optimizing the business value of nfv by harnessing real ......management/analytics, network...

© 2015 TM Forum | 1

Optimizing the Business Value of NFV by Harnessing

Real-time Financial and Quality of Service Analytics

Catalyst Project: Maximizing Profitability with NFV Orchestration

© 2015 TM Forum | 2

Collaboration

Champions:

Participants:

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NFV Enables a New Perspective

Financial Value

Operational Flexibility Physical

Functions

Virtualized

Functions

Dynamically

calibrated to the

right level of

flexibility

To achieve maximum value

Business Performance

Operating Gains

Maximizes the financial value of services by removing the

operational constraints of physical functions

… to factor in business profitability

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NFV Brings New Levels of Flexibility and Optimization

Collect

• Cost drivers

• Technical metrics

Analyze

• Model the options

• Optimize

Act

• Move

• Re-deploy

• Scale …

Creates an opportunity to consider both QoE/QoS & financial optimization

• Project leverages analytics and

policies that dynamically

determine orchestration

decisions to make more well-

rounded NFV instantiations

• We show a closed-loop

system from mediation,

monitoring, optimization

analytics through policy-driven

instantiation and restoral

considering QoE/QoS and

financial metrics Goal is to optimize business value dynamically

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Multi-dimensional Optimization is Key

Optimization

Policies

• Global clauses

• Financial clauses

• Service clauses

• Customer clauses

Cost Drivers

• Compute

• Storage

• Network

• Power/Location

Technical Metrics

• QoS

• CPU

• Memory

• Network bandwidth

Business

Impacts

• QoE

• SLA violations

• Services

• Operations

Policies account for

these diverse set of

objectives to drive

optimization decisions

Optimize against a wide

range of financial and

technical objectives

Decisions are geared toward

attaining a level of business

agility not possible before

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Examples of Financial-based Optimization

Triggers

• Time of day power tariff change - As a result the cost of placement for services would force the move to

another data center

• Cache resources become available - Low-cost disk caching becomes available so some services could move

because the transport-cache equation changes

• Margin optimization - Service revenue for some services changes, maybe due to a marketing campaign, and as a

result we move these services

• Cost creep - The cost of capacity has increased over time and a global optimization (which is being run in parallel)

identifies a +X% benefit to a global optimization so services are moved

• Service protection change - A service is now offered at a higher revenue because of improved protection

requirements which changes the overall cost dynamic

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PBM Motivation - NTT Concept

他社network 他社network

既存network 既存network

Service providers

(corporate/individual)

NTT Group Services

IoT/M2M Olympics/Paralympics B2B proposals Disaster

prevention

Network

Server/applications

Security Big data

Collaboration functions/automatic optimization

Common APIs

Network

Servers/applications

Company's own cloud

Compute Storage Database

Existing

network

Other

companies'

cloud services

Other

companies’

networks

NFV

SDN

LTE/5G LPWA network Optical

access Public Wi-Fi

Network Network

Servers/applications

Network

Integrated

Control

Offer network/cloud/applications by multiple businesses as one-stop service to service providers.

Expand B2B2X services provided by diverse industries and diverse players.

Enable batch operation/maintenance for service providers.

Improve efficiency of operation tasks to operate/maintain services w/ Hybrid NW.

Based on DSRA

Integrated Policy Management

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Requirements for Policy management

Manage policies across policy domains (i.e. policy federation)

Identify Information model for engaged parties in business model

Enhance the scope of managed entities towards Service in addition to Network Resource

Existing NW Virtualized NW(SDN) VNF (Data Center)

Federation

Hie

rarc

hy

Policy for providers (B2B2X)

Policy for providers (B2B2X)

Policy for customers (B2B2X)

Multi-aspect Policy

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Contribution Points to ZOOM Project

Propose requirements for policy mgt. and corresponding information model at ZOOM (Foundation Team)

Suggest the necessity of multi-aspect policy to manage policies which vary in aspects

Policy federation across policy domains

Information model for engaged parties in business model

Scope Expansion to Service in addition to NW Resource

ZOOM

Foundation Team

• Policy requirements

• Information model

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User Stories on Service Operation

Designing policies that optimize financial and QoE/QoS performance

Dynamically collecting the most relevant metrics

Automatically evaluating risk of optimization options

Dynamically orchestrating where to run virtual network functions

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Architecture & Processes

• Procedural definition of a closed-loop control system composed of data collection, analytics, optimization, dynamic policies and NFV orchestration

