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INTelligent Energy awaRe NETworks. An EPSRC UK Funded Project in Green Networks Professor Jon Crowcroft, Cambridge And P

INTelligent Energy awaRe NETworks. An EPSRC UK Funded Project in Green Networks Professor Jon Crowcroft, Cambridge And Professor Jaafar Elmirghani University of Cambridge, & University of Leeds, UK Jon.crowcorft@cl.cam.ac.uk j.m.h.elmirghani@leeds.ac.uk. Outline.

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INTelligent Energy awaRe NETworks. An EPSRC UK Funded Project in Green Networks Professor Jon Crowcroft, Cambridge And P

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  1. INTelligent Energy awaRe NETworks. • An EPSRC UK Funded Project in Green Networks Professor Jon Crowcroft, Cambridge And Professor Jaafar Elmirghani University of Cambridge, & University of Leeds, UK Jon.crowcorft@cl.cam.ac.uk j.m.h.elmirghani@leeds.ac.uk

  2. Outline • Network energy consumption trends • Motivation • Access network power consumption trends • Power usage efficiency (PUE) • Photonic versus electronic switching • Terminal versus network power consumption • Wired and wireless network energy consumption comparison • Power management approaches • Access, metro, core power consumption • Energy efficient routing in optical networks • Energy per bit • Energy Efficient Routing (EER) • Performance Evaluation of EER • Sleep cycles • Conclusions • References

  3. Outline • Network energy consumption trends • Motivation • Access network power consumption trends • Power usage efficiency (PUE) • Photonic versus electronic switching • Terminal versus network power consumption • Wired and wireless network energy consumption comparison • Power management approaches • Access, metro, core power consumption • Energy efficient routing in optical networks • Energy per bit • Energy Efficient Routing (EER) • Performance Evaluation of EER • Sleep cycles • Co-Optimising • Co-Lo Data Center&Sustainable Energy Production Location • Conclusions • References

  4. Ministry of Internal Affairs and Communications Japan report concluded that ICT equipment (routers, servers, PCs and network systems) consumed 4% of the total electricity generated all over Japan in 2006, a figure of 45,000,000 MWh. Over the past five years the figure has grown by more than 20% [1]. The goal is to reduce this figure and its CO2 impact. It also has to be observed that ICT can help reduce the ecological impact by reducing journeys and introducing more efficient business processes. Studies indicate that for ICT equipment, 50% of CO2 emission is due to the production stage, 45% due to the usage stage and 5% due to the recycling/disposal stage [2]. Therefore it pays to reduce the number of elements in the network and to design architectures and protocols with the per-element usage in mind. Motivation

  5. The figure shows NTT DoCoMo’s energy consumption for communication equipment and number of 3G base stations [1] Energy consumption increase is proportional to the number of installed base stations Access network power consumption trends

  6. PUE is defined as the ratio of “total energy consumption” to “IT equipment energy consumption” [1]. In addition to IT equipment, lighting and air conditioning are the main contributors to the total energy. A typical value of PUE is 1.7 [1]. Therefore it is worthwhile reducing the equipment, designing the usage carefully, but also examining high temperature / uncooled components. Advanced hardware design and removing the cooling element reduces CO2 emission by 20% to 40% it is estimated [3]. Power usage efficiency (PUE)

  7. Photonic switching has much lower energy consumption compared to electronic switching. It has been shown that the power needed per bit for switching is 100 to 1000 times higher in an electronic semiconductor switch as compared to a photonic switch [4]. Photonic versus electronic switching

  8. Typical current mobile terminal power consumption is 0.83Wh per day (including battery charger and terminal) [1]. The corresponding network power consumption is 120Wh [1]. The ratio is 150:1 and therefore the network power consumption is the main contributor to CO2 and effort has to be directed at the network primarily. Significant research effort has gone into extending the mobile terminal battery life by optimising and reducing its power utilisation from 32Wh per day in 1990 to 0.83Wh per day in 2008, a factor of 38 [1, 5]. In comparison the network power consumption has received little attention to date. Terminal versus network power consumption

