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Toward Superfluid Deployment of Virtual Functions:
Exploiting Mobile Edge Computing for Video Streaming
Stefano Salsano(1), Luca Chiaraviglio(1), Nicola Blefari-Melazzi(1), Carlos Parada(2),
Francisco Fontes(2), Rufael Mekuria(3), Dirk Griffioen(3)
(1)CNIT / Univ. of Rome Tor Vergata, Italy; (2) Altice Labs, Portugal; (3) Unified Streaming, Netherlands
Soft5 Workshop - First International Workshop on Softwarized Infrastructures for 5G
and Fog Computing, in conjunction with ITC 29
Genoa, Italy - 8th September, 2017
A super-fluid, cloud-native, converged edge system
Outline
• Superfluidity project: goals and architecture
• Mobile Edge Computing (MEC)
• Video Streaming with Late Transmuxing (LTM)
• Combined MEC/LTM Video streaming testbed
2
Superfluidity project
Superfluidity Goals
• Instantiate network functions and services on-the-fly
• Run them anywhere in the network (core, aggregation, edge), across
heterogeneous infrastructure environments (computing and networking), taking
advantage of specific hardware features, such as high performance accelerators,
when available
Superfluidity Approach
• Decomposition of network components and services into elementary and
reusable primitives (“Reusable Functional Blocks – RFBs”)
• Platform-independent abstractions, permitting reuse of network functions
across heterogeneous hardware platforms
3
The Superfluidity vision
4
Current NFV
technology
Granularity
Time scale
Superfluid
NFV
technology
Days, Hours Minutes Seconds Milliseconds
Big VMs
Small
components
Micro
operations • From VNF
Virtual Network Functions
to RFB
Reusable Functional Blocks
• Heterogeneous RFB execution
environments
- Hypervisors
- Modular routers
- Packet processors
…
Heterogeneous composition/execution environments
5
• Classical NFV environments (i.e. by ETSI NFV standards)
– VNFs are composed/orchestrated to realize Network Services
– VNFs can be decomposed in VNFC (VNF Components)
«Big»
VNF
«Big»
VNF
«Big»
VNF
«Big»
VNF
VNFC
VNFC
VNFC
VM
VM
Heterogeneous composition/execution environments
6
• Towards more «fine-grained» decomposition…
• Modular software routers (e.g. Click)
– Click elements are combined in configurations (Direct Acyclic Graphs)
Heterogeneous composition/execution environments
7
• Towards more «fine-grained» decomposition…
• XSFM-based (eXtended Finite State Machine) decomposition of
traffic forwarding / flow processing tasks, and HW support for
wire speed execution
The Superfluidity Architecture
8
RFB
#a
RFB
#b
RFB
#c
RFB
#n
(node-level) RDCL script
REE
RFB#2
(network-level) RDCL script
REE
Manager
REE User
REE
Resource
Entity
UM API
MR API
REE
User
UM API
REE
Resource
Entity
REE
Manager
(network-wide) REE
RFB Execution Environment
RFB#1
MR API(node-level) REE
RFB Execution Environment
(Towards) Common Abstractions
for Heterogeneous Environments
9
RFB Execution Environment(s) RFBs Description & Composition
Languages (RDCLs) and tools
• “Traditional” NFVI infrastructure
hypervisors with Full VMs
• Unikernel based virtualization
• Software modular routers
environments (e.g. Click)
• Radio processing SW modules
• Hardware packet processors
• ETSI VNF descriptors / NEMO
• MISTRAL – HEAT
• Docker Compose …
• Click configurations / SEFL /
