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NFV and SDN in Future Carrier Networks
Future Architectures for Resource Orchestration (FARO)
Riccardo Guerzoni, Zoran Despotovic,
Riccardo Trivisonno, Ishan Vaishnavi, Artur Hecker, Sergio Beker
CRI/ERC/FCN
ETSI workshop on Future Networks
Sophia Antipolis, 9-11 April 2013
NFV: Telco Operators
wants to lower TCO
running network
functions on general
purpose HW [1]
Telco+IT integration:
SDN will be the big
pipe of the Core
Telecom infrastructure Data Center
Edge Data Center
Future Carrier Networks
Data Center
Edge Data Center
SGSN
Edge Data Center
Data Center
Networks federation:
Telcos cannot cover all the
possible access techniques
without incurring in
prohibitive operational costs
OTT vs. Telco: how to
make sure that
communication services
(Telco) and the content
services (OTT) have a
viable and collaborative
revenue model?
Internet of Things:
Telcos need to manage
the explosion of D2D
communication (10N
devices in 2020).
BRAS
IMS
MME
RNC
AS AS
Several challenges push the Telco Operators towards a new paradigm How to evolve this patchwork into a coherent framework?
Orchestrate all these various services and fit
optimally onto the available infrastructure
Our Goal №1:
Increase Utilization Ratio of Physical Resources in Each
Operator Network
Good news: All physical resources are programmable
and expose suitable APIs (openness), which make the orchestration possible!
Orchestration:
automatic integration/replacement of physical infrastructure
Operating System
CPU Disk NIC Printer Memory
Hardware
Driver Driver Driver Scheduling
Virtual memory
Computer
Radio Access DC Network SAN M2M
Physical resources
OCCI OF iSCSI RA Virt. ?
Operator Orchestration
OpenStack
It is time for a systematic approach to networks orchestration
These (and other) interfaces enable automation and
seamless integration of the Cloud Infrastructure!
Our Goal №2: Common abstraction of resources to enable
Automatic integration of infrastructures
Brokerage: Span multiple PIPs and improve
resource utilization even further
Our Goal №3:
Enable brokerage of resources
There are service request that no single Physical Infrastructure Provider (PIP) can satisfy Example: Ubiquitous localization service (indoor vs. outdoor, GPS vs. triangulation)
One area covered by Kabel Deutschland, another by Cablesurf.de
Services can be cheaper if combined from multiple PIPs Larger range of possibilities
Spare resources of each PIP (even after internal orchestration) can be used further
Enable resource integration across PIPs borders
Virtual Resources
Hypervisors/
Controllers
Virtual Resources
Hypervisors/
Controllers
PIP(s) Orchestration PIP(s) Orchestration
Mediated access to APIs Broker-Virtual. i/f
Resources Control
Service
Provider
Introducing Our Solution
FARO (Future Architecture for Resources Orchestration)
Broker Orchestration
PIP(s) Orchestration
Virtual Resources
Hypervisors/
Controllers
SP –Broker
Negotiation
i/f
Bro
ke
r-P
IP
Neg
otia
tio
n
i/f
PIP-Virtual.
Resources Mgmt
i/f
Embedding
Embedding
SP-Virtual. i/f
Resources Control
Note: PIPs, Brokers and Service Providers could be different departments of the same Network Operator; this eco-system does not necessarily imply new industry players
Physical Infrastructure Orchestration: efficient infrastructure at competitive price, diversify (big data centers, access technologies, edge data center, devices)
Broker Orchestration: federate, enable business, offer technological and operational flexibility
Service Providers: do new things, use the infrastructure to provide always innovative services
Orchestrator(s) inside an evolved eco-system
Orchestrating should involve 2 classes of challenges:
Technical aspects: embed services and network functions into physical/virtual infrastructures
Economical aspects: automate SLA vs. price negotiation
Resources Publication New Platform request
(service graph) Embedding
and negotiation Access to Resources Proactive Monitoring and SLA automation
PIP1 PIP2
PIP3
Broker DB
PIP1 PIP2
PIP3
Broker
DB
SP1
PIP1 PIP2
PIP3
Broker
SP1
DB
PIP2 PIP3
SP1
PIP1 API API PIP2
SP1
PIP1 API
Broker
MON
MON
PIP3
API
algo SLA
Use case: Network Functions Virtualization
Embedding Carrier grade Network Elements over Cloud Computing Technologies is a significant
embodiment of cloud network orchestration:
Embed network elements as service graphs, maximizing the utilization of the infrastructure;
Migrate network elements or, better, components of network elements; e.g. migrate instances of the
GTP-U and PMPI components and the related switching capacity to a peripheral small data center to
offload the User Plane.