  9. A university campus network has been reported to use 6% of the total campus power [6]. This amount corresponds approximately to the production of 13 tons of nitrogen oxides (a precursor to ozone), 35 tons of sulphur dioxide and 5100 tons of carbon dioxide as a result of burning fossil fuels. If the associated heating and cooling are added, network energy consumption will double [6]. As a result of monitoring on campus networks [6], it was observed that the power consumption of multi port hubs and switches is almost independent of the number of devices connected to the hub or switch. For example a Cisco switch consumed 40 watts with no devices connected and 42 watts with 16 devices connected. Therefore it pays to reduce the number of elements in the network. It was found on campus [6] that the wired network consumed between 200,000 and 500,000 kWh per year, while the wireless network consumed only 30,000 kWh. A factor of 10 difference between wired and wireless which indicates that the focus has to be on the wired network for energy saving. In the US, ICT accounts for 3% of the total country energy consumption [7, 8]. Wired and wireless network energy consumption comparison

  10. Power management approaches • Internet power usage has continued to increase over the past decade due to (i) more absolute number of devices (ii) higher active power of devices and (iii) more active hours of usage per day [9]. • Can shut off CPU and instruction level (nano to micro seconds), inter-packet or intra-flow CPU halt or shut-off (micro to millisecond) and inter-flow, the entire computer/communication can be turned off (seconds to hours) [9].

  11. Energy consumption is rapidly developing into an environmental and political concern [11, 12]. It has been studied in transport, buildings etc, but less in telecom. There is also concern about constructing and maintaining large data centres and switching centres [13]. Therefore the question has been raised whether the Internet growth will be constrained by power rather than bandwidth [11]. In [11] the conclusion reached is that photonic switching alone will not solve the Internet energy consumption problem (ie need to look at the overall picture including switching, routing protocols etc). In some studies the extra power needed for cooling is assumed to be equal to the power used by the equipment [11, 14]. In the access (based on PON) typical power consumption estimates are 10W for optical network units (ONU) and 100W for optical line terminal which resides in an edge node and connects to several ONUS [11]. A typical edge router in the metro, for example Cisco 12816, consumes 4.21 kW [11, 15]. A typical core router, such as Cisco CRS-1 multishelf system with 92 Tb/s full duplex switching capacity consumes 1020 kW [11, 15]. Access, metro, core power consumption (1)

  12. WDM systems connecting the edge nodes to the core node consume 1.5 kW for every 64 wavelengths [11, 16]. Typically one multiwavelength amplifier is required per fibre, consuming around 6W [11, 16]. The WDM terminal systems connecting core nodes consume 811 W for every 176 channels, while each intermediate line amplifier consumes 622 W for every 176 channels [11, 17]. Access, metro, core power consumption (2)

  13. Current estimates indicate that power consumption accounts for about half the cost of ownership of communication networks [18, 19]. Energy saving in networks is possible due to two main reasons [18]: Networks are provisioned at present for the worst case scenario and many times over provisioned (3 to 5 times). Therefore varying the number of active elements and sections of a network according to demand can save power. The power consumption of the network at present remains substantial even when the network elements are idle. Therefore provisioning just the right amount and introducing sleep operations during idle times can help. Some more on motivation & techniques

  14. Consider energy used in manufacturing as well as operation, therefore reduce the number of network components. Consider PUE, therefore uncooled components and systems are attractive. Photonic switching instead of electronic routing whenever possible. Network power consumption higher than that of the terminal. Wired part still consumes more power than the wireless part. Reduce the “over provisioning” whenever possible. Introduce sleep modes and sleep cycles. Power consumption can account for up to half of the operating costs in networks. Summary

  15. Outline • Network energy consumption trends • Motivation • Access network power consumption trends • Power usage efficiency (PUE) • Photonic versus electronic switching • Terminal versus network power consumption • Wired and wireless network energy consumption comparison • Power management approaches • Access, metro, core power consumption • Energy efficient routing in optical networks • Energy per bit • Energy Efficient Routing (EER) • Performance Evaluation of EER • Sleep cycles • Conclusions • References