Symnet
• PN (Process Networks), SDF
(Synchronous Data Flow)…
• XFSMs
Working prototype
RDCL 3D: RFB Description and Composition Language Design Deploy and Direct
10
This is a regular
VM (XEN HVM)
These are 3 Unikernel
VMs
(ClickOS)
Outline
• Superfluidity project: goals and architecture
• Mobile Edge Computing (MEC)
• Video Streaming with Late Transmuxing (LTM)
• Combined MEC/LTM Video streaming testbed
11
Mobile Edge Computing (MEC) Basics
12
Datacenter
Core
Central Office
Central Office
Customer
Mobile Edge Computing (MEC) Model
13
MEC Platform
Dedicated/Specific
Hardware
Dedicated/Specific
Hardware
COTS Hardware
Base Band
Unit B (BBU)
Base Band
Unit A (BBU)
Remote Radio
Head A (RRH)
Remote Radio
Head B (RRH)
CPRI
CPRI
(fixed)
Central
Office
DC
API API API API API
MEC-App MEC-App MEC-App
MEC-App MEC-App MEC-App
Core DC
Mobile Edge Computing (MEC) Architecture
14
User Traffic Forwarding
eNB/BBU
User Traffic Forwarding
CORE
(EPC)
MEC TOF
GTP-U
Encap
S/DNAT
Traffic
Filter
(UL)
Router
Traffic
Filter
(DL)
GTP-U
Encap
MEC Host
ME Service
(TOF)
ME Service
(RNIS)
ME Service
(LOC)
ME Service
(DNS)
ME App C
(e.g. Video
Streaming)
ME App B
(e.g. Augmented
Reality)
ME App A
(e.g. IoT)
GTP-U
Decap
GTP-U
Decap
NFVI
User Traffic Forwarding
UserTrafficForwarding
API
(LOC)
API
(DNS)
API
(NIS
API
(TOF)
MEPlatform(BUS)
Applications and Services (AS)
Data Plane Forwarding (DPF)
MEC System level
ME Orchestrator
Virtualized Infrastructure Management
(VIM)
Platform Manager
ME App A
Manager
ME App B
Manager
ME App C
Manager
Rep
MEC Edge level
Management and Orchestration (MO)
Network scenario: sharing the HW infrastructure
15
RRHs
RRHs
RRHs
ADSL
Cable
Fiber
Fixed Networks
vDSLAM/vOLT/vCTMS BBUBBU BBU
µDC-PoP
Edge Computing (MEC) Platform
Service Example: Local Offloading of Video Streaming
(with Late Transmuxing)
16
Core DC
MEC Host
Data Plane Data Plane
Traffic
Offloading
Function
BBURRH
Outline
• Superfluidity project: goals and architecture
• Mobile Edge Computing (MEC)
• Video Streaming with Late Transmuxing (LTM)
• Combined MEC/LTM Video streaming testbed
17
Basic Video Streaming deployment
18
JIT
PACKAGER
MPEG-DASH,
HLS, HSS, HSD
CDN
MPEG-DASH,
HLS, HSS, HSD
Media Storage
or Live Encoder
Origin ServerCDN
Proxy Cache
End-user
Devices
Late TransMuxing
19
1. Unified Origin: one ingest format, various output formats
2. Contribution: split and move to the edge
3. How? Use an intermediate optimal media exchange format between core and
edge, cache this format
Late Transmuxing Prototype
RFB based deployment
20
Video
Content
Storage
Nginx
Byte
Range
Cache
Nginx
CDN
Cache
Proxy
Back endEdge end
Manifest
Generation
Apache
Origin
Transmux
App
Manifest
Generation
Apache
Edge
Transmux
App
Multi
bit-rate
Encoder
Front end
Player
page
Outline
• Superfluidity project: goals and architecture
• Mobile Edge Computing (MEC)
• Video Streaming with Late Transmuxing (LTM)
• Combined MEC/LTM Video streaming testbed
21
Prototype architecture
22
UE (Bob)
MEC/Video Streaming Demo
User Traffic Forwarding
eNB/BBU
User Traffic Forwarding
Server
MEC TOF
GTP-U
Encap
S/DNAT
Traffic
Filter
(UL)
Router
Traffic
Filter
(DL)
GTP-U
Encap
GTP-U
Decap
GTP-U
Decap
Data Plane Forwarding
MEC Host
NFVI
User Traffic Forwarding
UserTrafficForwarding
