Currently there is increasing interest in scientific research on network traffic management for advanced scenarios (e.g. Internet of Everything (IoE), Everything as a Service (XaaS), Smart Cities, and the like) and their respective demands for novel network services. Such networked applications bring massive amounts of traffic data to be processed in real-time, thus driving researchers to develop affordable yet efficient network management systems. In fact, new paradigms, services, and architectures, such as Network Virtualization (NV), Software-Defined Networking (SDN), Distributed Cloud Computing, Network Functions Virtualization (NFV), Service Function Chaining (SFC), etc, will require robust and dynamic capabilities to support a myriad of possibilities for applications from the IoE and XaaS concepts. For example, there is a need for an in-depth understanding of the composition and the dynamics of Internet traffic to perform accurate capacity planning, deploy efficient management policies and pricing strategies, assess protocol performance, and detect abnormalities in such scenarios. Research on measurement, modeling, and analysis of network traffic and infrastructure always face new challenges as new applications are continuously deployed. In this talk, I will discuss the rise of IoE and XaaS as well as the demand for advanced networking services, paradigms, and architectures (e.g., SDN, NFV). I will give an overview of some challenges, opportunities, and directions in these research topics.

1. 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

2. 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

3. Technical Background

4. Network Virtualization
Advanced Networking Architectures

5. 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.)

6. NV: concepts

7. Business Model for NV

8. Business Models for Wireless Network Virtualization

9. NV: Mapping problem

10. Software-defined networking
Advanced Networking Architectures

11. 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

12. Motivation: economics

13. 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

14. 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

15. 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

16. SDN Architecture

18. 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”

20. 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

21. 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!

22. 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

23. Internet2 SDN use case

24. Internet2 SDN infrastructure

25. 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)

26. SDN use case: Virtual Cloud

27. SDN IN THE MOBILE CORE

28. Network Functions Virtualization (NFV)
Service Function Chaining (SFC)
Advanced Networking Architectures

29. 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)

32. 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

33. 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

34. 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

35. 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

36. 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)

37. 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

38. Internet of Everything (IoE)
Advanced Scenarios

39. Let there be IoT

40. Let there be IoE/IoA “bringing together
people, process, data,
and things to make
networked connections
more valuable” (Cisco)

41. Everything as a Service (XaaS)
Advanced Scenarios

42. 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

43. Smart Cities and Urban Services
Scenarios for IoT/IoE

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

47. We are virtualizing every concentric circle in this figure!

48. Things get complicated when we think in a larger scale

49. Things get complicated when we think in a larger scale

50. Measurements and Analysis
Formulation of Optimization Problems
Dependability Analysis
Applied Game Theory
Control Theory
…
Supporting Tools and Techniques

51. 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

52. Measurements: Packets

53. Measurements: Flows
Sampling
Technique
Flow
Monitoring
Tool
F4
F3
F2
F1
F4
F3
Representative
flow sample
Collected,
classifiedflows
NetworkPackets
Flow
Collector
Router: flow
building
Collector: flow
storage
31 2 4
GUI: flow analysis
and reporting
5
On-line sampling Off-line sampling
Traffic Management
and Analysis
Live Network

54. 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

55. 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

56. Packet trace

57. Flow records

58. 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

59. Research Challenges And
Opportunities

60. 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

61. 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

62. 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
62

63. 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 commodity
platforms
Update of app
signatures database
• Encrypted traffic is
not possible
• Analysis of packet
payload forbidden in a
number of countries

64. 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

65. 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

66. RCO #4 - SDN/NFV
 XaaS on top of SDN/Network Virtualization
Infrastructures

67. 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)

68. 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

69. 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

71. My Research Agenda

72. 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

73. 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

74. 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

75. 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

76. 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

77. 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

78. Center For Informatics (CIn)
Federal University Of Pernambuco (UFPE)
Recife, Brazil
About

79. 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 America
Recognition
• ~100 PhD professors
• ~25% BR Research ChairsFaculty
• Computer Science, Computer Engineering,
Information SystemsPrograms

80. 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

82. Recife, Pernambuco, Brazil