Green Telecom & IT Workshop by IISc and Bell Labs: Embodied topology by Prof. Elmirghani
1 like666 views
This document discusses several topics related to optimizing network topology and energy efficiency, including: 1) Considering both operational and embodied energies when optimizing physical network topology to minimize total energy usage and carbon footprint over the network lifetime. 2) Using data compression techniques in optical networks to reduce bandwidth usage and operational energy, while accounting for the additional energy required for compression/decompression. 3) Developing optimization models and heuristics to design energy-efficient BitTorrent peer-to-peer networks that minimize total energy consumption.
![Energy-Efficient Data Compression for Optical Networks
• Data compression is becoming a widely used technique to save
bandwidth which will consequently result in energy savings.
• Trade-off between the energy consumption of computational resources
and memory required to compress and decompress data and the network
energy savings.
• Cisco forecasts that 90% of the Internet traffic will be video by 2015.
• In [1], the authors considered semantic compression to reduce the video
storage space.
• YouTube videos can be compressed by a ratio of 20:1 compared to
ordinary histogram representations.
_______________________________________________________________________________
1. Jörn Wanke et. al,”Topic Models for Semantics-preserving Video Compression,” ACM
International Conference on Multimedia Information Retrieval (ACM MIR), Philadelphia, PA,
2010.](https://image.slidesharecdn.com/prof-elmirghaniembodiedtopologyp2p-120406232244-phpapp02/85/Green-Telecom-IT-Workshop-by-IISc-and-Bell-Labs-Embodied-topology-by-Prof-Elmirghani-9-320.jpg)
![Power Consumption of Data Compression
• In [2], the data compression energy
consumption per bit is given as:
RC is the data compression ratio
A and β are parameters
A is given as:
ε is a scaling parameter
is the maximum data compression ratio
β represents the efficiency of the data compression algorithm.
ENet is the energy consumption of the network.
Therefore:
_________________________________________________________________________________________________________
2. Dan Kilper et. al ”insights on coding and transmission energy in optical networks”, E-energy 2011](https://image.slidesharecdn.com/prof-elmirghaniembodiedtopologyp2p-120406232244-phpapp02/85/Green-Telecom-IT-Workshop-by-IISc-and-Bell-Labs-Embodied-topology-by-Prof-Elmirghani-10-320.jpg)
Green Telecom & IT Workshop by IISc and Bell Labs: Embodied topology by Prof. Elmirghani
- 1. Topology design, Embodied Energy and Peer-to-Peer Networks Prof. Jaafar Elmirghani, University of Leeds j.m.h.elmirghani@leeds.ac.uk Contributors: X. Dong, Ahmed Lawey, T. El-Gorashi
- 2. Outline • Previous work • Physical Topology Optimization Considering Embodied Energy • Network Devices Embodied Energy • Optimized Topologies Considering Operational and Embodied Energies • Energy-Efficient Data Compression for Optical Networks • Power Consumption of Data Compression • MILP Model for Data compression in IP over WDM networks • Energy-Efficient Data Compression and Routing Heuristic • Energy-Efficient BitTorrent • MILP Model for Energy-Efficient BitTorrent • Energy-Efficient BitTorrent Heuristic 2
- 3. WP1: Energy efficient network architecture Renewable energy Topology optimisation Content Data centres distribution networks 3
- 4. Physical Topology Optimization Considering Embodied Energy • Introducing additional devices to minimize the operational energy might increase the embodied energy and consequently the total Carbon footprint of the network. • The average commercial lifetime (LT) of network devices is estimated as 10 years and the maintenance adds 10% of the device production embodied energy EEMB-p annually. • Objective: MILP Minimize: • The embodied energy of most network devices is mainly composed of: Printed Circuit Boards (PCB), semiconductor devices, bulk materials and metal. The Embodied Energy and the Density of the Different Materials of Network Devices Materials/Processing Embodied Energy Density MJ/kg g/m2 Semiconductor device 120000 400 (on PCBs) Metals 100-400 Various Bulk materials 20-400 Various PCB 300-500 2000-4500
