Βελτιστοποίηση και Διαχείριση Οπτικών Δικτύων
και Υπολογιστικών Συστημάτων
καθ. Μάνος Βαρβαρίγος, ΣΗΜΜΥ, ΕΜΠ
Planning, Management and Optimization of
Systems and Optical Networks
Prof. Manos Varvarigos, NTUA
Εισαγωγή

6.4 Billion connected "Things" will be in use in 2016, up 30 Percent From
2015, according to Gartner

Global IP traffic will grow at a compound annual growth rate (CAGR) of 23
percent from 2014 to 2019, according to CISCO’s Visual Networking Index

The continuous growth of consumers’ IP traffic causes tremendous
pressure on network and system infrastructures

Until recently only systems were able to be monitored, managed, while
optimizing their performance in real-time

Core networks on the other hand required careful planning and
provisioning, since dynamic changes were not possible, requiring weeks
and even months of manual labour for a change (e.g. network upgrade,
setup a new lightpath) to take effect.

Software Defined Networking

Software Defined Networking (SDN) transforms network devices to remotely programmable
forwarding elements, allowing the separation of the forwarding decisions (control plane) from the
devices themselves (forwarding plane).

SDN enables network programmability, so that networks can be planned, operated and managed
cheaply, flexibly and on demand

The forwarding decisions are taken in a central entity, giving it control for adapting the transfers
based on the network usage/characteristics and application characteristics
Application layer
Applications
Applications
API
Control layer
Infrastructure layer
Applications
API
SDN Controller
Network device
Network device
Network device
Network device
Network device
Unified resources’ management

Software Defined Networking (SDN) is currently considered the key enabler for future
optical and IP networks, for the Internet of Things (IoT), for mobile and wireless
networks

SDN will complement traditional system monitoring and management protocols,
frameworks, methodologies towards a unified resources’ management environment

In this way resources, ranging from IoT devices, to servers, storage systems (both
physical and virtual) and networks can be monitored, managed and optimized

Through SDN resources can also be sliced up, creating isolated virtual networks and
systems, which can be managed and optimized independently of the physical
infrastructure, by users, companies, etc
Monocrat & Mantis

Our team develops Monocrat and Mantis

Monocrat is a client management software for the management of IoT and
(physical & virtual) systems (servers, desktops, printers, mobile devices)

Mantis is a network planning and operation tool for multi-layer IP/Optical
networks
Monocrat
Monocrat provides a unified resources’ management interface
Performing management
operations and analytics
a plethora of IT resources
Monocrat








Discovery - resources
Inventory - hw, sw, configurations
Monitoring - cpu, memory, storage, network
Management - managerial operations
Software - install, uninstall, patch, metering
Alarms - on predefined events
Cost of resources – VM resource utilization-related cost
User Analytics & Performance Statistics
8
Monocrat
Provides Common UI interfaces independently of the way
operations & analytics are actually performed on the various
resources
9
Mantis

We are building Mantis, a complete and thorough network orchestration (planning
and operation) platform for the Optical and the IP layer

Mantis will be the logic (algorithms and software) for the future Software Defined
Networks (SDN) at both Physical & IP layers (playing the joint role of layer-0 and
layer-3 PCE)

Mantis considers in a holistic manner the Optical and IP layers, performing multilayer
optimization, multilayer protection and restoration
Mantis – Planning and Operation


Mantis operates in the network planning and the network operation mode

Network planning typically occurs before a network is deployed

Network operation involves the incremental processing of demands, one or a set at a time, or the
adaptation to traffic variations
Mantis can be used to examine online planning and operation what-if scenarios or be interfaced to
the control plane to curry out the taken decisions
Mantis: the SDN logic of the Optical Network
For Mantis, SDN (software defined networking) is an “enabling technology”:
-
Mantis provides network optimization intelligence behind any network
orchestration-control platform (opendaylight, Netconf, tailf, GMPLS, etc)
and network segment (core, metro, datacenter, submarine)
-
Takes advantage of the momentums (reflected by the expected market
growths) towards SDN, optical metro, multilayer, network-as-a-service in
WAN, and flex-grid
-
Provides a more flexible, dynamic and intelligent network than we have
nowadays, squeezing the most out of its resources
Mantis: the SDN logic of the Optical Network
Mantis can be interfaced to

the Network Management System of the optical network, to be the logic of the optical network

to SDN controller (or other centralized control entity), which will pass Mantis’ decisions to the IP
edges of the optical network
Meeting the Dual Transition
(SDN programmability & Optical Flexibility)

The effort currently spent by the Industry on Software Defined Networks (SDN) has
focused on the way the planning and operation decisions are implemented, basically
introducing new Network Operating Systems (NOS) and High Level Network
Operating/Programming Languages