Applications and Services
• The LTM server in the core is initially contacted
CORE
(EPC)
UO
LTM
UO
Storage
MEC Host
NFVI
User Traffic Forwarding
Applications and Services
UserTrafficForwarding
UE (Bob)
MEC/Video Streaming Demo
User Traffic Forwarding
eNB/BBU
User Traffic Forwarding
Server
MEC TOF
GTP-U
Encap
S/DNAT
Traffic
Filter
(UL)
Router
Traffic
Filter
(DL)
GTP-U
Encap
GTP-U
Decap
GTP-U
Decap
Data Plane Forwarding
Unified
Origin
LTM
• The Management and Orchestration (MANO) layer instantiates the LTM
application in the edge (so far, MANO is just a manual script execution)
CORE
(EPC)
UO
LTM
UO
Storage
MEC Management
and Orchestrator
MEC Host
Unified
Origin
LTM
NFVI
User Traffic Forwarding
Applications and Services
UserTrafficForwarding
UE (Bob)
MEC/Video Streaming Demo
User Traffic Forwarding
eNB/BBU
User Traffic Forwarding
Server
MEC TOF
GTP-U
Encap
S/DNAT
Traffic
Filter
(UL)
Router
Traffic
Filter
(DL)
GTP-U
Encap
GTP-U
Decap
GTP-U
Decap
Data Plane Forwarding
• After the LTM instantiation is fully operational, the MANO layer
configures the TOF in order to redirect the video request to the new LTM
CORE
(EPC)
UO
LTM
UO
Storage
MEC Management
and Orchestrator
MEC Host
Unified
Origin
LTM
NFVI
User Traffic Forwarding
Applications and Services
UserTrafficForwarding
UE (Bob)
MEC/Video Streaming Demo
User Traffic Forwarding
eNB/BBU
User Traffic Forwarding
Server
MEC TOF
GTP-U
Encap
S/DNAT
Traffic
Filter
(UL)
Router
Traffic
Filter
(DL)
GTP-U
Encap
GTP-U
Decap
GTP-U
Decap
Data Plane Forwarding
• The subscriber will, transparently, start getting the video service from
the edge LTM, which in turn accesses the central storage for contents
CORE
(EPC)
UO
LTM
UO
Storage
Testbed Results
27
Testbed Results
Deployment time of Edge Video Streaming components
28
Thank you. Questions?
Contacts
Stefano Salsano
University of Rome Tor Vergata / CNIT
stefano.salsano@uniroma2.it
http://superfluidity.eu/
The work presented here only covers a subset of the work performed in the project
29
References
• SUPERFLUIDITY project Home Page http://superfluidity.eu/
• G. Bianchi, et al. “Superfluidity: a flexible functional architecture for 5G networks”, Transactions on
Emerging Telecommunications Technologies 27, no. 9, Sep 2016
• S. Salsano, L. Chiaraviglio, N. Blefari-Melazzi, C. Parada, F. Fontes, R. Mekuria, D. Griffioen,
“Toward Superfluid Deployment of Virtual Functions: Exploiting Mobile Edge Computing for Video
Streaming”, Soft5 Workshop, 1st International Workshop on Softwarized Infrastructures for 5G and
Fog Computing, in conjunction with 29th ITC conference, Genoa, Italy, 8th September 2017
• S. Salsano, F. Lombardo, C. Pisa, P. Greto, N. Blefari-Melazzi,
“RDCL 3D, a Model Agnostic Web Framework for the Design and Composition of NFV Services”,
submitted paper, https://arxiv.org/abs/1702.08242
• L. Chiaraviglio, L. Amorosi, S. Cartolano, N. Blefari-Melazzi, P. Dell’Olmo, M. Shojafar, S. Salsano,
“Optimal Superfluid Management of 5G Networks”,
3rd IEEE Conference on Network Softwarization, NetSoft 2017, 3-7 July 2017, Bologna, Italy
30
The SUPERFLUIDITY project has received funding from the European Union’s Horizon
2020 research and innovation programme under grant agreement No.671566
(Research and Innovation Action).