S-GW
GTP-
C
BBERF
GTP-
U PMIP
Mobility
Anchor
Loc B Loc D
National Data Center (Loc A)
Edge Data Center (loc D)
Loc E
GTP-
U PMIP
Loc C
Loc D Loc A
Connectivity: BW, latency
Connectivity: switching capacity
Computational capacity (CPU, RAM, …)
Storage capacity
GTP-
U PMIP
Loc D
Cloud resources publication
The Orchestrator interacts with distributed controllers\hypervisors. Each controller, belonging to a PIP: publishes resources; receives provisioning requests.
The definition of standard interfaces to expose virtual resources is a key topic to enable cloud orchestration. The information model should expose only the relevant characteristics of the offered infrastructure: Resource capacity (connectivity: BW, latency …; IT resources: computational capacity, interfaces, storage capacity …) SLA Data Policies *2+ (performance, preservation, uptime guarantee …) SLA Business Level Policies [2] (price, withdrawal conditions, compensation, …)
Powered down
OS
VM OS OS
Phys
node
Phys
node
OS
VM
Infrastructure
Controller
Physical
node
Physical
node Phys
node
Phys
node
Offered
resources
DB
Standard interface to Orchestrator
WAN
Controller
Standard interface to Orchestrator
OS OS
Phys
node
Phys
node
Infrastructure
Controller
OS OS OS OS
Phys
node
Phys
node
Phys
node
Phys
node
OS
Phys
node
Standard interface to Orchestrator
OS
VM
OS
VM
Physical
node
Offered
resources
DB
Offered
resources
DB
Embedding algorithms
The embedding is performed at different levels by Brokers and PIPs. It consists in translating service requirements into resources allocation, dynamically re-evaluating the allocation as a consequence of: new incoming requests changes in the costs of the infrastructure evolution of the offered infrastructure monitoring reports about SLA fulfillment
Examples of embedding algorithm in [3] and scheduling graphs in [4].
For this purpose, the information model must support: Different level of abstraction (physical -> virtual -> service graph)
Resources elasticity, to offer short term expansion capabilities
Resources granularity, to ensure efficient allocation
Examples of IaaS description languages: OCCI [5] and VXDL [6]. OVF for the description of virtual appliances.
The information model should interface to OpenStack Compute, Storage and Network modules
and proprietary IaaS frameworks (need to consider DMTF CIM standards).
A
B D
C E
Service/ applications graph
Virtual resources graph
B D
C E
A
B D
C E
A
Physical resources graph
Application embedding
PIP embedding
Access to cloud resources
Powered down
Middleware NE1
OS OS OS
VM VM VM OS OS
Phys
node
Phys
node
OS
VM
IT MW
Controller
Physical
node
Physical
node
Physical
node Phys
node
Powered down
Middleware NE1
OS OS OS
VM VM VM OS OS
Phys
node
Phys
node
OS
VM
IT MW
Controller
Physical
node
Physical
node
Physical
node Phys
node
Phys
node
WAN
Controller
MW NE1
NE1 Application
NE1 Application
NE1 Application
OS OS
Phys
node
Phys
node
IT MW
Controller
OS OS OS OS
Phys
node
Phys
node
Phys
node
Phys
node
OS
Phys
node
Any virtualization controller/hypervisor should disclose standard APIs to allow E2E provisioning of resources to Service Providers.
