is based on open-source projects that we can use across GCP, AWS and internally. Everything is predicated on Docker containers and Kubernetes container orchestration. Istio provides the policy and load-balancing functions of a service mesh and GRPC provides the low latency integrations between the micro-services. These technologies provide the enablers for the monitoring & visualisation capabilities of Kiali, Zipkin and Grafana.

The following diagram shows the open-source component architecture to support different internal data centres (one for IT running Pivotal and one for mobile network IT running Openstack), Google App Engine and AWS Kubernetes service EKS on EC2. This logical architecture has the intention of a single pane of glass for service management toolkit technologies.

Open Source Monitoring Toolset across Hybrid Clouds

To achieve a single pane of glass across multi clouds requires the need of a aggregation function that can integrate the control plane of multiple Kubernetes container orchestrations. Istio achieves this by supporting multicluster deployments across hybrid clouds by deploying a control plane to each Kubernetes cluster. Kiali can provide service mesh observability of a Istio multi-cluster environment. A Helm variable global.remoteZipkinAddress can be used to connect Zipkin distributed tracing to the Istio cluster.

All of this together enables a Kubernetes control plane on each hybrid cloud environment to be interconnected to the master visualisation technology operations centre environment.

The traffic flow of a Kube ingress allows the ELB using GRPC to integrate multiple clusters where the Prometheus collection agents are deployed. These can then be aggregated together through the Prometheus server in the logical control plane.

Note that the HELM Tiller deployments to each cluster support the multi-cluster control plane as described here.

Kubernetes and Istio Mixer Control Plane for Multicluster Deployments

Prometheus provides the time series of events for the multiple clusters that can then be queried by any Grafana server which treats storage backends as time series data (Data Source). Each Data Source has a specific Query Editor that is customized for the features and capabilities that the particular Data Source exposes. Grafana can also consume StackDriver, CloudWatch and Ceilometer for Openstack.

In conclusion:

  • Istio, Helm & Tiller can manage a multi-cluster hybrid cloud deployment
  • moving to a hybrid cloud requires a visualisation of complex integrations which is where Istio and Kiali service mesh observability are strong
  • hybrid cloud monitoring can be achieved by deployment of agents including Prometheus collection agents to individual clusters and connected to a Prometheus server which in turn is rendered by a Grafana server
  • Zipkin provides distributed tracing and integrates with the Istio managed cluster

One point not described is the requirement for a technical inventory that describes the individual micro-services and the toolsets that can be deployed to each container, but i’ll save for another blog.

Finally, there are technology alternatives to Kiali, Zipkin, Grafana and Prometheus such as included Logstash & ELK, FluentD and commercial solutions like Datadog.

5G and TM Forum Digital Transformation Middle East

I’m talking at the TM Forum Middle East Digital Transformation event https://dtme.tmforum.org/speakers/charles-gibbons/ on 5G. It’s great to be invited to share my knowledge of 5G architecture and delivery. I will be covering the roll out of 5G service in the UK and will be specifically covering how knowledge share is critical for successful implementations of 5G.

EE is launching 5G in the UK in 2019 across 16 cities: https://newsroom.ee.co.uk/ee-announces-5g-launch-locations-for-2019/

EE coverage design_FINAL


Focus on 5G Monetisation and the business value and the need for Open APIs for an ecosystem architecture. Telcos do not have a domain right to provide IoT services over 5G. It is important that all CSPs support open APIs for their 5G services including TM Forum, GSMA OpenAPIs, ETSI Mobile Edge Compute APIs, NIST and other more commercial offerings.