Research Challenges and

Opportunities in the Era of the

Internet of Everything and

Everything as a Service

April 2015

Carleton University - ARS-Lab

Stenio Fernandes

CIn/UFPE, Recife, Brazil

Agenda

A bit of technical background

Advanced Networking Architectures

Advanced Scenarios

Internet of Everything (IoE)

Everything as a Service (XaaS)

Smart Cities and Urban Services

Supporting Tools and Techniques

Applied Research

Research Challenges and Opportunities

My R&D Agenda

Technical Background

Network Virtualization

Advanced Networking Architectures

NV: concepts

What is NV?

Decoupling of the services provided by a

(virtualized) network from the physical network

Virtual network is a “container” of network services

(L2 -L7) provisioned by software

Faithful reproduction of services provided by

physical network

Analogy to a VM – complete reproduction of physical

machine (CPU, memory, I/O, etc.)

NV: concepts

Business Model for NV

Business Models for Wireless Network Virtualization

NV: Mapping problem

Software-defined networking

Advanced Networking Architectures

SDN – Motivation

Current networks cannot support this growth!

-Not service-oriented

-Static configuration

-Status not available to apps/users

-Cannot provide dynamic negotiation to users

Motivation: economics

The Need for a New Network

Architecture (The ONF view)

key computing trends:

Changing traffic patterns

contrast to client-server applications

today’s apps access different services

access to content and applications from any type of device, anywhere,

at any time

The rise of cloud services

agility to access applications, infrastructure, and other IT resources on

demand and à la carte

Big data means more bandwidth

Mega datasets is fueling a constant demand for additional network

capacity in the data center

Limitations of Current Networking

Technologies (The ONF View)

Meeting current market requirements using device-level

management tools and manual processes

Complexity that leads to stasis

The static nature of networks is in contrast to the dynamic nature of

today’s environment

Inconsistent policies

To implement a network-wide policy, thousands of devices and

mechanisms must be configured

Inability to scale

traffic patterns are dynamic and unpredictable

users with different apps and performance needs

SDN (the ONF view)

Emerging network architecture where network

control is decoupled from forwarding and is directly

programmable

can treat the network as a logical or virtual entity

Network intelligence is (logically) centralized

SDN controllers maintains a global view of the network

Network appears to the applications and policy

engines as a single, logical switch

infrastructure gains vendor-independent control over the

entire network from a single logical point

SDN Architecture

Motivation: what drives SDN R&D?

Reduced network costs (CAPEX / OPEX)

Support to Innovative New Products (applications,

services)

Synergy with Cloud Computing Services and

Infrastructure

And most importantly: Real time network

programmability

This is the quest for networks with improved performance

while keeping them simple, scalable, and “ smart”

A Simplified View of SDN

1. A network in which the control plane is physically separate

from the forwarding (data) plane

• A single control plane controls several forwarding devices

Supporting SDN with OpenFlow

First standard communications interface for SDN

between the control and forwarding layers

It allows direct access to and manipulation of the forwarding

plane of network devices

OpenFlow IS NOT SDN!

OF-based SDN Benefits

SDN-based orchestration and management tools

to quickly deploy, configure, and update devices across the entire network

Reduced complexity through automation

develop tools that automate many management tasks

Higher rate of innovation

Allowing operators to program and reprogram the network in real time

Increased network reliability and security

More granular network control

apply policies at a very granular level

Better user experience

Centralized network control and state information

available to higher-level applications

Internet2 SDN use case

Internet2 SDN infrastructure

Consequences of SDN adoption

1. Hardware and Software from different vendors

2. Simplified Programmability

3. Enable application-level control/programming of

network

4. Enables centralized control, which implies

simplification of network operations

5. Prospective integration with Network Virtualization

technologies (cf. previous section)

SDN use case: Virtual Cloud

SDN IN THE MOBILE CORE

Network Functions Virtualization (NFV)

Service Function Chaining (SFC)