The information given is the author’s view and does not necessarily represent the view
of the European Commission (EC). No liability is accepted for any use that may be
made of the information contained.
31

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Superfluid Deployment of Virtual Functions: Exploiting Mobile Edge Computing for Video Streaming

  • 1. Toward Superfluid Deployment of Virtual Functions: Exploiting Mobile Edge Computing for Video Streaming Stefano Salsano(1), Luca Chiaraviglio(1), Nicola Blefari-Melazzi(1), Carlos Parada(2), Francisco Fontes(2), Rufael Mekuria(3), Dirk Griffioen(3) (1)CNIT / Univ. of Rome Tor Vergata, Italy; (2) Altice Labs, Portugal; (3) Unified Streaming, Netherlands Soft5 Workshop - First International Workshop on Softwarized Infrastructures for 5G and Fog Computing, in conjunction with ITC 29 Genoa, Italy - 8th September, 2017 A super-fluid, cloud-native, converged edge system
  • 2. Outline • Superfluidity project: goals and architecture • Mobile Edge Computing (MEC) • Video Streaming with Late Transmuxing (LTM) • Combined MEC/LTM Video streaming testbed 2
  • 3. Superfluidity project Superfluidity Goals • Instantiate network functions and services on-the-fly • Run them anywhere in the network (core, aggregation, edge), across heterogeneous infrastructure environments (computing and networking), taking advantage of specific hardware features, such as high performance accelerators, when available Superfluidity Approach • Decomposition of network components and services into elementary and reusable primitives (“Reusable Functional Blocks – RFBs”) • Platform-independent abstractions, permitting reuse of network functions across heterogeneous hardware platforms 3
  • 4. The Superfluidity vision 4 Current NFV technology Granularity Time scale Superfluid NFV technology Days, Hours Minutes Seconds Milliseconds Big VMs Small components Micro operations • From VNF Virtual Network Functions to RFB Reusable Functional Blocks • Heterogeneous RFB execution environments - Hypervisors - Modular routers - Packet processors …
  • 5. Heterogeneous composition/execution environments 5 • Classical NFV environments (i.e. by ETSI NFV standards) – VNFs are composed/orchestrated to realize Network Services – VNFs can be decomposed in VNFC (VNF Components) «Big» VNF «Big» VNF «Big» VNF «Big» VNF VNFC VNFC VNFC VM VM
  • 6. Heterogeneous composition/execution environments 6 • Towards more «fine-grained» decomposition… • Modular software routers (e.g. Click) – Click elements are combined in configurations (Direct Acyclic Graphs)
  • 7. Heterogeneous composition/execution environments 7 • Towards more «fine-grained» decomposition… • XSFM-based (eXtended Finite State Machine) decomposition of traffic forwarding / flow processing tasks, and HW support for wire speed execution
  • 8. The Superfluidity Architecture 8 RFB #a RFB #b RFB #c RFB #n (node-level) RDCL script REE RFB#2 (network-level) RDCL script REE Manager REE User REE Resource Entity UM API MR API REE User UM API REE Resource Entity REE Manager (network-wide) REE RFB Execution Environment RFB#1 MR API(node-level) REE RFB Execution Environment
  • 9. (Towards) Common Abstractions for Heterogeneous Environments 9 RFB Execution Environment(s) RFBs Description & Composition Languages (RDCLs) and tools • “Traditional” NFVI infrastructure hypervisors with Full VMs • Unikernel based virtualization • Software modular routers environments (e.g. Click) • Radio processing SW modules • Hardware packet processors • ETSI VNF descriptors / NEMO • MISTRAL – HEAT • Docker Compose … • Click configurations / SEFL / Symnet • PN (Process Networks), SDF (Synchronous Data Flow)… • XFSMs
