merging millimeter-wave and mobile edge computing ... · • obu-to-obu, rsu-to-rsu, obu-to-rsu...

Merging millimeter-wave and mobile edge computing technologies in 5G networks: Opportunities and challenges

Sergio Barbarossa

Acknowledgments: H2020 EU/Japan Project

Elena Ceci, Mattia Merluzzi, Stefania Sardellitti

CLEEN 2017, Torino, June 22, 2017

•  5G Mi-Edge

•  Mobile Edge Computing •  The 3 Primary Colors of 5G: Communication-Computation-Caching •  Computation offloading: Joint optimization of comm/comp resources •  Synergies and challenges in merging MEC & mmWave •  Conclusion

Summary


H2020 EU/Japan Project 5G-MiEdge: Millimeter-wave Edge Cloud as an Enabler for 5G Ecosystem

List of participants

1 (EU coordinator) Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Heinrich- Hertz-Institute, Germany 2 Commissariat à l'énergie atomique (CEA), France 3 Intel Deutschland GmbH Intel Germany 4 Telecom Italia, Italy 5 Sapienza University of Rome, Italy 6 (JP coordinator) Tokyo Institute of Technology, Japan 7 KDDI R&D Laboratories Inc., Japan 8 Panasonic AVC Networks Company, Japan The research leading to these results are jointly funded by the European Commission (EC) H2020 and the Ministry of Internal affairs and Communications (MIC) in Japan under grant agreements N° 723171 5G MiEdge in EC and 0159-{0149, 0150, 0151} in MIC

5G Mi-Edge


Use cases

1. Omotenashi services Applications (a) Ultra-high-speed content download in a dense area (b) Massive video streaming Scenarios: Airport, train station, shopping mall Added value of mmWave and MEC Technologies: - Pre-fetching most popular contents in the local edge server - Running analytics on the MEC servers to learn, locally, most popular contents across space and time Requirements for 5G system: •  Area peak system rate: more than 6.6 Gbps •  Traffic density: 10 Mbps/m2 (= 6 Gbps/600 m2 at airport waiting area and shopping mall), 13.2 Mbps/m2

(= 6.6 Gbps/500 m2 at train platform)

5G Mi-Edge


Use cases

2. Moving Hotspot Scenario Applications (a) Entertainment contents download (b) Upload and share sightseeing photos/videos in SNS Scenarios: Train, bus, metro Added value of mmWave and MEC Technologies: - Pre-fetching most popular contents based on prediction of users’ requests - Data shower at metro/bus stations Requirements for 5G system (train): •  High peak data rate for user link: DL: 2.15 Gbps, UL: 0.54 Gbps •  High peak data rate (e.g. 10Gbps) for backhaul at stations for synchronizing with cloud servers •  Area coverage: 10 – 20m •  MEC server(s) in a train formation for data prefetching/caching •  Traffic density: 134 Mbps/m2 (80.33 Gbps/600m2)

5G Mi-Edge


Use cases

3. 2020 Tokyo Olympic Games Applications (a) Olympic game application/data download (b) Augmented reality services (3) Massive SNS sharing Scenarios: entrance gates, stadium Added value of mmWave and MEC Technologies: - MEC servers providing immersive augmented reality experience to users - user navigation throughout the scene Requirements for 5G system: •  User rate: Minimum rate 50 Mbit/s •  Area (system) rate: Total of 500 Gbit/s to serve the stands area of the stadium, radio access segment •  End-to-end latency: 5ms (for immersive reality)

5G Mi-Edge


Use cases

4. Automated driving Example of HD LIDAR image

5G Mi-Edge

mmWave based V2V/V2X to exchange HD maps


Use cases

4. Automated driving Requirements for 5G system: •  High data rate in enhanced V2V/V2X link to exchange HD map information: > 1 Gbps •  Traffic density of vehicles: 0.2 vehicles/m/lane •  Low end-to-end latency in enhanced V2V/V2X links to realize safe maneuvering: < 10 ms •  Communication range of enhanced V2V/V2X links to guarantee safe stop of vehicles: > 150 m •  High reliability on enhanced V2V/V2X links to avoid accidents: 99.99% •  OBU-to-OBU, RSU-to-RSU, OBU-to-RSU relay to extend coverage of cooperative perception •  Mobile edge computing in RSU and OBU to process cooperative perception in real-time •  Control plane over LTE/NR for radio resource management of enhanced V2V/V2X side-links •  Dedicated carrier for enhanced V2V/V2X at frequency band above 24.25 GHz to support wider bandwidth without unnecessary interference