- 5. Network Devices Embodied Energy The Embodied Energy of CRS-1 16 Slots Chassis Routing System CRS-1 16 Slots Chassis Routing System Embodied energy (MJ) Total Module Dimension (cm) Weight Units (GJ) (kg) PCB Semiconductor Bulk Metals Materials IP PLIM H52.3, D47.2, W4.6 3.8 555 9480 144 900 16 177.3 Port MSC H52.3, D47.2, W4.6 6.68 555 8280 200 2000 16 176.6 Power H50,D46,W90 35 980 1440 1300 11900 1 15.6 (estimate) RP H52.3, D28.4, W7.1 5.8 335 7080 228 1800 2 18.9 FC H52.2, D28.5, W7.1 5.6 223 4920 224 1820 2 14.4 SM H52.3, D28.5, W3.6 5.4 335 6960 182 1690 8 73.3 Fan Tray N/A 20 0 0 0 8000 2 16 System N/A 486 0 0 19440 174960 1 194.4 Chassis Total embodied energy of a full load CRS-1 16 Slots Chassis Routing System 686.5 The Embodied Energy of Active Network Devices Embodied energy (MJ) Total Device Dimension (cm) Weight (kg) (GJ) PCB Semiconductor Bulk Materials Metals Transponder H32.1, D22.8, W2.3 1.4 164 3480 40 380 4.1 EDFA H4.5, D25.9, W48.3 3.08 135 3393 224 899 4.7 Regenerator H4.4, D30, W43.9 4.4 197 4425 320 1100 6 Multi H32.1, D22.8, W2.3 1.5 164 2446 225 414 3.2 /Demultiplexer (Estimated) PLIM: Physical layer interface module; MSC: Module service card; RP: router processor; FC: Fan controller; SM: Switch module
- 6. Network Devices Embodied Energy The Embodied Energy of the 192x192 Glimmerglass Optical Switch Materials/Processing Embodied Energy (MJ) Weight Total Embodied (g) Energy (GJ) SCS processing 30.3 0.253 Semiconductor device 4116 34.3 Metals 5440 13600 11 Bulk materials 1200 (Estimated) 3000 PCB 220.5 (Estimated) 490 The Embodied Energy per km of the GYTY53 Optical Cable Component Material Thickness or Weight Embodied Diameter kg/km Energy (estimation) MJ/km PE outer sheath PE 3mm 122.46 9907 (analysis) Steel tape steel 0.5 mm 37.5 (analysis) 1200 MJ/km PE inner sheath PE 1 mm 25.12 (analysis) 2302 MJ/km Strength member steel 2 mm 24.8 (analysis) 793 MJ/km Fibers glass 125 μm 1.73 (analysis) 123 MJ/km Loose tube (6 items) PBT 1 mm 25.2 (estimated) 2245 MJ/km Filling compound Polymers -- 14.9 (estimated) 1490 MJ/km Total embodied energy 18.059 GJ/km SCS: Single crystal silicon; PE: PolyEthylene
- 7. Network and traffic Lifetime (10 year) energy, original NSFNET Distance between two neighbouring EDFAs 80 (km) Capacity of each wavelength (B) 40 (Gb/s) Power consumption of a router port (PR) 1000 (W) Power consumption of a transponder (PT) 73 (W) Power consumption of an EDFA (PE) 8 (W) Power consumption of an optical switch (PO) 85 (W) Power consumption of a multiplexer/demultiplexer (PMD) 16 (W) 7
- 8. Optimized Physical Topologies Considering Operational and Embodied energies • Large embodied energy of the optical cable shorter links. • The embodied energy is the major contributor to the total Symmetric traffic, non-bypass Asymmetric traffic, network energy consumption non-bypass • Significant embodied energy savings of 20% and 59% are achieved compared to the original NSFNET topology and the operational-power- optimized topology, respectively resulting in a total energy saving of 47% and 13%.
- 9. Energy-Efficient Data Compression for Optical Networks • Data compression is becoming a widely used technique to save bandwidth which will consequently result in energy savings. • Trade-off between the energy consumption of computational resources and memory required to compress and decompress data and the network energy savings. • Cisco forecasts that 90% of the Internet traffic will be video by 2015. • In [1], the authors considered semantic compression to reduce the video storage space. • YouTube videos can be compressed by a ratio of 20:1 compared to ordinary histogram representations. _______________________________________________________________________________ 1. Jörn Wanke et. al,”Topic Models for Semantics-preserving Video Compression,” ACM International Conference on Multimedia Information Retrieval (ACM MIR), Philadelphia, PA, 2010.