  16. Energy efficient routing in optical networks • The energy costs of the network will grow as the amount of data on the network increases. • As the network expands in its capacity, energy consumption in the core network is an important concern for the networking industry. • Some of the possible approaches that can reduce the energy consumption include, • put to sleep some of the wavelength routed nodes and, • at a network level consider changing routes during low traffic periods. • These two approaches decrease the QoS and connectivity. Energy consumption can be reduced so long as the QoS performance remains within SLA

  17. Energy efficient routing in optical networks • Using intelligent optical control planes, lightpaths (or wavelength channels) can have dynamic route selection polices. • By using an efficient optical control management mechanism, network nodes (WRN) can be set to ON or OFF states. • During the OFF cycle the nodes, adopt a sleep mode, cutting down thetraffic routed through them. Traffic originating at the node or destined to the node is handled • The energy reduction achieved due to a sleep cycle is at the cost of decrease in QoS.

  18. Energy per bit and WRN architecture used in the study Wavelength routed node (WRN) used in the network architecture

  19. Energy per Bit • The two different types of energy associated with the optical networks, • Energy associated with the transmission of one optical bit over fiber, • Energy consumed by a router (WRN) for switching an optical signal. Fig: Wavelength routed node (WRN) used in the network architecture • The energy associated with the transmission of 1 bit can be expressed as

  20. Energy per Bit • The power consumed in an optical network path is given by, • If an optical bit traverses H hops, with each hop consisting of k optical inline amplifiers, then the total energy consumed due to WRN and EDFAs is, • The energy per bit across a fiber of length Ln between the nodes n,n+1 is given by, • The energy required to transmit an optical bit across H hops is given by,

  21. Energy per bit

  22. Calculation of energy needed

  23. Calculation of energy needed

  24. Parameters used

  25. Energy Efficient Routing (EER) Algorithm • We propose an Anycasting routing technique to minimize the energy consumptionin the optical network. • Anycasting is defined as the communication paradigm, in which the user has the ability to choose a probable destination from a group of possible destinations unlike deciding it a-priori as in unicast. • An Anycast request is denoted as a two-tuple (s,Ds), where s is the source node initiating a session and Ds is set of probable destinations. • A sleep cycle is defined as the time duration in which a WRN cuts off the traffic routed through it and adopts an OFF state.

  26. Energy Efficient Routing (EER) Algorithm • Definition: We denote the network element vector for a link i as, • The overall NEV for a route R, consisting of links {i, i + 1, . . ., j − 1, j} is given by, • Definition: We define the threshold parameters for a service () as,

  27. Energy Efficient Routing (EER) Algorithm Fig: Burst header packet fields used in the EER algorithm

  28. Performance Evaluation • The National Science Foundation (NSF) network topology and the Italian network are considered in our study. • Random sleep modes were considered. • We have considered the service threshold vector as , NSF network Italian Mesh Network (IMnet)

  29. EER and SPR algorithms in NSFNet Comparison of energy dissipation in the NSF network for Shortest Path Routing (SPR) and Energy Efficient Routing (EER), under varying traffic.

  30. Energy consumed and energy saved for different anycasting orders (k), NSFNet Average power consumption for each lightpath in various anycast scenarios in NSFNet Average energy saving obtained due to anycasting in NSFNet

  31. Blocking probability NSFNet, Energy consumption at different anycasting orders (k), IMNet Average blocking probability in various anycast scenarios in NSFNet Average power consumption for each lightpath in various anycast scenarios in IMNet

  32. Energy saved and blocking probability IMNet Average energy saving obtained due to anycasting in IMNet Average blocking probability in various anycast scenarios in IMNet

  33. Power saving obtained with sleep modes in 4/1 NSFnet and 6/1 IMnet Power saving obtained with sleep modes in 4/1 NSFnet and 6/1 IMnet

  34. Summary Summary • We have computed the energy required to transmit a bit in an optical channel. • We have evaluated the energy consumption based on per hop parameters and node architecture. • Using anycasting communication and efficient BHP signaling, we have minimized the energy consumption in optical burst switched networks. • The energy saving is obtained without significantly scarifying the QoS.