  • 10. Working prototype RDCL 3D: RFB Description and Composition Language Design Deploy and Direct 10 This is a regular VM (XEN HVM) These are 3 Unikernel VMs (ClickOS)
  • 11. Outline • Superfluidity project: goals and architecture • Mobile Edge Computing (MEC) • Video Streaming with Late Transmuxing (LTM) • Combined MEC/LTM Video streaming testbed 11
  • 12. Mobile Edge Computing (MEC) Basics 12 Datacenter Core Central Office Central Office Customer
  • 13. Mobile Edge Computing (MEC) Model 13 MEC Platform Dedicated/Specific Hardware Dedicated/Specific Hardware COTS Hardware Base Band Unit B (BBU) Base Band Unit A (BBU) Remote Radio Head A (RRH) Remote Radio Head B (RRH) CPRI CPRI (fixed) Central Office DC API API API API API MEC-App MEC-App MEC-App MEC-App MEC-App MEC-App Core DC
  • 14. Mobile Edge Computing (MEC) Architecture 14 User Traffic Forwarding eNB/BBU User Traffic Forwarding CORE (EPC) MEC TOF GTP-U Encap S/DNAT Traffic Filter (UL) Router Traffic Filter (DL) GTP-U Encap MEC Host ME Service (TOF) ME Service (RNIS) ME Service (LOC) ME Service (DNS) ME App C (e.g. Video Streaming) ME App B (e.g. Augmented Reality) ME App A (e.g. IoT) GTP-U Decap GTP-U Decap NFVI User Traffic Forwarding UserTrafficForwarding API (LOC) API (DNS) API (NIS API (TOF) MEPlatform(BUS) Applications and Services (AS) Data Plane Forwarding (DPF) MEC System level ME Orchestrator Virtualized Infrastructure Management (VIM) Platform Manager ME App A Manager ME App B Manager ME App C Manager Rep MEC Edge level Management and Orchestration (MO)
  • 15. Network scenario: sharing the HW infrastructure 15 RRHs RRHs RRHs ADSL Cable Fiber Fixed Networks vDSLAM/vOLT/vCTMS BBUBBU BBU µDC-PoP Edge Computing (MEC) Platform
  • 16. Service Example: Local Offloading of Video Streaming (with Late Transmuxing) 16 Core DC MEC Host Data Plane Data Plane Traffic Offloading Function BBURRH
  • 17. Outline • Superfluidity project: goals and architecture • Mobile Edge Computing (MEC) • Video Streaming with Late Transmuxing (LTM) • Combined MEC/LTM Video streaming testbed 17
  • 18. Basic Video Streaming deployment 18 JIT PACKAGER MPEG-DASH, HLS, HSS, HSD CDN MPEG-DASH, HLS, HSS, HSD Media Storage or Live Encoder Origin ServerCDN Proxy Cache End-user Devices
  • 19. Late TransMuxing 19 1. Unified Origin: one ingest format, various output formats 2. Contribution: split and move to the edge 3. How? Use an intermediate optimal media exchange format between core and edge, cache this format
  • 20. Late Transmuxing Prototype RFB based deployment 20 Video Content Storage Nginx Byte Range Cache Nginx CDN Cache Proxy Back endEdge end Manifest Generation Apache Origin Transmux App Manifest Generation Apache Edge Transmux App Multi bit-rate Encoder Front end Player page
  • 21. Outline • Superfluidity project: goals and architecture • Mobile Edge Computing (MEC) • Video Streaming with Late Transmuxing (LTM) • Combined MEC/LTM Video streaming testbed 21
  • 23. UE (Bob) MEC/Video Streaming Demo User Traffic Forwarding eNB/BBU User Traffic Forwarding Server MEC TOF GTP-U Encap S/DNAT Traffic Filter (UL) Router Traffic Filter (DL) GTP-U Encap GTP-U Decap GTP-U Decap Data Plane Forwarding MEC Host NFVI User Traffic Forwarding UserTrafficForwarding Applications and Services • The LTM server in the core is initially contacted CORE (EPC) UO LTM UO Storage
