research challenges and opportunities in the era of the internet of everything and everything as a...
TRANSCRIPT
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
62
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