Mantis focus is on how these planning and operation decisions are taken so as to
optimize network CAPEX&OPEX. Based on the SDN capabilities we provide the actual
programming (software) of the network so that it is adjustable to traffic changes, faults,
and application demands, while decreasing complexity for the Telecom Operators

Mantis is the “optimization logic” (algorithms and software) of SDN and not a new Network
Operating System (NOS) or a High Level Network Language

Elastic networks enable a fine-granular, adjustable, cost- and power- efficient transport
able to carry a wide range of capacities, that can vary in real time
Elastic Optical Networks
Flex-grid technology
• 12.5 GHz slots
• Slot concatenation
 Sub- and super-wavelength
granularity
With existing fixed ITU grid (DWDM networks) 400
Gb/s or higher cannot be supported
Tunable (programmable) Transceivers
also referred to as Bandwidth Variable Transceivers (BVT)
• Modulation scheme
• FEC
• Symbol rate
• Spectrum slots used
• ….
 Operation tailored to needs, convenient for
offline planning, even better for dynamic
network operation
Elastic optical networking – Trend or reality

Flex-grid 12.5 GHz spectrum slots standardized by ITU (G.694.1)

Flex-grid switches: subsystems are available

Flexible transceivers: multi-format and multi-baudrate transceivers are available


Nokia PSE-2 (Photonic Service Engine version 2) programmable chipset

Able to adapt the transmission characteristics (rate, bandwidth, etc)

Physical layer model (e.g. GN model) plays a crucial role
Replace DWDM networks (current practice in transport) in the next 5 years

Will find applications in other networks: MAN, access, inter- and intra-Datacenter
Ciena’s
flexible TxRX
prototype
Finisar’s WaveShaper switch
Advantages of elastic optical networking

Planning (without considering network adaptability): >30%
improvement in spectrum utilization when compared to DWDM
systems

Enables programmability of the optical layer

Not present in currently employed DWDM systems

Optical transponders can be tuned to follow IP-layer traffic
fluctuations

Suits well with Software Define Networking (SDN) concept

Dynamic optical network adaptation: the advantages can be
enormous

The fine granularity (close to the demand of individual users) will
eventually lead to the convergence of core/metro/access segments
Mantis optical layer algorithms






Routing, Modulation Level and
Spectrum Allocation (RSA or
RMLSA)
(path, slot allocation, transmission
parameters of the transponders)
Spectrum De-Fragmentation and
re-optimization
Connection(s) re-configuration to
absorb traffic variations
Problems considered at the optical
or jointly at the optical and IPedges
New direction: Interface with
optical monitoring plane to close
the control loop and target selfadaptation
16 QAM
32 QAM
32 QAM
time

Spectrum variable (nonconstant) connections
Tunable transponders (more in
the next slide)
Optimization problems
Spectrum
Fragmentation

Physical layer and tunable transponders

Account for the physical layer and the tunability of
transponders

Exploit multiple dimensions

Modulation format: QPSK, 16QAM etc

Baudrate

FEC

Guardband left from adjacent connections

The path, spectrum allocation

Mantis takes into account the physical layer through
the GN model

but also Mantis considers the IP edge (next slides) to
enable a true multi- and cross-layer optimization
Modeling transponders tunability,
accounting for the physical layer
The consideration of all these dimensions, the cross-layer optimization,
combined with the dynamic network operation (time dimension) can yield
superior resource efficiency and optimal network utilization
Multi-layer Networking

Optical network + IP
edges (IP/MPLS, MPLSTP, OTN)

End-to-end provisioning
requires control
operations at both IP and
optical layers

SDN or MPLS/GMPLS
controlled jointly or with
different protocols

Consideration of
optimization problems
jointly in both layer yields
superior performance
and CAPEX/OPEX
savings

Creates Virtual Networks
Mantis - GUI
Managing configurations
•
•
•
•
Mantis comes with a user-friendly GUI
Users create network configurations that include
the topology, traffic matrix, node architecture,
number of wavelengths or spectrum slots, type of
fiber and equipment, fiber parameters, etc
According to the specific networking scenario the
user selects the algorithm
After the algorithm is finished, the user can view
the outcome
Topology manipulation
Equipment description
Results
Mantis Software


Mantis software comes in three forms

Desktop (standalone) application

Cloud service (SaaS)

PCE
As a cloud service it achieves speed-up by running computationally intensive
what-if scenarios in parallel in the cloud

currently supports Amazon Web Services (AWS) and private resources based on
OpenStack

Mantis employs efficient and innovative heuristics, since most problems are NPComplete
Thank you !
Future steps