Energieeffizienz in verteilten Systemen: Modellierung und Simulation

1. Energieeffizienz in verteilten Systemen:Modellierung und Simulation Helmut Hlavacs University of Vienna Department of Distributed and Multimedia Systems

2. 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

3. Themen WG1: Ongoing evaluation of components WG2: Modeling energy efficiency WG3: Adaptive actions WG4: Characterization of performance-energy saving trade-off WG5: Dissemination

4. 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!)

5. 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

6. 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

7. Idle Consumption Energy Star

8. Consumption depending on CPU Load Google 2007

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

10. 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: broadband60 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

11. Example: File Sharing • Millions of PCs in households world wide • Long running • Consume large quantities of energy • Can we make file sharing energy efficient?

12. File Popularity vs. Rank • Zipf‘s Law but with exponential tail Dan, Carlsson, 2010

13. 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)

14. 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

15. BitTorrent Seeder: a peer that has the whole file Leecher: a peer that has only part of the file

16. 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?

17. Model Parameters • Parameters • x(t)…number of leechers • y(t)…number of seeders • l…arrival rate of new leechers • m…uploading bandwith of a peer • c…downloading bandwidth of a peer • q…abort rate of peers (set to zero in our case) • g…rate at which seeders leave the system (t=1/g) • h…effectiveness

19. Fluid Model • Bandwidth that is downloaded into peers • Bandwidth that is uploaded from peers and seeders • Rate of leechers turning into seeders

20. Solution of the Differential Equations Mean download time System power consumption Theonlyparameterwecaninfluence in thedistributedalgorithmisthenice time t=1/g

21. Which Nice Time t is Optimal?

22. The Optimal Nice Time

23. 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

24. 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

25. Number of Running PCs No consolidation With consolidation

26. Experimental Results

27. 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 • Requiresmanualmanagement and maintenance • Bad scaling of local growth (bandwidth)

28. 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

29. 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