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

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1 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

2 Summary 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

3 5G Mi-Edge 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 G MiEdge in EC and 0159-{0149, 0150, 0151} in MIC

4 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 m 2 at airport waiting area and shopping mall), 13.2 Mbps/m 2 (= 6.6 Gbps/500 m 2 at train platform)

5 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/m 2 (80.33 Gbps/600m 2 )

6 5G Mi-Edge Use cases 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)

7 5G Mi-Edge Use cases 4. Automated driving Example of HD LIDAR image mmwave based V2V/V2X to exchange HD maps

8 5G Mi-Edge 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 GHz to support wider bandwidth without unnecessary interference

9 Mobile Edge Computing MEC is an enabler of many 5G functionalities IoT mmwave links dense deployment MEC user/application centric virtualization

10 Mobile Edge Computing MEC: multi-tier IT service delivery mmw radio AP + MEC server Cloud-5G centralized control : Data plane : Control plane : mmw backhaul : wired backhaul macrocell Radio Access Point Mobile edge orchestrator Mobile edge orchestrator MEC spreads intelligence towards the periphery of the network 10

11 Mobile Edge Computing MEC basic architecture MEC server data center coordination of MEC servers RAP mobile backhaul SAE-GW MEC server Internet Service Prov. application service prov. RAP Basic idea: Keep communication and computation tasks as local as possible

12 The 3 Primary Colors of 5G Communication-Computation-Caching are three faces of a common framework Communication Communication + Caching Communication + Caching + Computation This holistic view suggests joint optimization of C 3 resource allocation

13 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) (1) E. Bastug, M. Bennis, M. Debbah, Living on the edge: The role of proactive caching in 5G wireless networks, IEEE Comm. Magazine, Aug (1) M.A. Maddah-Ali, U Niesen, "Coding for Caching: Fundamental Limits and Practical Challenges, IEEE Comm. Mag, 2016 (2) S. Barbarossa, S. Sardellitti, P. Di Lorenzo, Communicating while Computing: Distributed Cloud Computing over 5G Heterogeneous Networks, IEEE Signal Processing Magazine, Nov (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 (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 (3) J. Oueis and E. Calvanese Strinati, Computation Caching for Local Cloud Computing, IEEE WCNC 2017, March 2017 (3) M. S. Elbamby, M. Bennis, W. Saad, Proactive Edge Computing in Latency-Constrained Fog Networks April 2017

14 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

15 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 Overall latency: = Tx + exe + Rx where - Tx = time necessary to transfer the input bits b from MU to the serving SCeNB - exe = time for the server to run the application - Rx = time necessary to receive the result back Overall latency couples radio access and computational aspects Joint optimization of radio / computational resources S. Barbarossa, S. Sardellitti, P. Di Lorenzo, Communicating while Computing: Distributed Cloud Computing over 5G Heterogeneous Networks, IEEE Signal Processing Magazine, Nov. 2014

16 Single cloud serving multiple cells MIMO multicell network Goal: Joint allocation of computational and communication resources to minimize the transmit energy consumption under latency constraint in a dense deployment scenario 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

17 Single cloud serving multiple cells Degrees of freedom: - Radio resources: precoding matrices / beamforming - Computing resources: percentage of CPU cycles assigned to each user (VM) f in Constraints: - Transmit power budget - Server computational capacity: X i n 2I f in apple f S Critical issues: Inter-cell interference, admission control incorporating QoE

18 Single cloud serving multiple cells Problem formulation: Find optimal Q, (Q in ) in 2I and f, (f in ) in 2I minimizing overall energy spent by MUs min Q,f E(Q), X n,i E in (Q in, Q n ) s.t. a)g in (Q,f in ), c in r in (Q in, Q n ) +! i n f in Tin apple 0, 8i n 2I, Tx time Comp time b) X f in apple f T and f in 0, 8i n 2 I, i n 2I c)q in 2 Q in, 8i n 2 I, users' latency constraints computing rate constraint Tx power constraint where r in (Q) = log 2 det I + H H i n nr in (Q n ) 1 H in nq in R in (Q n ), R n + XN c XK m n6=m=1 j=1 H jm nq jm H H j m n 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

19 Single cloud serving multiple cells Proposed approach: Successive Convex Approximation (SCA) Sum energy vs. iteration number Proposed SCA algorithm converges in a very few iterations 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

20 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

21 Merging mmw & MEC Architectural challenges: Merging MEC with Hybrid-CRAN (1) Dual split CPs are sequences of functions in physical layer that are dedicated to process signals from a cell, when signals of UEs are multiplexed Challenge: Design Multi-tier MEC building on Hybrid-RAN UPs are sequences of functions that process the signal streams on a per-ue basis (1) X. Wang, A. Alabbasi, C. Cavdar, Interplay of Energy and Bandwidth Consumption in CRAN with Optimal Function Split, ICC 2017

22 mmw & MEC Physical layer challenges: Blocking Measurements 60 GHz, courtesy from Fraunhofer Institute (HHI) Human body shadowing

23 mmw & MEC Physical layer challenges: Blocking Measurements 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

24 mmw & MEC Counteractions: Multi-link connectivity and resource overbooking a Backhaul Backhaul b Backhaul Backhaul X c Backhaul Backhaul 1 2 d Backhaul Backhaul 1 2 X X X Allocate radio and computational resources as a function of blocking probabilities

25 mmw & MEC Single-user / Multi-link: Statistically dependent blocking events Average energy consumption vs. distance 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

26 mmw & MEC Single-user / Multi-link: Average energy vs. computational load (independent blocking events) Single link Double link Average transmit energy (J) Required computational rate (CPU Cycles) 10 6 Diversity gain increases for applications requiring higher computational load S. Barbarossa, E. Ceci, M. Merluzzi, E. Calvanese-Strinati, Enabling Effective Mobile Edge Computing Using Millimeter Wave Links, ICC 2017

27 mmw & MEC Multi-user / Single-link Closed forms for single-link case Given computational load w k for each user, the computational rates f k are: f k = w k P K k=1 w k f S according to proportional fair methods, and f k = p wk k P K k=1 p wk k f S according to our joint optimization, where k depend on the channel Joint allocation outperforms disjoint allocation S. Barbarossa, E. Ceci, M. Merluzzi, "Overbooking Radio and Computation Resources in mmw-mobile Edge Computing to Reduce Vulnerability to Channel Intermittency", EUCNC 2017

28 mmw & MEC Multi-user / Multi-link Power (dbm) link joint 2 links joint 4 links joint 1 disjoint 2 disjoint 4 disjoint Distance (m) Most of the gain occurs when passing from one AP to two AP s S. Barbarossa, E. Ceci, M. Merluzzi, "Overbooking Radio and Computation Resources in mmw-mobile Edge Computing to Reduce Vulnerability to Channel Intermittency", EUCNC 2017

29 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 G MiEdge in EC and {0149, 0150, 0151} in MIC

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