• Driven by policy decisions that optimize performance based upon QoE/QoS

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Business Policy Enablement

Network & Cloud

Monitoring

QoE+Financial Optimization

Policy-Driven

Orchestration

• Financial & technical policy

specifications

• Complex decision support

• Leverage and extend TMF

policy standards


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Network & Cloud

Monitoring


Policy-Driven

Orchestration

• Policy-driven monitoring & alerting

Dynamic and Real-time

• Customer experience & network surveillance

• Orchestration-driven adaptive network & service monitoring


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Network & Cloud

Monitoring


Policy-Driven

Orchestration

• Policy-driven cloud monitoring & alerting

• Cloud/DC performance dashboard and

analysis at multiple hierarchies

• On-demand power, computation,

storage statistics at any time scale


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Network & Cloud

Monitoring


Policy-Driven

Orchestration


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Network & Cloud

Monitoring


Policy-Driven

Orchestration

• Joint network and data center optimization

• Live network status updates & policy-driven close the optimization loop

• Flexible models support predictive capacity management


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Network & Cloud

Monitoring


Policy-Driven

Orchestration

• Intra-data center optimization

• Live cloud status updates & policy-driven close the optimization loop

• Agile cost modeling supports business planning


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Network & Cloud

Monitoring


Policy-Driven

Orchestration

• Enables business policies to be factored

in operations process flow-through

• Streamlines the balance between

technical & financial considerations in

VNF orchestration

• Maintains a logically centralized

programmable policy repository for

physical and virtual infrastructure


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Network+Cloud Monitoring

CoE+Profits Optimization

Policy-driven Orchestration


Business Policy

Enablement

Network & Cloud

Monitoring

QoE + Financial

Optimization

Policy-driven

Orchestration


Close the loop and

control the cycle

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Architecture & Process Enhancements

• Explores cases where competing policies need

to be reconciled to make optimization decisions

‒ With inclusion of QoE and financial data,

optimization/analytics becomes multi-faceted and

dynamic

‒ Multiple kinds of optimization may need to be

reconciled (finance, CEM, network/data center) by the

decision engine

• Improves the intelligence of the closed-loop

control system by involving financial support

systems

‒ Understand the financial elements that make up the

cost and revenue components of service offerings

• Decision engine may be human-assisted – in

particular when handling exceptions

• This phase looks at new metrics, policy model

extensions and optimization algorithms

Financial Input and Analytics

Dynamic Monitoring Orchestration

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Policy Ecosystem

Fully Vertical Provider

(content, cloud, network)

Global Policy

Cloud Policy Network Policy

Cloud Provider

Network provider

Network Policy

Content Provider

Content Policy

Cloud Policy Federated Policy Management

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Contributions to TM Forum

ZOOM Foundations

ZOOM Information Model Extensions to accommodate multi-aspect

policies involving resource policies and the operator’s financial objectives

ZOOM Operate

ZOOM User Stories that capture interactions among data collection,

analytics, optimization, policy management and NFV orchestration

Normalized architecture and operational considerations for

implementing closed-loop control in hybrid networks

TR229

IG1128

Policy modeling

Decision Support Mechanism that dynamically gauges the level of

automation in making decisions based upon risk factors that are calibrated

according to a decision impact severity scale

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Policy Modeling

Information modeling aspects of policy management

‒ The Catalyst team is working on the underlying policy model to abstract policies that

govern various system operations, starting from regulating QoE to configuring and

securing the cloud as a whole in addition to its individual tenant virtual infrastructures

‒ Another challenge being addressed is the complexity of B2B2X ecosystems where the

policies of various actors need to work in concert with global policies required to assure

the smooth end-to-end delivery of cloud services

‒ Looking at the Information Framework as enhanced for ZOOM to see how to

accommodate multiple and potentially conflicting policies. This work is being

incorporated into a policy dashboard that will be part of the prototype shown in Nice

next year.

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• Optimize resource elasticity to maximize

profitability while being more responsive to

customer needs and changing business and

operational conditions

• Use multi-aspect policies to enable

OSS/BSS, data center

management/analytics, network

optimization, data collection and monitoring

and financial systems to work in concert

• Improve the profitability of new business

models and NFV ecosystems by solving

operational problems and contributing to

new directions in TMF ZOOM standards

development Sets the stage for using

financial/business data to

actually drive the business

Business-agile NFV Orchestration

© 2015 TM Forum | 26

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