5G Mi-Edge


MEC is an enabler of many 5G functionalities

MEC

mmWave links

user/application centric

dense deployment

virtualization

Mobile Edge Computing

IoT



macrocell Radio Access Point

mmW radio AP + MEC server

Cloud-5G centralized control

Mobile edge orchestrator

MEC spreads intelligence towards the periphery of the network

MEC: multi-tier IT service delivery

10

: Data plane: Control plane: mmW backhaul: wired backhaul

Mobile edge orchestrator



MEC server

RAP

MEC server

RAP

mobile backhaul SAE-GW Internet

Service Prov.

application service prov.

data center

coordination of MEC servers

MEC basic architecture

Basic idea: Keep communication and computation tasks as local as possible


The 3 Primary Colors of 5G

Communication Communication + Caching

Communication + Caching + Computation

Communication-Computation-Caching are three faces of a common framework

This holistic view suggests joint optimization of C3 resource allocation


The 3 Primary Colors of 5G

Recent theoretical developments motivate joint management of •  Communication and caching coded caching, proactive caching (1)

•  Computation and communication coded computing, computation offloading (2)

•  Computation and caching computation caching (3)

(3) M. S. Elbamby, M. Bennis, W. Saad, “Proactive Edge Computing in Latency-Constrained Fog Networks” April 2017

(1) M.A. Maddah-Ali, U Niesen, "Coding for Caching: Fundamental Limits and Practical Challenges”, IEEE Comm. Mag, 2016

(2) S. Li, M.A. Maddah-Ali, Q. Yu, and A. S. Avestimehr, " A Fundamental Tradeoff between Computation and Communication in Distributed Computing", ISIT 2016

(2) S. Barbarossa, S. Sardellitti, P. Di Lorenzo, “Communicating while Computing: Distributed Cloud Computing over 5G Heterogeneous Networks”, IEEE Signal Processing Magazine, Nov. 2014

(2) S. Sardellitti, G. Scutari, S. Barbarossa, “Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing”, IEEE Trans. on Signal and Information Processing over Networks, June 2015

(1) E. Bastug, M. Bennis, M. Debbah, “Living on the edge: The role of proactive caching in 5G wireless networks”, IEEE Comm. Magazine, Aug. 2014

(3) J. Oueis and E. Calvanese Strinati, “Computation Caching for Local Cloud Computing”, IEEE WCNC 2017, March 2017


Computation offloading

Advantages offered by computation offloading using MEC •  Empower simple devices (e.g., tiny sensors in IoT) with augmented

computational capabilities •  Prolong battery lifetime of mobile devices

•  Reduce end-to-end latency needed to run sophisticated applications

•  Facilitate latency control with respect to MCC over wide area networks

•  Enable RAN-aware content delivery


Overall latency: where - = time necessary to transfer the input bits b from MU to the serving SCeNB - = time for the server to run the application

- = time necessary to receive the result back Overall latency couples radio access and computational aspects

Joint optimization of radio / computational resources

Computation offloading: Single-user case

Optimization strategies: 1)  Minimize Tx energy subject to overall latency constraint 2)  Minimize latency, subject to maximum transmit power constraint

S. Barbarossa, S. Sardellitti, P. Di Lorenzo, “Communicating while Computing: Distributed Cloud Computing over 5G Heterogeneous Networks”, IEEE Signal Processing Magazine, Nov. 2014

� = �Tx

+�exe

+�Rx

�Tx

�exe

�Rx


Single cloud serving multiple cells

Goal: Joint allocation of computational and communication resources to minimize the transmit energy consumption under latency constraint in a dense deployment scenario

MIMO multicell network

S. Sardellitti, G. Scutari, S. Barbarossa, “Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing”, IEEE Trans. on Signal and Information Processing over Networks, June 2015


Degrees of freedom: -  Radio resources: precoding matrices / beamforming

-  Computing resources: percentage of CPU cycles assigned to each user (VM)

Constraints: -  Transmit power budget

-  Server computational capacity: Critical issues: Inter-cell interference, admission control incorporating QoE


X

in2Ifin fS

fin


Find optimal and minimizing overall energy spent by MUs where

minQ,f

E(Q) ,X

n,i

Ein(Qin ,Q�n)

s.t. a)gin(Q, fin) ,cin

rin(Qin ,Q�n)+

!in

fin� T̃in 0, 8in2I,

b)X

in2Ifin fT and fin � 0, 8in 2 I,

c)Qin 2 Qin , 8in 2 I,

Problem formulation:

users' latency constraints


Tx time Comp time

rin(Q) = log2 det�I+HH

innRin(Q�n)�1HinnQin

�

Rin(Q�n) , Rn +NcX

n 6=m=1

KmX

j=1

HjmnQjmHHjmn

Q , (Qin)in2I f , (fin)in2I

Tx power constraint

computing rate constraint



Proposed approach: Successive Convex Approximation (SCA)