Advanced Networking Architectures

NFV: Definition

Internet Research Task Force (IRTF): “network

architecture concept that proposes using

virtualization related technologies, to virtualize

entire classes of network node functions into building

blocks that may be connected, or chained, together

to create communication services”

transform the traditional operator networks by evolving

standard virtualization technology

consolidate network equipment types onto industry

standard high volume services, switches and storage

located in a variety of NFV Infrastructure Point of

Presences (NFVI PoPs)

NFV: Terminology

NF: Network Function

A functional building block within an operator's

infrastructure

It has well-defined external interfaces and behavior

Network Function Consumer

NFV: NFV-Enabled Equipment

commodity equipment to replace the dedicated

hardware boxes for the network functions

Network Function Provider: a Network Function

Provider (NFP) provides virtual network function

software

NFV: Terminology

NFVI: NFV Infrastructure

computing, storage and network resources to

implement the virtual network function

VNF: Virtual Network Function

an implementation of an executable software program

whole or part of an NF that can be deployed in a NFVI

NFV: Requirements

Portability: VNF mobility across different

but standard multi-vendor environment

moving a VNF within the NFV framework

with the Service Level Specification (SLA)

requirements including performance,

reliability and security could be a challenge

Performance: Virtualization adds

additional processing overhead and

increases the latency

NFV: Requirements

Elasticity: scaling with the SLA

requirements

Resiliency: service availability and fault

management

Security and Service Continuity:

restoration of any ongoing data sessions

should be transparent to the user of NFV

service

NFV: Use Cases

Network Function Virtualization Infrastructure as a

Service (NFVIaaS)

Generic IaaS plus NaaS requirement which allows the

telecom operator to build up a VNF cloud on top of their

own DCs Infrastructure

Virtual Network Function as a Service (VNFaaS)

allows the enterprise to merge and/or extend its specific

services / applications into a 3rd party commercial DC

provided by a telecom operator

Virtual Network Platform as a Service (VNPaaS)

NFV: Use Cases

Telecom Network Functions Migration

Mobile Core Network functions

IMS functions

Mobile base station functions

Content Delivery Networks (CDN) functions

Home Environment functions

Fixed Access Network functions

Internet of Everything (IoE)

Advanced Scenarios

Let there be IoT

Let there be IoE/IoA “bringing together

people, process, data,

and things to make

networked connections

more valuable” (Cisco)

Everything as a Service (XaaS)

Advanced Scenarios

Everything IS a Service Now

Classic

Software (SaaS)

Platform (PaaS)

Infrastructure (IaaS)

Network Related

Classification (CaaS), Deep Packet Inspection (DPIaaS)

Botnets (BaaS)

Processing Related

Analytics (AaaS)

Intelligence (InaaS)

The list of *aaS is huge and it is growing fast

Smart Cities and Urban Services

Scenarios for IoT/IoE

Building a Highway for ‘IoE/Big Data’ based Urban Services

We are virtualizing every concentric circle in this figure!

Things get complicated when we think in a larger scale

Things get complicated when we think in a larger scale

Measurements and Analysis

Formulation of Optimization Problems

Dependability Analysis

Applied Game Theory

Control Theory

…

Supporting Tools and Techniques

Measurements

Packet

• More detailed: from link to application layer (with timestamps)

• Huge storage and processing requirements

• Header or payload (full or partial)

Flow

• Flow summaries

• connection info, number of packets, duration, volume

• IPFIX/CISCO’s NetFlow v5/v9 records

Aggregate

• SNMP counts

Measurements: Packets

Measurements: Flows

Sampling Technique

Flow Monitoring

Tool

F4

F3

F2

F1

F4

F3

Representativeflow sample

Collected, classified flows

Network Packets

FlowCollector

Router: flow

building

Collector: flow

storage

31 2 4

GUI: flow analysis

and reporting

5

On-line sampling Off-line samplingTraffic Management

and AnalysisLive Network

Analysis of Packet Traces

IP header• Traffic volume by IP addresses or ASes

• Burstiness of the stream of packets

• Packet properties (e.g., sizes, out-of-order)