  35. Outline • Network energy consumption trends • Motivation • Access network power consumption trends • Power usage efficiency (PUE) • Photonic versus electronic switching • Terminal versus network power consumption • Wired and wireless network energy consumption comparison • Power management approaches • Access, metro, core power consumption • Energy efficient routing in optical networks • Energy per bit • Energy Efficient Routing (EER) • Performance Evaluation of EER • Sleep cycles • Conclusions • References

  36. Related Work • Different solutions have been proposed to save energy in optical networks • In previous work [1] a static sleep cycles algorithm was proposed to reduce the amount of energy consumed in optical networks. • The network is divided into clusters of nodes. Clusters are set to switch between the ON and OFF modes statically. • Putting some nodes in sleep state means that some traffic flows will have to take longer routes, i.e. energy is saved at the expense of QoS. _____________________________________________________________________________________ [1] B.G.Bathula, J. M. H. Elmirghani, "Green Networks: Energy Efficient Design for Optical Networks," Proceedings of Sixth IEEE/IFIP International Conference on Wireless and Optical Communications Networks (WOCN 2009), Apr. 2009, pp. 1-5.

  37. Intelligent sleep cycles for energy efficiency • In this work we propose an intelligent sleep cycles algorithm where nodes switch between the ON and OFF modes dynamically according to the traffic flows in the network. • The major consideration for any energy saving solution is the trade off between the amount of energy saved and the level of performance degradation. • Dynamic sleep cycles are expected to save more energy while keeping the network performance within acceptable levels. • When nodes go to sleep, they can still transmit and receive traffic but they cannot route traffic. • Nodes monitor the traffic flows passing through. A monitoring window period is defined. • If within the monitoring window the overall blocking probability is less than a certain threshold, some nodes are selected to go to sleep according to the traffic flow and their location in the network topology.

  38. Intelligent sleep cycles for energy efficiency • A node which is the only neighbour for another node cannot go to sleep. • If the network blocking probability exceeds the acceptable (service) blocking probability threshold, the most recent node to sleep wakes up to improve the blocking probability. • In this algorithm, the blocking probability threshold is setup (within SLA) to achieve a trade-off between the amount of energy saved and the network performance.

  39. Intelligent sleep cycles for energy efficiency

  40. In this work we assume a Grid scenario where OBS is implemented as the switching technique and anycasting as the routing paradigm. The anycast algorithm proposed in [1] is implemented. It is based on selecting the Grid resources that achieve the lowest number of hops to reduce the total amount of energy consumed. A more “static” OCS / dynamic OCS optical network can also be considered, Anycasting Algorithm ______________________________________________________________________________________________________ [1] De Leenheer et. al“Anycast Algorithms Supporting Optical Burst Switched Grid Networks”, International Conference on Networking and Services, p 6 pp., 2006.

  41. Simulations were conducted on the high-speed Italian network topology. All nodes were considered to be connected to local area or access networks. Five nodes are selected to serve as Grid resource centres. The network is assumed to deploy 64 data channels and 2 control channels. The wavelength rate is assumed to be 10 Gb/s. We assume that the nodes are equipped with full wavelength conversion capability. Deflection routing is used to reduce the burst loss. We assume an average burst size of 1 MB. Bolzano Milano Verona Trieste Torino Venezia Bolgano Genova Fireze Pisa Ancona Perugia Rome Pescara Napoli Bari Cagliari Potbanza Catanzaro Palermo Catania Simulation Scenario

  42. Simulation Scenario • Traffic is assumed to follow a Poisson distribution. • Traffic generated from the nodes is assumed to be asymmetric: 60% of the traffic is destined to Grid resources and 40% of the traffic is destined to ordinary nodes. • The simulation results evaluate two parameters: the amount of energy saved and the burst blocking probability. • The blocking probability threshold is assumed to be 0.1 and the traffic monitoring window is assumed to be 0.2 seconds. • A range of values of blocking probability and monitoring window size were considered.