Proposed SCA algorithm converges in a very few iterations


Sum energy vs. iteration number


Merging mmW & MEC

Opportunitites: •  High data rate access to MEC functionalitites

•  Wireless backhauling enabling orchestration of MEC servers Challenges: •  Channel intermittency due to obstacles

•  Interference from users transmitting from similar angles (e.g., canyon crowd)

•  Large attenuation over long distance links

Proposed approach: •  Overbook resources to meet latency constraints on average, depending on probabilities

of blocking events

•  Use multi-link communications


Merging mmW & MEC

Architectural challenges: Merging MEC with Hybrid-CRAN(1) Dual split

Challenge: Design Multi-tier MEC building on Hybrid-RAN

(1) X. Wang, A. Alabbasi, C. Cavdar, “Interplay of Energy and Bandwidth Consumption in CRAN with Optimal Function Split”, ICC 2017

CPs are sequences of functions in physical layer that are dedicated to process signals from a cell, when signals of UEs are multiplexed

UPs are sequences of functions that process the signal streams on a per-UE basis


mmW & MEC

Physical layer challenges: Blocking Measurements taken @ 60 GHz, courtesy from Fraunhofer Institute (HHI) Human body shadowing


mmW & MEC

Physical layer challenges: Blocking Measurements taken @ 60 GHz, courtesy from Fraunhofer Institute (HHI) Blocking across streets

•  Transmitter antenna at 3m height

•  Receiver Antenna at 1.5m height

•  15min observation time

•  Clear impact of traffic lights cycle


mmW & MEC

Counteractions: Multi-link connectivity and resource overbooking

Allocate radio and computational resources as a function of blocking probabilities

1 2Backhaul

1 2Backhaul

d

b

X

X

X

Backhaul

Backhaul

1 2Backhaul

cBackhaul

1 2Backhaul

aBackhaul

X


mmW & MEC

Single-user / Multi-link: Statistically dependent blocking events

What matters is not only the number of access points, but their angular spread

S. Barbarossa, E. Ceci, M. Merluzzi, "Overbooking Radio and Computation Resources in mmW-Mobile Edge Computing to Reduce Vulnerability to Channel Intermittency", EUCNC 2017

Average energy consumption vs. distance


mmW & MEC

Single-user / Multi-link: Average energy vs. computational load (independent blocking events) Diversity gain increases for applications requiring higher computational load

1 2 3 4 5 6 7 8 9Required computational rate (CPU Cycles) ×106

10-5

10-4

Aver

age

trans

mit

ener

gy (J

)

Single linkDouble link

S. Barbarossa, E. Ceci, M. Merluzzi, E. Calvanese-Strinati, “Enabling Effective Mobile Edge Computing Using Millimeter Wave Links”, ICC 2017


mmW & MEC

Multi-user / Single-link Joint allocation outperforms disjoint allocation


fk =wkPKk=1 wk

fS

according to proportional fair methods, and according to our joint optimization, where depend on the channel

Closed forms for single-link case Given computational load for each user, the computational rates are:

fk =

pwk �k

PKk=1

pwk�k

fS

wk

fk

�k


mmW & MEC

Multi-user / Multi-link Most of the gain occurs when passing from one AP to two AP’s

Distance (m)10 20 30 40 50 60 70 80 90 100

Pow

er (d

Bm)

-20

-15

-10

-5

0

5

10

15

20

1 link joint2 links joint4 links joint1 disjoint2 disjoint4 disjoint



Conclusions

•  Mobile edge-computing enables joint optimization of radio and computing resources, depending on channel state, backhaul state, and application parameters

•  Optimal association of mobile users to base stations and clouds provides a mechanism

for optimal instantiation and migration of containers (light version of virtual machines)

•  Handover mechanisms incorporate radio and computing state and request •  mmW channel intermittency can be counteracted by multi-link communication and

resource overbooking based on knowledge (estimation) of blocking probability •  Incorporation of online distributed learning mechanisms in mobile devices (task

profilers) as well as in the edge of the network enable proactive resource allocation

•  Extensions: coded and proactive caching, coded computing, dynamic multitask scheduling, URLL communications, optimal functional split, TCP-split, …

The research leading to these results are jointly funded by the European Commission (EC) H2020 and the Ministry of Internal affairs and Communications (MIC) in Japan under grant agreements N° 723171 5G MiEdge in EC and 0159-{0149, 0150, 0151} in MIC

merging millimeter-wave and mobile edge computing ... · • obu-to-obu, rsu-to-rsu, obu-to-rsu...

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