The Role of Optical Networks in the Big Data Era

Transcription

1 The Role of Optical Networks in the Big Data Era Massimo Tornatore Dept. of Electronics and Information, Politecnico di Milano Piazza Leonardo da Vinci Milan, Italy

2 Outline An overview of my past research activities Optical networks in the Big Data era Convergence of network access technologies On-demand big-data transfer Energy efficiency and integration of renewables

3 Research methodologies Optimization models and algorithms (graph theory, MILP, heuristics, complexity theory, combinatorics) Theory of traffic, classic queuing theory (transient analysis, Jensen s method) Simulation tools (network level, discrete events, NS-2, in-house built C++ tools) Prototype implementations (Access network testbeds, switching nodes) 3

4 Planning Tools for Telecom Optical Networks Politecnico di Milano year Optical Network design and simulation Resilience, protection, availability ASON/GMPLS control plane SLA-aware routing Semi-transparent OTN, physical impairments Multi-domain routing Carrier-grade Ethernet Multi-layer TE algorithms OPTCORE AGNES 4

5 OptCore graphical interface Workspace window 5

6 OptCore graphical interface Output display (I) 6

7 OptCore graphical interface Output display (II) 7

8 Opto-electronic switching testbed Synchronous packet-switching demonstrator BESOS (Bandwidth efficiency and Energy Saving by sub-lambda Optical Switching) Lab prototype of a high-bandwidth optoelettronic packetswitching node synchronized using the GPS satellite signal 8

9 Convergence of network access technologies On-demand big-data transfer Energy efficiency and integration of renewables 3 main research avenues to provide scalable, cost&energy-efficient network support to big data 9

10 Internet Growth July Total Host Count 908,585,739

11 Traffic Growth [S.K. Korotky, OFC 2012] 11

12 Content «Big Bang» From 15-billion web pages in 2002 to 281-billion in 2008!! Sustained growth of 100% The content on Internet in 2008 has surpassed the world aggregate content previously stored (analogically or digitally): Paper,Film, Magnetic Storage

13 Big Data Big data is data that exceeds the processing capacity of conventional database systems New effective «ICT approaches» are needed to tame the volume, velocity and variability of big data The challenges include capture, curation, storage, search, sharing, access and transfer, analysis, and visualization 13

14 Optical Networks & BigData (1) It all started with Lambda grids, consisting of: high capacity pipes connecting the grid sites; storage and computing resources (supercomputers, clusters) Lambda-Grid Implementations (for large-scale scientific applications): National LambdaRail DOE UltraScience Net Canarie CA*Net European Geant (Phosphorous) CHEETAH testbed

15 Optical Networks & BigData (2) Nowadays Cloud Services are based on the same concept and are penetrating operators and providers networks: from user-to-user to user-to-content DD 1 End User Request (1,2,4) End User Request (1,3) 1 End User Request (2,4) 5 App 2 VM 2 App 3 VM 3 End User 4 2 App 2 VM 2 App 4 VM App 1 VM 1 App 1 VM 1 App 4 VM 4 7 DD 2 DD 3 Request (3,4) Enterprise

16 Convergence of network access technologies On-demand big-data transfer Energy efficiency and integration of renewables 16

17 but, what is «convergence»? Convergence In-network processing (Cloud) Node consolidation BBU Hostelling Functional convergence Structural Content to convergence the edge 17

18 COnvergence of fixed and Mobile BrOadband access/aggregation networks 4 Network operators, 3 SMEs, 3 vendors, 4 universities, 1 research institute Period: Total funds: 10M 18

19 Convergence of Access Networks In the past, fixed and mobile access networks have been optimized and evolved independently, with partly contradicting trends Access infrastructures represent long term investments (> 50% of total ICT) and about 90% of network energy consumption: a sustainable network evolution strategy is required increase performance for fixed and mobile access (Gigabit society) lower cost and energy consumption per bit 19

20 Telecom Network Evolution towards Cloud Metro Courtesy of Orange 20

21 Summarizing: NG-POP concept Distribution of most popular contents, CDN Optical node concentration NG-POP Content Content ONT OLT OLT BBU System module Resource allocation System module Spectrum allocation S/P-GW Distributed Core Nodes BBU hotelling WiFi 3GPP 3GPP Base station Functional convergence, e.g., convergence of fixed and mobile network functions Structural convergence, e.g., convergence of fixed and mobile infrastructures and equipment 21

22 Convergence of network access technologies On-demand big-data transfer Energy efficiency and integration of renewables 22

23 Data Aggregation Request (DAR) Example F 3 =20 GB FC (4 Gbps) 10GbE Routing + MSPP Scheduling + Rate Assignment 1GbE s 3 MSPP d MSPP F 1 =20 GB s 1 10GbE s 2 MSPP 1GbE F 2 =10 GB Bandwidth Granularity Legend: Holding Time