  43. Performance Evaluation Effect of the Intelligent Sleep Cycles Algorithm • The intelligent sleep cycles algorithm has succeeded to save an amount of energy of about 9 KWH during the simulation run course for 0.1≤L≤0.9. • However at a network load equal 1, there was no energy saving because network blocking probability at that load is expected to be higher than the blocking probability threshold (therefore the number of nodes that can go to sleep is limited). Effect of Intelligent Sleep Cycles Algorithm on Network Saved Energy

  44. Performance Evaluation Effect of the Intelligent Sleep Cycles Algorithm • There is no difference in the blocking probability between the two curves at very high load (L=1). Because the blocking probability is higher than the blocking probability threshold therefore no node goes to sleep. • There is a slight difference at very low load (L=0.1) because when nodes go to sleep, they don’t affect the performance seriously due to the low congestion at that load. • The impact of the intelligent sleep cycles algorithm is obvious at 0.2≤L≤0.9. Effect of Intelligent Sleep Cycles Algorithm on Network Blocking Probability

  45. Performance Evaluation • It is obvious that at lower loads (L≤0.3), the amount of energy saved is equal for all blocking probability thresholds. • However at high load (0.4≤L≤0.9), the energy saved reduces as the blocking probability threshold decreases, because under a very low blocking probability threshold, it is unlikely to send nodes to sleep. Effect of the Blocking Probability Threshold Effect of Blocking Probability Threshold on the Network Saved Energy

  46. Performance Evaluation • The effect of traffic monitoring window size on the energy saved can be clearly noticed for 0.2≤L≤0.9. When traffic monitoring window size decrease, the amount of saved energy increases. • Small monitoring window sizes allow the exploitation of brief durations when the network blocking probability is low allowing more energy saving. • However, having a very small window size requires a very efficient routing algorithm for quick response to network changes. Effect of the Traffic Monitoring Window Size Effect of Traffic Monitoring Window Size on the Network Saved Energy

  47. We have proposed an intelligent sleep cycles algorithm for energy saving in optical networks. Simulation results have shown that the intelligent sleep cycles algorithm has succeeded to save a considerable amount of energy with a limited performance degradation, specially at lower network loads. Under a lower blocking probability threshold, better performance was achieved but less energy savings were gained. The energy savings per year, is around 100 GWH. Summary

  48. UK tidal/wind -> 10% But intermittent Well connected by fiber Safe control with a lot of current/voltage flying around:) Cheaper to move data (and code) than current Not simple linear with distance Tradeoff is complex (latency is bad for users, but not so bad for many cloud systems - whole system migration (fast xen migration over 1-10Gbps) is doable Data migration or live mirroring (or delta/synch) needs looking at Youtube cite CPU for re-coding video/audio as more energy intensive than storage Not just a convex optimisation problem Can’t just do like dual (ECN/Kelly) Co-Lo Sustainable Energy&Data Centers

  49. Need to capture cost in energy of data v. electricity Akamai used spot price - not useful for us Possibly can capture as a linear programming problem Other work (personal containers) allows us to migrate web service data Backend (sqlservers etc) less obvious Migration Metrics

  50. Etoh, Minoru; Ohya, Tomoyuki; Nakayama, Yuji, “Energy Consumption Issues on Mobile Network Systems,” Energy Consumption Issues on Mobile Network Systems, July 28 2008-Aug. 1 2008 Page(s):365 – 368. T. Origuchi, T. Maeda, M. Yuito, Y. Takeshita, T. Sawada, S. Nishi, and M. Tabata. Eco-efficiency evaluation of 3G Services. In Proc. 7th Int. Conf. on EcoBalance, Nov. 2006. W. Wu. Green bts gives fresh breath. Technical Report 38, Communicate, Huawei Technologies, Feb. 2008. http://www.huawei.com/publications/PublicationIndex.do. H. Hinton. Photonic switching fabrics. IEEE CommunicationsMagazine, 28(4):71–89, April 1990. J. Paradiso and T. Starner. Energy scavenging for mobile & wireless electronics. IEEE Pervasive Computing, 4(1):18–27, 2005. Matthews, H.S.; Hendrickson, C.T.; Hui Min Chong; Woon Sien Loh, “Energy impacts of wired and wireless networks,” Electronics and the Environment, 2002 IEEE International Symposium on, 6-9 May 2002 Page(s):44 – 48. References

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