API
To Service Provider
API
To SP
API
To Service Provider
API
To SP
PIP Orchestration
Broker Orchestration
SP PIP Hypervisors
API
Instantiation
Resources request Service Graph
Access Info
Access Management
Broker
Broker
Broker
Broker
OS OS OS
VM VM VM
OS
VM
Physical
node
Physical
node
OS
VM
OS
VM
Physical
node
Monitoring
Virtualization
Server
Soft Data Plane
Controller
Regional
Data Center
Server
Soft Data Plane
FCN National
Data Center
Core Core Core
Co
ntr
ol P
lan
e
WC
DM
A
Co
ntr
ol
Pla
ne
LT
E C
on
tro
l Pla
ne
G
SM
Use
r P
lan
e
WC
DM
A
Use
r P
lan
e
LTE
Use
r P
lan
e
GSM
Switches Server
IT Data Center
Wireless Core Core
Secu
rity
Au
then
tica
tio
n
…
Controller Controller Controller
De-coupling the infrastructure from the service has evident drawbacks in terms of traceability of the root causes in case of performance degradations; in order to correlate SLA breaches to the performance of the underlying infrastructure(s), the virtual resources should report standardized measurements records [7]. This result can be achieved by embedding in each controller/hypervisor a standardized monitoring server.
The figure shows a possible distribution of monitoring servers: Telco Protocols performance over IaaS Physical/Virtual resource allocation and availability
Conclusions
Approaching NFV challenges from FARO perspective establishes concrete requirements for the
Orchestration platform.
The requirements can be grouped in the following areas of work:
Interfaces standardization: resource publication, resources access, resources monitoring;
Embedding algorithms: map service graphs issued by Service Providers and Brokers into virtual
infrastructures offered by multiple parties (PIPs);
Definition of a multi-layer resources and service graphs description framework, enabling SLA
automation;
Technology evolution: development of inter-operable virtualization techniques, enabling
migration of services/functions through WANs [8].
References
*1+ “Network Functions Virtualisation - An Introduction, Benefits, Enablers, Challenges & Call for Action”, NFV Industry Specification Group (ISG) in ETSI, October 22-24, 2012 at the “SDN and OpenFlow World Congress”, Darmstadt-Germany [2] Practical Guide to Cloud Service Level Agreements, CSCC (Cloud Standards Customer Council), April 2012 [3] Chowdhury, M.; Rahman, M.R.; Boutaba, R., "ViNEYard: Virtual Network Embedding Algorithms With Coordinated Node and Link Mapping," Networking, IEEE/ACM Transactions on , vol.20, no.1, pp.206,219, Feb. 2012 [4] Bittencourt, Luiz F., Edmundo RM Madeira, and Nelson LS Da Fonseca. "Scheduling in hybrid clouds." Communications Magazine, IEEE 50.9 (2012): 42-47. [5] Metsch, Thijs, and Andy Edmonds. "Open Cloud Computing Interface–Infrastructure,”." Standards Track, no. GFD-R in The Open Grid Forum Document Series, Open Cloud Computing Interface (OCCI) Working Group, Muncie (IN). 2010. [6] Koslovski, Guilherme Piegas, Pascale Vicat-Blanc Primet, and Andrea Schwertner Charao. "VXDL: Virtual resources and interconnection networks description language." Networks for Grid Applications (2009): 138-154. *7+ Guerzoni, Fontana, Beker, Soldani, “A User Centric Troubleshooting Framework for Current and Future Networks”, Wireless World Research Forum Meeting 30, April 2013, Oulu-Finland [8] Wood, Timothy, et al. "Cloudnet: A platform for optimized wan migration of virtual machines." University of Massachusetts Technical Report TR-2010-002 (2010).
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