  • 24. MEC Host NFVI User Traffic Forwarding Applications and Services UserTrafficForwarding UE (Bob) MEC/Video Streaming Demo User Traffic Forwarding eNB/BBU User Traffic Forwarding Server MEC TOF GTP-U Encap S/DNAT Traffic Filter (UL) Router Traffic Filter (DL) GTP-U Encap GTP-U Decap GTP-U Decap Data Plane Forwarding Unified Origin LTM • The Management and Orchestration (MANO) layer instantiates the LTM application in the edge (so far, MANO is just a manual script execution) CORE (EPC) UO LTM UO Storage MEC Management and Orchestrator
  • 25. MEC Host Unified Origin LTM NFVI User Traffic Forwarding Applications and Services UserTrafficForwarding UE (Bob) MEC/Video Streaming Demo User Traffic Forwarding eNB/BBU User Traffic Forwarding Server MEC TOF GTP-U Encap S/DNAT Traffic Filter (UL) Router Traffic Filter (DL) GTP-U Encap GTP-U Decap GTP-U Decap Data Plane Forwarding • After the LTM instantiation is fully operational, the MANO layer configures the TOF in order to redirect the video request to the new LTM CORE (EPC) UO LTM UO Storage MEC Management and Orchestrator
  • 26. MEC Host Unified Origin LTM NFVI User Traffic Forwarding Applications and Services UserTrafficForwarding UE (Bob) MEC/Video Streaming Demo User Traffic Forwarding eNB/BBU User Traffic Forwarding Server MEC TOF GTP-U Encap S/DNAT Traffic Filter (UL) Router Traffic Filter (DL) GTP-U Encap GTP-U Decap GTP-U Decap Data Plane Forwarding • The subscriber will, transparently, start getting the video service from the edge LTM, which in turn accesses the central storage for contents CORE (EPC) UO LTM UO Storage
  • 28. Testbed Results Deployment time of Edge Video Streaming components 28
  • 29. Thank you. Questions? Contacts Stefano Salsano University of Rome Tor Vergata / CNIT stefano.salsano@uniroma2.it http://superfluidity.eu/ The work presented here only covers a subset of the work performed in the project 29
  • 30. References • SUPERFLUIDITY project Home Page http://superfluidity.eu/ • G. Bianchi, et al. “Superfluidity: a flexible functional architecture for 5G networks”, Transactions on Emerging Telecommunications Technologies 27, no. 9, Sep 2016 • S. Salsano, L. Chiaraviglio, N. Blefari-Melazzi, C. Parada, F. Fontes, R. Mekuria, D. Griffioen, “Toward Superfluid Deployment of Virtual Functions: Exploiting Mobile Edge Computing for Video Streaming”, Soft5 Workshop, 1st International Workshop on Softwarized Infrastructures for 5G and Fog Computing, in conjunction with 29th ITC conference, Genoa, Italy, 8th September 2017 • S. Salsano, F. Lombardo, C. Pisa, P. Greto, N. Blefari-Melazzi, “RDCL 3D, a Model Agnostic Web Framework for the Design and Composition of NFV Services”, submitted paper, https://arxiv.org/abs/1702.08242 • L. Chiaraviglio, L. Amorosi, S. Cartolano, N. Blefari-Melazzi, P. Dell’Olmo, M. Shojafar, S. Salsano, “Optimal Superfluid Management of 5G Networks”, 3rd IEEE Conference on Network Softwarization, NetSoft 2017, 3-7 July 2017, Bologna, Italy 30
  • 31. The SUPERFLUIDITY project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No.671566 (Research and Innovation Action). The information given is the author’s view and does not necessarily represent the view of the European Commission (EC). No liability is accepted for any use that may be made of the information contained. 31