Transport header

• Traffic breakdown by protocol

• TCP congestion and flow control

• Number of bytes and packets per session

Application header

• URLs, HTTP headers, file type

• DNS queries and responses,

• mobile devices

54

REGEX to DFA to DPI Systems

• it’s possible to identify patterns (signatures) present in the app messages

• Deep Packet Inspection (DPI) systems

• App signatures may be represented by Regular Expressions (RegEx)

• ReGex may be represented as Finite Automatons (NFA or DFA)

From the collected data: Packet payload

Packet trace

Flow records

Core Modelling

• maximize insight into the data set

• extract important variables

• detect outliers and anomalies

• develop parsimonious models

Exploratory Data

Analysis

• Does the data follow a particular PDF?

• Maximum Likelihood Estimation

• Hypothesis testing

Statistics Inference

Research Challenges And

Opportunities

Research Challenges and

Opportunities (RCO) - General

IoE, Smart Cities Platforms, and XaaS

Network Functions Virtualization/ Service Function Chaining

Virtual Networks / SDN

Optimizations of Canonical Network Elements and Services

Research Challenges and Opportunities

(RCO) - General

Cloud Computing Services promoted huge changes in the

computer networking field

Distributed and hybrid clouds are a reality

Moving massive amount of data to be moved

SDN seems to be a smart solution to address scalability and

other issues for Big Data

NV is available as the supporting technology

IoE and Smart Cities face barriers to full deployment

Opportunities for advanced research is everywhere in those

new scenarios

RCO #1: Measurements

Network-wide view

Crucial for evaluating control

actions

Multiple kinds of data from multiple

locations

Large scale

Large number of high-speed links

and routers

Large volume of measurement data

The “do no harm” principle (passive measurements)

Don’t degrade router performance

Don’t require disabling key router features

Don’t overload the network with

measurement data

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RCO #2: Packet Measurements

Building efficient DPI engines

• 1 packet every 5ns!!!

• Based on DFA/NFA from regular expressions that express application signatures

• For hardware-based or commodityplatforms

Update of app signatures database

• Encrypted traffic is not possible

• Analysis of packet payload forbidden in a number of countries

RCO #3: High-Performance Traffic Monitoring

Systems in Virtualized Environments

Large number of application

signatures

Complexity of the signature

patterns

Unpredictability of signature location in the network flow, as well as within the packet payload

Performance bottlenecks at Virtualization

levels

RCO #4 - SDN/NFV

Elasticity in Distributed Clouds/SDN/NFV

accommodate the increased traffic in a fine-grained manner

VNF was not designed for scaling up/down

SFC considering Dependability Parameters

Optimization of VNF Forwarding Graph

placement problem

Consider multiple stateful and stateless VNF functions

Elasticity in NFV+SDN with Predictable Performance

Elasticity with Reliability

Network Performance

Instantiation and migration of virtual appliances

RCO #4 - SDN/NFV

XaaS on top of SDN/Network Virtualization Infrastructures

RCO #4 - SDN/NFV

Northbound (apps) to Southbound (devices) Understanding of Traffic Patterns

Needs precise classification systems

Needs model building

At high-speed

Real-time

Adapt to abrupt and long-term changes

Cope with millions to billions of flows in short-term

Core challenge: decide which service policy to be applied to a flow (Classification and optimization problem)

RCO #4 - SDN/NFV

SDN Architecture Design

accommodating consistency, dependability, and scalability

requirements

control plane: centralized or distributed processing?

controller placement problem

How many? Where to place them? How to distribute tasks?

