Energieeffizienz in verteilten Systemen: Modellierung und Simulation
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Energieeffizienz in verteilten Systemen:
Modellierung und Simulation
Helmut Hlavacs
University of ViennaDepartment of Distributed and Multimedia Systems
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FTW Kolloquium 18.5.2010
Energy Efficient ICT
• COST Action IC804 Energy efficiency in large scale distributed systems
• Supported by the European Commission• http://www.cost804.org/• Member States: 17 (+3 pending)• Member Insitutions: ~40
• Chair: Jean-Marc Pierson, IRIT, Toulouse• Vice-Chair, Grant Holder: Helmut Hlavacs, Univ. of Vienna
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FTW Kolloquium 18.5.2010
Themen
WG1: Ongoing evaluation of components
WG2: Modeling energy efficiency
WG3: Adaptive actions
WG4: Characterization of performance-energy saving trade-off
WG5: Dissemination
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FTW Kolloquium 18.5.2010
Distributed Systems
• Networked computing entities• At network edges• Interact with each other• Heterogeneous• Small or large scale• Communication via NW protocols or middleware
abstraction• Distributed algorithms (-> software!)
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FTW Kolloquium 18.5.2010
Optimizing Distributed Systems and Algorithms
• Behavior driven by interaction between nodes and communication systems
• Complex, many parameters• Emergent behavior through local information
• Performance evaluation and optimization– Implement and run (e.g., PlanetLab)– Simulation– Need math. models to understand the performance
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FTW Kolloquium 18.5.2010
Saving Power in Distributed Systems
• Optimize single nodes– Advanced Configuration and Power Interface (ACPI)
• C (idle): suspend to RAM/disk, WakeOnLAN• P (operational: frequency, voltage pairs) states
– Multicores: dectivate single cores– Specialize nodes (e.g. nettops vs. GPU)
• In distributed systems– Optimize parameters– Hardware consolidation
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FTW Kolloquium 18.5.2010
Idle Consumption
Energy Star
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FTW Kolloquium 18.5.2010
Consumption depending on CPU Load
Google 2007
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FTW Kolloquium 18.5.2010
Hardware Consolidation
Requires a model of workload, energy consumption and efficiency, network bandwidth, system performance, QoS, …
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FTW Kolloquium 18.5.2010
Residential ICT
• World wide (2009): over a Billion PCs• EU-25 (2007)
– 2005: ~105 Mio desktop, 24 Mio laptops and 104 Mio monitors (47 Mio flat panel) installed in households
– 2006: broadband 60 Mio subscriber lines in the EU-25,
– End devices in homes contribute a large share of electricity consumption growth in the EU
• UK (2006): residential office equipment ~7 TWh (or 6% of total residential consumption).
• UK (2007): 21% of work PCs never switched off (1.5 TWh)• USA (2007): 16 TWh by office/home PCs• USA (2008): 74 TWh consumed by Internet equipment
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FTW Kolloquium 18.5.2010
Example: File Sharing
• Millions of PCs in households world wide• Long running• Consume large quantities of energy
• Can we make file sharing energy efficient?
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FTW Kolloquium 18.5.2010
File Popularity vs. Rank
• Zipf‘s Law but with exponential tail
Dan, Carlsson,
2010
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FTW Kolloquium 18.5.2010
Energy Efficient File Downloading
• BitTorrent fluid model (Qiu, Srikant 2004)
• Distributed proxies (Hlavacs et al. 2008)
• BitTorrent with proxy (Anastasi et al. 2010)
• Green BitTorrent (Blackburn, Christensen 2009)
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FTW Kolloquium 18.5.2010
BitTorrent
• Most prominent P2P file sharing protocol• Good for popular files• Clients download pieces from a complete source• Start sending missing pieces to each other• Policy agains free riders (choking)• The available bandwidth can be saturated
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FTW Kolloquium 18.5.2010
BitTorrent
Seeder: a peer that has the whole file
Leecher: a peer that has only part of the file
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FTW Kolloquium 18.5.2010
BitTorrent Fluid Model
• Qiu, Srikant, Modeling and Performance Analysis of BitTorrent-Like Peer-to-Peer Networks, SigComm 2004
• Peers are like buckets where data flows into• Data flows with max up/downlink bandwidth• Good realistic model
• Can we use it to investigate energy efficiency?
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FTW Kolloquium 18.5.2010
Model Parameters
• Parameters• x(t)…number of leechers• y(t)…number of seeders• …arrival rate of new leechers• …uploading bandwith of a peer• c…downloading bandwidth of a peer• …abort rate of peers (set to zero in our case)• …rate at which seeders leave the system (=1/)• …effectiveness
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FTW Kolloquium 18.5.2010
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FTW Kolloquium 18.5.2010
Fluid Model
• Bandwidth that is downloaded into peers
• Bandwidth that is uploaded from peers and seeders
• Rate of leechers turning into seeders
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FTW Kolloquium 18.5.2010
Solution of the Differential Equations
Mean download time
The only parameter we can influence in the distributed algorithm is the nice time =1/
System power consumption
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FTW Kolloquium 18.5.2010
Which Nice Time is Optimal?
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FTW Kolloquium 18.5.2010
The Optimal Nice Time
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FTW Kolloquium 18.5.2010
Distributed Hardware Consolidation
• H. Hlavacs, R. Weidlich, K.A. Hummel, A. Houyou, A. Berl, H. de Meer, Distributed Energy Efficiency in Future Home Environments, Annals of Telecommunications 63:7-8, Sept.-Oct. 2008.
• Covers the case for unpopular files• No sharing possible if files are hosted by only one seeder -> use
consolidation• Concentrate parallel downloads on distributed proxies, then move
files to the owners• Proxies are chosen on the fly
– Once a peer wants to download something but does not find a proxy itself, it becomes a proxy
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FTW Kolloquium 18.5.2010
Parallel Downloads• Concentrate downloads on a small number of nodes (e.g. might
have larger up/downlink bandwidth)• Can work only if the downstream goodput is less than the available
upstream bandwidth
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FTW Kolloquium 18.5.2010
Number of Running PCs
No consolidation
With consolidation
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FTW Kolloquium 18.5.2010
Experimental Results
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FTW Kolloquium 18.5.2010
Local Proxy• G. Anastasi, I. Giannetti, A. Passarella, A BitTorrent proxy for Green Internet
file sharing: Design and experimental evaluation, Computer Communications 33 (2010) 794–802
• Concentrate downloads on dedicated local proxy
• Critique
• Requires manual management and maintenance
• Bad scaling of local growth (bandwidth)
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FTW Kolloquium 18.5.2010
Green BitTorrent
• J. Blackburn, K. Christensen, A simulation study of a new Green BitTorrent, Proceedings First International Workshop on Green Communications (GreenComm 2009), Dresden, Germany, June 2009
• Hibernate seeders that currently do not have any uploads• If number of seeders drops below a limit• -> Wake up sleeping seeders per WakeOnLAN
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FTW Kolloquium 18.5.2010
Conclusion
• In large scale distributed systems energy can be saved by
– Local techniques (hardware, OS optimization)– Optimizing parameters of distributed algorithms– Hardware consolidation
• BUT: we have to understand why this works
• -> create models that provide insight