24 Another dimension: Deadline-Driven Requests Deadline-Driven Requests (DDRs) = requests which need to transfer a fixed amount of data, starting at any time, before a predefined deadline 1. Real-time file download: Maximum transfer time a person can tolerate (perhaps 5-10 sec) 2. Database/website/server backup: scheduled at any time, perhaps during night, but having a fixed deadline Flexible application that requires high bandwidth, but not necessarily instantaneously

25 Example of a DDRs DDRs allow flexibility of allocated bandwidth: Download time for a 1-GByte file: Bandwidth 8 sec if offered a 1-Gbps transfer rate 800 msec if offered 10-Gbps Different Transmission Rates on a Channel MaxRate = C (e.g., 10 Gbps) ArrivalTime Duration max = 8 sec Deadline (e.g., 8 sec) How to choose transmission rate to maximize network throughput? 1 Gbps = Minimum Rate (MinRate) MinRate = FileSize/ Deadline Duration min = 800 msec Time

26 Convergence of network access technologies On-demand big-data transfer Energy efficiency and integration of renewables 26

27 Energy Consumption and GHG Growth in ICT

28 Energy Consumption of Telecom Networks : 150% increase of energy consumption in telecom networks Access network: Now 70% of energy consumption Source: C. Lange and A. Gladisch, On the energy consumption of FTTH access networks, OFC/NFOEC 09 Workshop, San Diego, Mar

29 Network Domain Utilization <50% Utilization <30% Utilization <15% Utilization

30 Energy Savings Approaches i) Selectively turn down network elements i) Energy Efficient Traffic-Aware Design of On-Off Multi-Layer Translucent Optical Networks A. Morea, G. Rizzelli, M. Tornatore, O.Rival, in Computer Networks, Vol. 56, No. 10, pp , July 2012 ii) iii) Energy Optimization in IP-over-WDM Networks, Yi Zhang, Massimo Tornatore, Pulak Chowdhury, Biswanath Mukherjee, in Optical Switching and Networking (OSN), Special Issue on Green Communications and Networking, Vol. 8, No. 3, pp , July 2011 Building a Green Wireless-Optical Broadband Access Network (WOBAN), Pulak Chowdhury, Massimo Tornatore, Suman Sarkar, Biswanath Mukherjee, in IEEE/OSA Journal on Lightwave Technology, Vol. 28, No. 16, pp , Aug i) Energy-efficient network architecture i) Energy-Efficiency of All-Optical Transport through Time Driven Switching F. Musumeci, M. Tornatore, G. Fontana, Stefano Bregni and A. Pattavina, in IET Optoelectronics, special issue on Green Photonics, 2013 ii) iii) A Power Consumption Analysis for IP-over-WDM Core Network Architectures, F. Musumeci, M. Tornatore, A. Pattavina, in IEEE/OSA Journal of Optical Communications and Networking (JOCN), Vol. 4, No. 1, pp , Feb Energy-Efficient Mixed-Line-Rate (MLR) Optical Network Design, Pulak Chowdhury, Massimo Tornatore, Avishek Nag, Ezra Ip, Ting Wang, Biswanath Mukherjee, in IEEE/OSA Journal of Lightwave Technology, Vol. 30, No. 1, pp , Jan ii) Green routing i) Green Provisioning for Optical WDM Networks, Ming Xia, Massimo Tornatore, Yi Zhang, Pulak Chowdhury, Charles Martel, Biswanath Mukherjee, in IEEE Journal of Selected Areas in Quantum Electronics, Vol. 17, No. 2, pp , March-April 2011

31 Integrating Renewable Energy by Exploiting Data processing can be migrated to follow the sun/wind Two types of traffic: user and migration traffic Network consumes more energy, but green energy is exploited better Reduced grid transmission Cloud Services End User Request (1,2,4) End User Request (1,3) 1 End User Request (2,4) 5 DD CO 2 App 2 VM 2 App 3 VM 3 End User Request (3,4) App 2 VM 2 6 App 4 VM 4 3 App 1 VM 1 Enterprise App 1 VM 1 7 App 4 VM 4 DD 2 DD 3 Page 31

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33 Wrap up Network is becoming (big-)data centric, new network architectures and solutions are needed 3 main research avenues to provide scalable, cost&energy-efficient network support to big data: Convergence of access networks NG-POP concept (BBU Hotelling, node consolidation, content to the edge) On-demand big-data transfer New dimensions in routing (deadlines, scheduling, rate assignment) Energy efficiency Already a relevant body of research Cloud computing as flexible energy load for a smart electrical grid 33

34 Many thanks! 34