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Energieeffizienz in verteilten Systemen:

Modellierung und Simulation

Helmut Hlavacs

University of ViennaDepartment of Distributed and Multimedia Systems

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


Themen

WG1: Ongoing evaluation of components

WG2: Modeling energy efficiency

WG3: Adaptive actions

WG4: Characterization of performance-energy saving trade-off

WG5: Dissemination


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!)


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


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


Idle Consumption

Energy Star


Consumption depending on CPU Load

Google 2007


Hardware Consolidation

Requires a model of workload, energy consumption and efficiency, network bandwidth, system performance, QoS, …


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


Example: File Sharing

• Millions of PCs in households world wide• Long running• Consume large quantities of energy

• Can we make file sharing energy efficient?


File Popularity vs. Rank

• Zipf‘s Law but with exponential tail

Dan, Carlsson,

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)


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


BitTorrent

Seeder: a peer that has the whole file

Leecher: a peer that has only part of the file


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?


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



Fluid Model

• Bandwidth that is downloaded into peers

• Bandwidth that is uploaded from peers and seeders

• Rate of leechers turning into seeders


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


Which Nice Time is Optimal?


The Optimal Nice Time


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


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


Number of Running PCs

No consolidation

With consolidation


Experimental Results


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)


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


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