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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.firstname.lastname@example.org email@example.com. Outline.
Professor Jon Crowcroft, Cambridge
And Professor Jaafar Elmirghani
University of Cambridge, &
University of Leeds, UK
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 . 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
Energy consumption increase is proportional to the number of installed base stationsAccess network power consumption trends
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 .
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 .Power usage efficiency (PUE)
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 .Photonic versus electronic switching
The corresponding network power consumption is 120Wh .
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
As a result of monitoring on campus networks , 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  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
There is also concern about constructing and maintaining large data centres and switching centres .
Therefore the question has been raised whether the Internet growth will be constrained by power rather than bandwidth .
In  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 .
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)
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)
Energy saving in networks is possible due to two main reasons :
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
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
Wavelength routed node (WRN) used in the network architecture
Fig: Wavelength routed node (WRN) used in the network architecture
Fig: Burst header packet fields used in the EER algorithm
Italian Mesh Network (IMnet)
Comparison of energy dissipation in the NSF network for Shortest Path Routing (SPR) and Energy Efficient Routing (EER), under varying traffic.
Average power consumption for each lightpath in various anycast scenarios in NSFNet
Average energy saving obtained due to anycasting in NSFNet
Average blocking probability in various anycast scenarios in NSFNet
Average power consumption for each lightpath in various anycast scenarios in IMNet
Average energy saving obtained due to anycasting in IMNet
Average blocking probability in various anycast scenarios in IMNet
Power saving obtained with sleep modes in 4/1 NSFnet and 6/1 IMnet
 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.
The anycast algorithm proposed in  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
 De Leenheer et. al“Anycast Algorithms Supporting Optical Burst Switched Grid Networks”, International Conference on Networking and Services, p 6 pp., 2006.
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.
Effect of the Intelligent Sleep Cycles Algorithm
Effect of Intelligent Sleep Cycles Algorithm on Network Saved Energy
Effect of the Intelligent Sleep Cycles Algorithm
Effect of Intelligent Sleep Cycles Algorithm on Network Blocking Probability
Effect of the Blocking Probability Threshold
Effect of Blocking Probability Threshold on the Network Saved Energy
Effect of the Traffic Monitoring Window Size
Effect of Traffic Monitoring Window Size on the Network Saved Energy
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
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
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 obviousMigration Metrics
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
Cisco Data Sheets. [Online]. http://www.cisco.com
Lucent Technologies Data Sheets. [Online]. http://www.lucent.com/eon
Fujitsu Data Sheets. [Online]. http://www.fujitsu.com
Sergiu Nedevschi, Lucian Popa, Gianluca Iannaccone, Sylvia Ratnasamy and David Wetherall, “Reducing Network Energy Consumption via Rate-Adaptation and Sleeping,” Technical Report No. UCB/EECS-2007-128 http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-128.html, October 29, 2007.
E. Miranda and L. McGarry. Power/thermal impact of networking computing. In Cisco System Research Symposium, August, 2006.References