White Paper
Maximizing Profitability with
Network Functions Virtualization
December 8, 2014
Maximizing Profitability with NFV
1. An Opportunity to Factor in Business Needs
Service providers are striving for more business agility. But business agility requires more flexible
business models, and these models have been limited by what traditional networks can do. As
shown in the following diagram, for any particular service, the financial value of the service’s business
model may be constrained by the level of operational flexibility supported by the underlying physical
functions.
Figure 1 – Business Model Performance
Network functions virtualization (NFV) is all about flexibility – making services and networks more
responsive to customer needs with an infrastructure that is more elastic. As shown in the diagram
above, this elasticity makes possible the dynamic calibration of virtualized functions to reach the right
level of operational flexibility. But managing this flexibility, one that results in operating gains, requires
the right kind of orchestration.
To fully harness the benefits of NFV, a more comprehensive kind of orchestration is needed – one
that considers all aspects of a virtualized network function (VNF), not just quality of service (QoS), but
also the fluctuating costs of running them.
As a result, the TeleManagement Forum approved the creation of the catalyst project “Maximizing
Profitability with NFV.” Its mission is to delineate an operational environment where the orchestration
of virtualized network functions is done in accordance with policies that aim to optimize both,
business value – minimize costs, maximize profitability – and customer experience. This catalyst
project is championed by AT&T in cooperation with Microsoft, JDSU and Ericsson.
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2. NFV Orchestration as a Source of Business Value
It is possible to structure NFV orchestration in such a way that optimizes business value while
coordinating service quality. As shown in the following conceptual diagram, the key is to:

set the orchestration to enforce policies that account for the technical (e.g., QoS) and the
business (e.g., financial) performance of running VNFs

to use state data enriched with the analytics of QoS and financial metrics in the processing of
these policies
Figure 2 – Structuring NFV Orchestration to Deliver Business Value
By combining analytics, policies and orchestration, service providers will be able to achieve leastcost, quality-compliant homing of VNFs, not just at instantiation, but at all times by virtue of dynamic
adjustments (e.g., moving VNFs). This capability should help service providers to be proactive when
it comes to dealing with evolving business environments, ecosystems and market shifts. Additional
benefits include:

Energy-efficient operations

Improved financial control over network operations

Just-in-time network planning and provisioning
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Figure 3 – Business Context for NFV Orchestration
2.1. Analytics Predict Behavior
NFV adds a level of automated, real-time elasticity to network functions. But, to stay in control,
service providers need to anticipate the behavior of these resources. Analytics of relevant state data
facilitate this. This data is derived from metrics extracted from two types of systems.

One type involves network and data center equipment – to extract QoS metrics

The other type of systems involves building management, data center infrastructure
management, industrial automation controls etc. – to extract financial metrics
Some of the metrics are available (i.e., standardized). Others are standardized but not easily
accessible (i.e., in data silos). The rest are not currently mined from the infrastructure and in need of
standardization. The following table outlines these kinds of metrics.
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Standardized
Available

OSS/BSS
Non-standardized
Silo-ed

Data center infrastructure
management metrics

Building/facility
management metrics

Electrical power
management metrics

Environmental monitoring
metrics

HVAC system metrics


System automation controls
metrics
Human resources system
(OpEx) metrics

Financial/accounting
metrics

Procurement metrics
Table 1 - Relevant Metrics
Deriving state data involves various kinds of calculations. Sections 3.1.3 and 3.1.4 provide examples
of these.
2.2. Policies Shape Behavior
In this context, policies are structured to account for both, the service provider’s business needs (e.g.,
profitably running a virtualized function), and the subscriber’s SLA/QoS needs, and to depend upon
continuous, analytics-enriched state data to determine compliance throughout the entire lifecycle of
VNFs.
As shown in the following diagram, each policy is structured into a profile that consists of a global
business clause and one or more subscriber-specific clauses. As a result, this kind of policy requires
dynamic assembly. Sections 3.1.2 and 3.1.5 provide examples of this kind of policy.
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Figure 4 – Outline of a Policy Schema
To accommodate continuous optimization, the thresholds in these policies should be structured in
such a way that indicates two things:

A target range

The best possible range, even if it is above or below
In this way, the system may be flexible enough to temporarily adopt non-ideal settings in cases of
extraordinary conditions or in cases where emerging new conditions signal the need to adjust the
target operating ranges themselves.
2.3. Orchestration Realizes Behavior
Policies are ultimately expressed by the coordinated efforts of the service and NFV orchestrations to
determine the best place to instantiate a VNF. Once the VNF is running, these policies may trigger
the dynamic migration of the VNF to another location due to deteriorating QoS and/or operational
costs of running the function.
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Figure 5 – Business Agile NFV Orchestration
By having this kind of policy driving NFV orchestration, the service provider could realize an
operational environment that automatically/dynamically morphs itself to maximize financial
performance, SLA compliance, resource utilization etc. This sets the stage for using
financial/business data to actually drive the business.
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3. Use Case
To illustrate the various concepts of this catalyst project, the following use case example is
considered.
1) Pre-conditions
a) Customer orders the service.
b) Service chain is set up – VNF is instantiated in a data center which meets QoS
and costs objectives.
2) There is a power outage in the data center
a) Emergency power kicks in.
b) The outage is expected to last longer than average meaning that its cost will
cross the threshold set in the policies.
3) VNF is relocated to another data center which meets QoS and costs objectives.
a) The service chain changes accordingly.
Figure 6 – Outline of an Example Use Case
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3.1. Pre-conditions
3.1.1. VNF’s Technical Requirements are Modelled in the Catalog
Figure 7 – VNF Modeling in the Catalog
The product catalog is populated with the hierarchical models of the services offered.
These models may include a combination of physical and virtualized network
functions. For the virtualized functions, these models show the kind of underlying
virtual machines and virtual network components needed to run these functions.
In addition, each service documented in the product catalog can be offered at
different levels of quality, as it pertains to the underlying CPU, storage and networking
resources.
3.1.2. Business (Financial) Objectives are Set in the Policies
A business (financial) objective has been set – constitutes the “global clause” in the
policies. As an example, the system can be set to run VNFs at an operating margin
between 1 and 5%.
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Determining compliance with the “global clause” requires the analytics of state data
derived from cost metrics.
3.1.3. Cost Metrics Processing is Set
Continuing with the example, operating margin involves a revenue factor and a cost
factor and it is calculated as:
Operating margin = (revenue – cost)/revenue
Revenue is the service price/hour.
Cost could consider factors such as the cost of:





the electric power consumed per hour
cooling water consumed per hour
o Alternatively, these costs could be estimated from facility
temperature readings. Some HVAC systems generate
temperature (heat) maps that can be accessed over a web
service interface. An analytics system can estimate or derive a
cost from those temperature readings. A data center running
mostly “hot,” even if it is complying with SLAs, should be
expensive to operate – given the additional cooling & water costs.
If there are other data centers in the region running “cooler,” why
not run the VNFs there?
network throughput consumed per hour
facility lease cost per CPU used per hour
OpEx per hour
o One business metric is level of automation. Automation controls
systems could be queried to get an indication of the level of
automation at which a date center is running. That kind of data
could be used as one of the metrics to estimate the OpEx for that
location.
The data mediation stage is set to collect these kinds of resource consumption
metrics. It is also set to collect the associated tariff data to be able to calculate the
actual costs per hour.
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Figure 8 – Cost-related Analytics
3.1.4. QoS Metrics Processing is Set
The data mediation stage is set to collect QoS metrics such as server utilization and
availability, network throughput, packet loss, delay and jitter. These metrics can be
used to derive service quality state data or index.
Figure 9 – QoS-related Analytics
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HW
HW
SW
SW
Physical + virtual data access
agents collect key metrics.
Data is integrated,
correlated and mediated.
Real-Time Usage
and Load QoS KPIs
Trending and
Threshold
Alerting
Processed KPIs,
Usage/Load indicators and Alerts
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3.1.5. Service is Ordered
The customer places a service order from the product catalog.
Figure 10 – Ordering a Service from Product Catalog
The level of the ordered service determines the technical (QoS) aspects of the policy
(e.g., subscriber-specific clauses). This completes the assembly of the policy.
3.1.6. VNF is Instantiated
Analytics-enriched state data is used during the processing of the policy to determine
the best place to instantiate the VNF. The appropriate workflows in the NFV
orchestrator are invoked to instantiate the VNFs in the desired places. In addition, the
NFV orchestrator requests the SDN controller to setup a service chain.
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3.2. Use Case
3.2.1. VNF Cost Degrades
Following the example scenario previously introduced, this use case starts when
there is a power outage in the data center. Emergency power kicks in. But the outage
is expected to last longer than average meaning that its cost will cross the threshold.
Figure 11 – Operating Cost Degrades
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3.2.2. VNF Needs to be Moved
Analytics-enriched state data is maintained and applied to the policy ruling the active
session of the service. The processing of the policy triggers the need to relocate the
VNF.
Figure 12 – Policy Triggers VNF Relocation Procedures
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3.2.3. New, Best VNF Host is Determined
Analytics-enriched state data is used to determine an alternate location to run the
VNF according to both QoS and financial objectives set by the policy.
Figure 13 – Analytics Provides Data to Guide VNF Relocation
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3.2.4. VNF is Moved
Orchestration functions invoke the proper workflows to move the VNF to the other
data center. The service chain changes accordingly.
Figure 14 – Orchestrating the VNF Relocation
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4. Overall Insights
Most of today’s efforts focus on purely technical optimization. Although valuable, this is not the entire
opportunity. It is critical to consider both technical and business aspects before any dynamic changes
are made to avoid creating business problems while solving the technical ones. NFV enables a new
perspective on how to structure the business of service providers – to extend the optimization to
consider economics and business factors as well (e.g., cost of running VNFs, security/regulatory
compliance, choice of service partner).
The industry should expand its sights to consider the following aspects:

NFV creates the opportunity to optimize technical and business performance – in
particular, when supplemented with dynamic policy layering and the use of analytics in
the processing of these policies.

A schema should be defined to structure the relationships between this kind of policies
and business rules, SLAs, resource policies etc.

Analytics should factor in:
o
SLA conformance and its projected financial impacts – e.g., financial implications
when a technical objective is not met, such as SLA penalties or market share
loss, or when exceeded, such as maintaining premium bandwidth tiers at
premium costs.
o
Resource utilization for cost modeling purposes – examples include:
o

Cost per CPU cycle (based on energy consumption)

Cost per Gb storage (based on energy consumption)

Other energy costs, e.g. lighting, HVAC/humidity control, water cooling
costs if not factored into the above

Cost of equipment leasing/acquisition and maintenance

Cost of building/facility leasing or construction

Cost of building operation and maintenance

Cost of security

Cost of intra-datacenter networking

Cost of NFV infrastructure operations and maintenance

Cost of reskilling/training of operations staff
Both current and future data center QoS/cost states to make sure that the impact
of moving VNFs among data centers is pro-actively assessed to avoid degrading
inadvertently the technical/business conditions of data centers as a result of the
dynamic move of VNFs.

A common, extensible information model is needed to take into account business (nontechnical) as well as technical parameters, and to identify the VNFs being managed.

The current barriers between OSS/BSS and the proprietary data silos of data center
infrastructure management systems, building/facility management systems, finance
systems, and system automation controls , among others, should be removed to enable
the kind of analytics needed to achieve business-driven NFV orchestration.
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