Maximizing fault tolerance and dependable infrastructure

to support high-performance intra-DC data exchange for Big Data

Analytics

Optimized Policy Framework

automatic policy transformation

RCO #5 – Designing Platforms to Smart

Cities

Scalable Platforms for Smart Cities

on Top of CC+SDN infrastructures

To support deployment of urban services

Orchestration of services with transparent network

functions into a commodity data center

Joint compute and network virtualization and

programming

Network Functions Live Migration - Allocation /

(Re/De)allocation

My Research Agenda

Topics of Past Research(with traces of remaining interest)

High-Speed Traffic Measurement

Internet Traffic Modeling and Profiling

Peer-to-Peer Networking

Multimedia Streaming Protocols and Systems

Wireless and Mobile Networking

Performance of Transport Protocols

Current Research

Current Team (worldwide): 1 Post-doc, 8 PhD, 7 MSc

Some Graduate Studies Topics

A Game-Theoretic Approach to Vehicular Networks (VANETs)

Protocol Design

A Control-Theoretic Approach to Adaptive Streaming in the

Internet

Data Mining of SDN Controllers Performance

Domain Specific Modeling Languages for SDN

New Architectures for IoE/Smart Cities

Optimal Dependable Service Function Chaining Deployment

Current Research

Some Applied Research Projects

Canada-BR: Mining Trajectories on Automatic Identification

System (AIS) Satellite Data

EU-BR: Scalable and Secure Cloud Computing Services for Smart

Cities

France-BR: Measurement and SLA Management of Heterogeneous

Cloud Infrastructures

AR-BR: Traffic Monitoring and Analysis of Dependability in

Virtualized Networks

BR: Smart Tracking – A Business Decision Service Platform based

on Passive Data Collecting and Mobility Analysis of

Traceable Mobile Devices

BR: Mobile Devices Tracking and Positioning in the Context of

Smart Cities

Recent Papers

Design and Optimizations for Efficient Regular Expression Matching in DPI

Systems. Computer Communications, 2015

A flexible DHT-based directory service for information management. Peer-

to-Peer Networking and Applications, 2014

Dependable Virtual Network Mapping. Computing, 2014.

Design and analysis of an IEEE 802.21-based mobility management

architecture: a context-aware approach. Wireless Networks, 2012.

Urban Data Collectors: A Pragmatic Approach to Leveraging Urban Sensing,

IEEE Integrated Network Management Symposium 2015, 2015, Ottawa.

Model-Driven Networking: A Novel Approach for SDN Applications

Development. IEEE Integrated Network Management Symp. 2015, Ottawa

Work@CarletonU

Combining Expertises

Computer Networking (Measurement, Modeling, and

Analysis)

Modeling and Simulation

Supporting Advanced Research on Simulation

for Novel and Challenging Scenarios

Open to discussions

Help grad students

Helping writing new research proposals

Addressing the challenges of up-to-date scenarios

Research Challenges and

Opportunities in the Era of the

Internet of Everything and

Everything as a Service

April 2015

Carleton University - ARS-Lab

Stenio Fernandes

CIn/UFPE, Recife, Brazil

Center For Informatics (CIn)

Federal University Of Pernambuco (UFPE)

Recife, Brazil

About

CIn/UFPE

• ~42K students, ~1.3K PhD professorsUFPE

• Top 5 CS Graduate Program in Brazil

• Evaluation: CAPES level 6 (scale 1 to 7)

• Top 10 most important CS Research Center in Latin AmericaRecognition

• ~100 PhD professors

• ~25% BR Research ChairsFaculty

• Computer Science, Computer Engineering, Information SystemsPrograms

2000+ students

International collaboration:

Europe, Asia, and North America

Research Projects

(Private and Public funded)

CNPq, CAPES, FACEPE

Samsung, Ericsson,

Motorola, Nokia, LG, HP, etc

Recipient of a number of awards:

• 2011 Most Innovative Brazilian

Research Center

• Microsoft Imagine Cup (since 2005)

• ACM Intl. Programming Marathon

Recruitment:

Google, Microsoft, Facebook

CIn/UFPE

Recife, Pernambuco, Brazil