nfv hanover zhaozhongliang v2 - wordpress.comoai)! ! full)softwarembased)networkfunctions...

Zhongliang Zhao, Andre Gomes, Eryk Schiller, Torsten Braunwith related MCN partners

Hanover, 27.10.2015

NFV Workshop 2015@Hanover

EU FP7 Mobile Cloud Networking Project

© 2012-2015 MCN. All rights reserved. /

n Motivationsn Introduction to the MCN project (architecture, services)n Radio Access Networks as a Service (RANaaS)n Information Centric Network as a Service (ICNaaS)n Mobility Prediction as a Service (MOBaaS)n Conclusions

Outline


Motivations

n Large growth in mobile traffic challenges traditional telecom network architecturesn Video, data traffics require high bandwidthn Static architectures are inflexible, costly, slow to innovate

n Today MNOs mostly providen Traditional connectivity & voice servicesn Infrastructure sharing, pre-sized, pre-provisioned, pre-customized

n Tomorrow MNOs adopt IaaS/PaaS for network functionsn On-demand, Elastic, Pay-as-you-gon Reduces costs (move from CAPEX to OPEX)n Enables seamless integration of Mobile Networks + Cloud Computing + Storage

n Solution: Bring SDN+NFV to Telco Industry



Outline


Mobile Cloud Networking (MCN)

Moving Cloud Computing beyond Data Centers towards the Air Interface and Mobile End Users

n High level objectivesn Develop a novel “mobile network” architecture, using proof-of-concept prototypes, to lead the way from current mobile networks to a fully cloudified, on-demand, and elastic provisioning of novel mobile service


MCN Architecture


Service Lifecycle


Key Service Components

n Service Manager (SM)n Provides external interface to the usern Manages the SO of every tenantn Receives request for new tenant service instances

n Service Orchestrator (SO)n Orchestration of a service instancen Manages and abstracts underlying resources and service instancen “Runtime & Management” step of the service lifecyclen Monitors application specific metrics and scales

n Cloud Controller (CC)n Supports the deployment, provisioning, and disposal of servicesn Manages the lifecycle of a tenant service instancen Access to support/atomic service, configures atomic services


Key Components Overview i


Key Components Overview ii


MCN and NFV Mapping i


MCN and NFV Mapping ii


MCN NFV Scope (11.2012) & Applicable NFV Use Cases (10.2013)



Outline


Radio Access Network-aaS (RANaaS)

n Motivations: Today’s RANs face problems ofn Increasing capacity needs vs. falling revenues per user.n Large base stations deployments of high costs (CAPEX/OPEX) and power consumption.

n Resources dimensioned for peak traffic loads.n Large fiber connection availability in urban areas.

[GSMA, The Mobile Economy 2013]


Today’s 4G LTE Network

n All components are implemented by dedicated hardware platformn Expensiven Hard to configure and upgrade


Base Station: RRH + BBU

n In Centralized-RAN, base stations are split into:n Remote Radio Head (RRH),n Base Band Unit (BBU).

n RRHs and BBUs are linked by optical cablesn Aims fast deployments.

BBUs

RRHs


Cloud-RAN

n Currently, BBUs run on custom dedicated hardware platforms.

n RANaaS aims to extend C-RAN by:n Taking advantage of general purpose platforms.n Integrating virtualisation and the cloud paradigms.n Using shared computation, storage and networking resources of cloud infrastructures.

BBUs

RRHs


A key element: Software based eNB

n A commodity hardware computer with RAN software and RF equipment

n OpenAirInterface (OAI)n www.openairinterface.orgn Full software-based network functionsspanning all the network layers

n Open source from project partner

computer RF card


OAI LTE Virtual eNB and EPC


C-RAN in the Data Centre

: Server: Virtual Machine

: BBU

: Active RRH: Inactive RRH

Caption:

n RRH are connected with the BBUs in the Data Centre.n With few users, 3 RRH-BBU pairs cover the service arean With more users, more RRHs will be activated and extra BBUs will be instantiated on demand

BBU-Pool (in a Data Centre)

BBU Plane

End-user

Plane

RRH Plane

Switch


RANaaS Prototype

Based on a Heat orchestration Template (HoT), Heat orchestrator uses OpenStackcontrol plane to build Heat stack, a composite environment (web services, HSS, EPC and eNB).

LXC provides a VE in OpenStack. environment.


BBU Profiling Evaluations

n Physical Layer is the most deadline criticaln BBU processing requirements in a virtualized environment (processing time of radio frames < 3 ms)

n Evaluated Environments for BBU Profilingn GPP/LXC/KVM/Public-Private Cloud

n Downlink(Transmission) / Uplink(Reception)


RANaaS Conclusions

n RANaaS aims to integrate virtualisation and cloud paradigms.

n C-RAN software-based base-stations (BBUs) have critical processing requirements.

n The feasibility to run a single BBU on a GPP platform was evaluated using OAI LTE base station emulator:

n A prototype of RANaaS was presented, based on Heat, OpenStack and Linux Containers.



Outline


Information Centric Network-aaS(ICNaas): Follow Me Cloudn Motivations• Bandwidth usage is high and mobile users are dynamic• Follow-Me-Cloud concept has a deeper integration and may bring additional improvements• More information about access networks (e.g., user mobility, bandwidth usage, etc.)

• Greater granularity on decision making (up to small groups of users)

• Integration with ICN/CCN enables greater proximity of caching and repository migrations


Follow-Me Cloud (FMC): Architecture


n Key Conceptsn When users move, content moves with them to the new locationn Such principle may also be applied to service instancesn Monitoring can trigger content relocationn Proactive content relocation is the most desired scenarios– Mobility prediciton based– Not always accurate

n Challengesn How can resources be virtualized to allow such relocation?n What’s the role of caching?n What to relocate?n When should relocation be performed? Under which conditions?

