Nikolaos Laoutaris
Michael Sirivianos
Pablo Rodriguez
Abstract
Large datacenter operators with sites at multiple locations dimension their key resources according to the peak demand of the geographic area that each site covers. The demand of specific areas follows strong diurnal patterns with high peak to valley ratios that result in poor average utilization across a day. In this paper, we show how to rescue unutilized bandwidth across multiple datacenters and backbone networks and use it for non-real-time applications, such as backups, propagation of bulky updates, and migration of data. Achieving the above is non-trivial since leftover bandwidth appears at different times, for different durations, and at different places in the world.
For this purpose, we have designed, implemented, and validated NetStitcher, a system that employs a network of storage nodes to stitch together unutilized bandwidth, whenever and wherever it exists. It gathers information about leftover resources, uses a store-and-forward algorithm to schedule data transfers, and adapts to resource fluctuations.
We have compared NetStitcher with other bulk transfer mechanisms using both a testbed and a live deployment on a real CDN. Our testbed evaluation shows that NetStitcher outperforms all other mechanisms and can rescue up to five times additional datacenter bandwidth thus making it a valuable tool for datacenter providers. Our live CDN deployment demonstrates that our solution can perform large data transfers at a much lower cost than naive end-to-end or store-and-forward schemes.
Supplemental Material
- Amazon Simple Storage Service. aws.amazon.com/s3/.Google Scholar
- Datacenter map. www.datacentermap.com/datacenters.html.Google Scholar
- Equinix datacenter map. www.equinix.com/data-center-locations/map/.Google Scholar
- Facebook Statistics. www.facebook.com/press/info.php?statistics.Google Scholar
- James Hamilton's blog: Inter-datacenter replication & geo-redundancy. perspectives.mvdirona.com/2010/05/10/InterDatacenterReplicationGeoRedundancy.aspx.Google Scholar
- Personal communication.Google Scholar
- Riverbed Products & Services. http://www.riverbed.com/us/products/index.php.Google Scholar
- Signiant Products. www.signiant.com/Products-content-distribution-management/.Google Scholar
- Telefonica International Wholesale Services Network Map. http://www.e-mergia.com/en/mapaRed.html.Google Scholar
- Forrester Research. The Future of Data Center Wide-Area Networking. info.infineta.com/l/5622/2011-01-27/Y26, 2010.Google Scholar
- Ovum. CDNs The Carrier Opportunity. reports.innovaro.com/reports/cdns-the-carrier-opportunity, 2010.Google Scholar
- B. Ager, F. Schneider, J. Kim, and A. Feldmann. Revisiting Cacheability in Times of User Generated Content. In Global Internet'10.Google Scholar
- D. Andersen, H. Balakrishnan, F. Kaashoek, and R. Morris. Resilient Overlay Networks. In SOSP'01. Google ScholarDigital Library
- D. Beaver, S. Kumar, H. C. Li, J. Sobel, and P. Vajgel. Finding a Needle in Haystack: Facebook's Photo Storage. In OSDI'10. Google ScholarDigital Library
- D. Breitgand, D. Raz, and Y. Shavitt. The Traveling Miser Problem. In IEEE/ACM Transactions on Networking, '06. Google ScholarDigital Library
- G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao. Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services. In NSDI'08. Google ScholarDigital Library
- Y. Chen, S. Jain, V. K. Adhikari, Z.-L. Zhang, and K. Xu. A First Look at Inter-Data Center Traffic Characteristics via Yahoo! Datasets. In INFOCOM '11.Google Scholar
- B. Cohen. Incentives Build Robustness in BitTorrent. In P2P Econ'03.Google Scholar
- T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. MIT Press, 2001. Google ScholarDigital Library
- J. Gray. Long Term Storage Trends and You, 2006. http://research.microsoft.com/~Gray/talks/IO_talk_2006.ppt.Google Scholar
- A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel. The Cost of a Aloud: Research Problems in Data Center Networks. SIGCOMM Comput. Commun. Rev., 39(1), 2009. Google ScholarDigital Library
- A. Greenberg, J. R. Hamilton, N. Jain, S. Kandula, C. Kim, P. Lahiri, D. A. Maltz, P. Patel, and S. Sengupta. VL2: a Scalable and Flexible Data Center Network. In SIGCOMM'09. Google ScholarDigital Library
- M. Grossglauser and D. Tse. Mobility Increases the Capacity of Ad-hoc Wireless Networks. In IEEE/ACM Transactions on Networking, 2001. Google ScholarDigital Library
- S. Jain, K. Fall, and R. Patra. Routing in a Delay Tolerant Network. In SIGCOMM'04. Google ScholarDigital Library
- D. Katabi, I. Bazzi, and X. Yang. A Passive Approach for Detecting Shared Bottlenecks. In Proc. of IEEE ICCN '02.Google Scholar
- R. Kohavi, R. M. Henne, and D. Sommerfield. Practical Guide to Controlled Experiments on the Web: Listen to Your Customers not to the HiPPO. In KDD'07. Google ScholarDigital Library
- R. Kokku, A. Bohra, S. Ganguly, and A. Venkataramani. A Multipath Background Network Architecture. In INFOCOM'07.Google Scholar
- N. Laoutaris and P. Rodriguez. Good Things Come to Those Who (Can) Wait or How to Handle Delay Tolerant Traffic and Make Peace on the Internet. In HotNets-VII'08.Google Scholar
- N. Laoutaris, M. Sirivianos, X. Yang, and P. Rodriguez. Inter-Datacenter Bulk Transfers with NetSticher. Technical report, 2011. Available at the authors' website.Google Scholar
- N. Laoutaris, G. Smaragdakis, P. Rodriguez, and R. Sundaram. Delay Tolerant Bulk Data Transfers on the Internet. In SIGMETRICS'09. Google ScholarDigital Library
- T. Leighton, C. Stein, F. Makedon, E. Tardos, S. Plotkin, and S. Tragoudas. Fast Approximation Algorithms for Multicommodity Flow Problems. In STOC'91. Google ScholarDigital Library
- S. Mitchell and J. Roy. Pulp Python LP Modeler. code.google.com/p/pulp-or/.Google Scholar
- H. Pucha, D. G. Andersen, and M. Kaminsky. Exploiting Similarity for Multi-Source Downloads using File Handprints. In NSDI'07. Google ScholarDigital Library
- H. Pucha, M. Kaminsky, D. Andersen, and M. Kozuch. Adaptive file transfers for diverse environments. In USENIX ATC'08. Google ScholarDigital Library
- F. Shahrokhi and D. Matula. The Maximum Concurrent Flow Problem. In Journal of the ACM, 1990. Google ScholarDigital Library
- S. Shalunov and B. Teitelbaum. Qbone Scavenger Service (QBSS) Definition. Internet2 technical report, Mar. 2001.Google Scholar
- D. Ziegler. Distributed Peta-Scale Data Transfer. www.cs.huji.ac.il/ dhay/IND2011.html.Google Scholar
Index Terms
- Inter-datacenter bulk transfers with netstitcher
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