- 10. Power Consumption of Data Compression • In [2], the data compression energy consumption per bit is given as: RC is the data compression ratio A and β are parameters A is given as: ε is a scaling parameter is the maximum data compression ratio β represents the efficiency of the data compression algorithm. ENet is the energy consumption of the network. Therefore: _________________________________________________________________________________________________________ 2. Dan Kilper et. al ”insights on coding and transmission energy in optical networks”, E-energy 2011
- 11. MILP Model for Data compression in IP over WDM networks Objective: minimize Subject to: Including: Flow conservation constraint in the IP layer Linear approximation of the relationship between power consumption of data compression and data compression ratio Limit on the maximum data compression ratio
- 12. Results • The power consumption of Algorithms and Compression Ratios for Different decompression is equal to the types of data power consumption of Traffic Compression Compression compression. type algorithm ratio • We consider a mixture of traffic Text bzip2, ppmd (lossless) 4:1 (video, images, text) to reflect the Image JPEG, GIF, PNG (lossy) 10:1 global Internet traffic where 91% of Video MPEG-4, H.264(lossy) 20:1 the global Internet is video. • Average power savings of 29% and 39% are achieved by the MILP model under the bypass approach for β=1 and β=2, respectively. • Comparable power savings are achieved by the energy-efficient data compression and routing heuristic. • High power savings of 45% and 55% for β=1 and β=2, respectively are achieved under the non-bypass approach. Power consumption under the bypass approach
- 13. Results • The optimal data compression ratio for most of the node pairs varies slightly (between 70%-80%) under both the maximum and minimum traffic demands. Low Traffic Demand (6 am), Bypass High Traffic Demand (10 pm), Bypass • We have also analysed the impact of compression on the BER
- 14. Energy-Efficient BitTorrent • The two content distribution schemes, Client/Server (C/S) and Peer-to-Peer (P2P), account for a high percentage of the Internet traffic. • We investigate the energy consumption of BitTorrent in IP over WDM networks. • We show, by mathematical modelling (MILP) and simulation, that peers’ co- location awareness, known as locality, can help reduce BitTorrent’s cross traffic and consequently reduces the power consumption of BitTorrent on the network side.
- 15. Energy-Efficient BitTorrent • The file is divided into small pieces. • A tracker monitors the group of users currently downloading. • Downloader groups are referred to as swarms and their members as peers. Peers are divided into seeders and leechers. • As a leecher finishes downloading a piece, it selects a fixed number (typically 4) of interested leechers to upload the piece to, ie unchoke, (The choke algorithm). • Tit-for-Tat (TFT) ensures fairness by not allowing peers to download more than they upload. • We consider 160,000 groups of downloaders distributed randomly over the NSFNET network nodes. • Each group consists of 100 members. • File size of 3GB. • Homogeneous system where all the peers have the same upload capacity of 1Mbps. • Optimal Local Rarest First pieces dissemination where Leechers select the least replicated piece in the network to download first. • BitTorrent traffic is 50% of total traffic. • Flash crowd where the majority of leechers arrive soon after a popular content is shared. • We compare BitTorrent to a C/S model with 5 data centers optimally located at nodes 3, 5, 8, 10 and 12 in NSFNET. • The upload capacity and download demands are the same for BitTorrent and C/S scenarios (16Tbps).
- 16. MILP Model for Energy-Efficient BitTorrent Objective: Maximize Setting β=0 gives the original BitTorrent Subject To: Including: Peers download rate constraint Peers upload rate constraints Fairness constraint, Tit-For- TAT (TFT)
- 17. Peer Selection (100 Peer: 30 Seeders and 70 Leechers in Swarm 1) Original BitTorrent (Random Selection) Energy Efficient BitTorrent (Optimized Selection) 17
- 18. Energy-Efficient BitTorrent Heuristic • Energy-Efficient BitTorrent model performs peer selection based on the co-location of peers within the same nodes to minimize energy consumption. • The heuristic tries to mimic this behavior by: • Seeders span the neighboring nodes only. • Leechers are limited to their local nodes as long as there are sufficient number of peers (5 at least), otherwise they span to neighboring nodes.