Follow-Me Cloud


n Gather Information at the FMC Controllern Mobility prediction and detectionn Content popularity over time intervals, cache hit ratios, etcn Content availability at the networksn Resources availability (compute power, storage, etc)

n Decision Makingn Decide IF migration should occur, WHERE to and WHAT content should be transferred at WHEN.

n Goals: improve content access times, reduce bandwidth usage and minimize migration costs.

n Perform Content Migrationn Use the ICN/CCN protocols to perform content migrationn Selective sync of repositories based on decisions from the FMC controller

Follow-Me Cloud: Objectives


n MADM: Multiple-Attribute Decision-Makingn Not tied to specific problemsn Supports multiple weighted attributen Finite number of solutions for problems which involve selection/assessment

n Clost to ideal solutions, as ideal are usually not obtained:

n Attributen Content popularity, content sizen User mobility prediction information/accuracyn Free storage space at possible destinationsn Relative size of mobility group

Decision-Making Methods


ICN/FMC Conclusions

• Follow-Me Cloud brings benefits to very dynamic mobile networks

• MCDM methods can be applied to the problem if deciding where to relocate content.

• It is possible to obtain a very good decision without much information and complexity.

• Cost of content migration is important for the networks, but tradeoff with benefit must be carefully analyzed.



Outline


Mobility Prediction-aaS (MOBaas)

n Motivations• Traffic redirection in EPCaaS to optimize bandwidth usage• Content relocation in ICNaaS to optimize content migration


Implementation Architecture


Run-time Prediction Procedure


Mobility Prediction Algorithm I

• Dynamic Bayesian Networks-based mobility prediction

• Conditional distribution of the next cell of a user comprises location and time dependent distributions

• current cell ( )• movement time ( )• movement date ( )• n is the time step (in minutes) of a day

CiTnDl

P( c(n+1) = ci,| c(n) = ci,T (n) = Tn,D = Dl ) =

β ×P( c(n+1) = ci,| c(n) = ci )

location dependency

+ (1−β)×P( c(n+1) = ci,|T (n) = Tn,D = Dl )

time dependency


Mobility Prediction Algorithm II

• Location dependency is a first-order Markov Chain• Encodes the transition probability among locations (cells)• Calculate the probability of moving from to by counting the number of transitions among cells from the trace file

• Weekday/weekend• Same probability of moving from one cell to two neighbors

• Integrate time stamp (the time when a user is in the cell) and CellID to drive the M.C. states. State includes (Time, CellID)

C(n) =Ci C(n+1) =Ci,


Mobility Prediction Algorithm III

• Modified DBN model for mobility prediction algorithm

• Updated conditional distribution of the next cell

• Second part is day-dependency, which expresses the transition probability to next cell given a specific day• Derived by averaging the number of transitions to a specific cell• Weekday/weekend

P( c(n+1) = ci,| c(n) = ci,T (n) = Tn,D = Dl ) =

β ×P(c(n+1) = ci,| c(n) = ci, T (n) = Tn )+ (1−β)×P(c(n+1) = ci, |D = Dl )

⇒


Algorithm Evaluation

• Nokia Data Traces• 100 users over 2-6 months, Switzerland• 30% for testing and evaluate the accuracy• 70% for learning to derive the M.C. states and calculate the M.C. state transition probability matric

• e.g., a trace of 2.5 months includes 10 Monday, we use the first 7 for learning, the last 3 for testing

• Parameters and Metrics• Randomly select 50 states out of the M.C. states derived for each particular day of a week (MON/TUE/…/SUN)

• Prediction in next 20 minutes• Mean absolute error (MAE) in which define the predicted and actual probability

MAE = 1n

| Pi ,L − Pi ,T |, n = 50i=1

n

∑Pi ,L ,Pi ,T


Evaluation Results:Overall Accuracy of 100 Users

• Prediction accuracy = (1 - MAE) x 100

34%User ID: 6026

73%User ID: 5960


Evaluation Results:Quality of Movement Traces

User ID:6026

User ID:5960


Evaluation Results:Accuracy of Specific User

User ID: 6026User ID: 5960


MOBaaS Conclusions

• Trace quality has significant impact on prediction accuracy

• Prediction accuracy result is better than NOKIA paper (DBN-based model best accuracy is around 52%)• Improved DBN model, which integrates time-step with Cell-ID to drive a new M.C. states

• Weekday is easier to be predicted than weekend• Because it’s weekday?


Final Remarks

• MCN = Mobile Networks + Cloud Computing• It brings the concepts of on-demand, elastic, and pay-as-you-go into Telco industry.

• RANaaS aims to integrate virtualization and cloud paradigms to provide a Cloud-RAN, which can reduces cost and provides service flexibly.

• ICNaaS/FMC facilitates the migration of content caches located at the edge of mobile network.

• MOBaaS provides user movement prediction on demand, which can be used to optimize bandwidth usage and content relocation.


Thanks For Your Attention!http://cds.unibe.ch

Project duration: 1-11-2012 / 31-10-2015 (extended to 31-4-2016) Project budget: 15.7 M-€ / 10.5 M-€ funding Number of person/months: 1408 person monthsDetails: https://www.mobile-cloud-networking.eu

nfv hanover zhaozhongliang v2 - wordpress.comoai)! ! full)softwarembased)networkfunctions...

Documents