- 19. Results Average Download Rate Energy-efficient heuristic achieves a13% lower download rate. BitTorrent Power Consumption Non-bypass: MILP avg Power Saving=36% Heuristic avg Power Saving =36% Bypass: MILP avg Power Saving=30% Heuristic avg Power Saving =28%
- 20. Results Energy Consumption Non-bypass: Bypass: MILP average Energy Saving=36% MILP average Energy Saving=30% Heuristic average Energy Saving =25% Heuristic average Energy Saving =15%
- 21. Conclusions • It is essential to consider embodied as well as operational energy if the goal is to minimise the network’s carbon footprint. • Significant embodied energy savings of 20% and 59% are achieved compared to the original NSFNET topology and the operational- power-optimized topology, respectively resulting in a total energy saving of 47% and 13%. • Shown that power savings can be achieved through the use of data compression and appropriate routing heuristics. • Average power savings of 29% and 39% are achieved by the MILP model under the bypass approach for β=1 and β=2, respectively. • High power savings of 45% and 55% for β=1 and β=2, respectively are achieved under the non-bypass approach. • Introducing locality to BitTorrent can lead to a more efficient content distribution scheme compared to C/S with power savings of 30% (bypass) and 36% (nonbypass). 21
- 22. Related Publications 1. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “IP Over WDM Networks Employing Renewable Energy Sources,” IEEE/OSA Journal of Lightwave Technology, vol. 27, No. 1, pp. 3-14, 2011. 2. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “Green IP over WDM Networks with Data Centres,” IEEE/OSA Journal of Lightwave Technology, vol. 27, 2011. 3. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “On the Energy Efficiency of Physical Topology Design for IP over WDM Networks,” IEEE/OSA Journal of Lightwave Technology, vol. 28, 2012. 4. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “Renewable Energy in IP Over WDM Networks,” Proc IEEE 12th International Conference on Transparent Optical Networks ICTON 2010, June 27 - July 1, 2010, Munich, Germany, invited paper. 5. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “hybrid-power IP over WDM network,” Proc IEEE Seventh International Conference on Wireless and Optical Communications Networks WOCN2010, September 2010. 6. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “An Energy Efficient IP over WDM Network,” Proc. IEEE/ACM International Conference on Green Computing and Communications, GREENCOM, Hangzhou, China, Dec. 2010. 7. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “Renewable Energy for Low Carbon Emission IP over WDM networks,” Proc. 15th IEEE Optical Network Design and Modelling conference (ONDM’11), Bologna, Italy, 8-10 Feb 2011. 8. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “Low Carbon Emission IP over WDM network,” IEEE International Conference on Communications (ICC’11), Koyoto, Japan, June 2011. 8. Audzevich, Y., Moore, A., Rice, A., Sohan, R., Timotheou, S., Crowcroft, J., Akoush, S., Hopper, A., Wonfor, A., Wang, H., Penty, R., White, I., Dong, X., El-Gorashi, T. and Elmirghani, J., “Intelligent energy aware networks,” book chapter, published in Handbook of Energy-Aware and Green Computing, Taylor and Francis, invited, 2011. 9. Dong, X., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “Energy-Efficient IP over WDM Networks with Data Centres,” Proc IEEE 12th International Conference on Transparent Optical Networks ICTON 2011, 26 – 30 June, 2011, Stockholm, Sweden, invited paper. 10. Osman, N. I., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “Reduction of Energy Consumption of Video-on-Demand Services using Cache Size Optimization,” Proc IEEE Eighth International Conference on Wireless and Optical Communications Networks WOCN2011, May 2011. 11. Dong, X., Lawey, A.Q., El-Gorashi, T.E.H. and Elmirghani, J.M.H., “Energy Efficient Core Networks,” Proc 16th IEEE Conference on Optical Network Design and Modelling (ONDM’12), 17-20 April, 2012, UK.