George Parisis
Anil Madhavapeddy
Jon Crowcroft
ABSTRACT
Datacenter networking has brought high-performance storage systems' research to the foreground once again. Many modern storage systems are built with commodity hardware and TCP/IP networking to save costs. In this paper, we highlight a group of problems that are present in such storage systems and which are all related to the use of TCP. As an alternative, we explore Trevi: a fountain coding-based approach for distributing I/O requests that overcomes these problems while still efficiently scheduling resources across both networking and storage layers. We also discuss how receiver-driven flow and congestion control, in combination with fountain coding, can guide the design of Trevi and provide a viable alternative to TCP for datacenter storage.
- M. Aguilera, R. Janakiraman, and L. Xu. Using erasure codes efficiently for storage in a distributed system. In Proc. of DSN 2005, 2005. Google Scholar
Digital Library
- M. Al-Fares, A. Loukissas, and A. Vahdat. A scalable, commodity data center network architecture. In SIGCOMM, 2008. Google ScholarDigital Library
- R. J. Anderson. The Eternity service. In Pragocrypt, 1996.Google Scholar
- L. S. Brakmo, S. W. O'Malley, and L. L. Peterson. TCP Vegas: new techniques for congestion detection and avoidance. In SIGCOMM, 1994. Google ScholarDigital Library
- P. Breuer, A. Lopez, and A. Ares. The Network Block Device. Linux Journal, March 2000. Google ScholarDigital Library
- P. H. Carns, W. B. Ligon, III, R. B. Ross, and R. Thakur. PVFS: a parallel file system for Linux clusters. In USENIX ALS, 2000. Google ScholarDigital Library
- P. Cataldi, M. Shatarski, M. Grangetto, and E. Magli. Implementation and performance evaluation of LT and raptor codes for multimedia applications. In IIH-MSP, 2006. Google ScholarDigital Library
- A. G. Dimakis, V. Prabhakaran, and K. Ramchandran. Decentralized erasure codes for distributed networked storage. IEEE Transactions on Information Theory, 52: 2809--2816, 2006.Google ScholarCross Ref
- L. Ellenberg. DRBD 9 and device-mapper: Linux block level storage replication. In the Linux System Technology Conference, 2009.Google Scholar
- S. Ghemawat, H. Gobioff, and S.-T. Leung. The Google File System. In SOSP, 2003. Google ScholarDigital Library
- A. Greenberg, J. Hamilton, N. Jain, S. Kandula, C. Kim, P. Lahiri, D. Maltz, P. Patel, and S. Sengupta. VL2: a scalable and flexible data center network. ACM SIGCOMM CCR, 39(4), 2009. Google ScholarDigital Library
- S. Hand and T. Roscoe. Mnemosyne: Peer-to-Peer steganographic storage. In IPTPS, 2002. Google ScholarDigital Library
- C. Hopps. Analysis of an equal-cost multi-path algorithm. RFC 2992, 2000. Google ScholarDigital Library
- M. Luby. LT Codes. In Proc. of FOCS, 2002. Google ScholarDigital Library
- A. Madhavapeddy, R. Mortier, C. Rotsos, D. Scott, B. Singh, T. Gazagnaire, S. Smith, S. Hand, and J. Crowcroft. Unikernels: library operating systems for the cloud. In ASPLOS, 2013. Google ScholarDigital Library
- S. McCanne, V. Jacobson, and M. Vetterli. Receiver-driven layered multicast. In SIGCOMM, 1996. Google ScholarDigital Library
- M. Menth, F. Lehrieder, B. Briscoe, P. Eardley, T. Moncaster, et al. A survey of PCN-based admission control and flow termination. Communications Surveys & Tutorials, IEEE, 12(3): 357--375, 2010. Google ScholarDigital Library
- E. B. Nightingale, J. Elson, J. Fan, O. Hofmann, J. Howell, and Y. Suzue. Flat datacenter storage. In USENIX OSDI, 2012. Google ScholarDigital Library
- Oracle. The Oracle Clustered File System. http://oss.oracle.com/projects/ocfs/.Google Scholar
- G. Parisis, G. Xylomenos, and T. Apostolopoulos. DHTbd: A reliable block-based storage system for high performance clusters. In CCGRID, 2011. Google ScholarDigital Library
- B. Pawlowski, D. Noveck, D. Robinson, and R. Thurlow. The NFS version 4 protocol. In SANE 2000, 2000.Google Scholar
- A. Phanishayee, E. Krevat, V. Vasudevan, D. G. Andersen, G. R. Ganger, G. A. Gibson, and S. Seshan. Measurement and analysis of TCP throughput collapse in cluster-based storage systems. In USENIX FAST, 2008. Google ScholarDigital Library
- C. Raiciu, C. Paasch, S. Barre, A. Ford, M. Honda, F. Duchene, O. Bonaventure, and M. Handley. How hard can it be? designing and implementing a deployable multipath TCP. In Proc. of USENIX NSDI, 2012. Google ScholarDigital Library
- Y. Saito, S. Frolund, A. C. Veitch, A. Merchant, and S. Spence. FAB: building distributed enterprise disk arrays from commodity components. In ASPLOS, 2004. Google ScholarDigital Library
- F. Schmuck and R. Haskin. GPFS: A shared-disk file system for large computing clusters. In of USENIX FAST, 2002. Google ScholarDigital Library
- P. Schwan. Lustre: Building a file system for 1,000-node clusters. In Linux Symposium, 2003.Google Scholar
- A. Shokrollahi. Raptor codes. IEEE Transactions on Information Theory, 52(6): 2551--2567, 2006.Google ScholarDigital Library
- K. Tan and J. Song. A Compound TCP approach for high-speed and long distance networks. In IEEE INFOCOM, 2006.Google ScholarCross Ref
- V. Vasudevan, A. Phanishayee, H. Shah, E. Krevat, D. G. Andersen, G. R. Ganger, G. A. Gibson, and B. Mueller. Safe and effective fine-grained TCP retransmissions for datacenter communication. In SIGCOMM, 2009. Google ScholarDigital Library
- S. A. Weil, S. A. Brandt, E. L. Miller, D. D. E. Long, and C. Maltzahn. Ceph: a scalable, high-performance distributed file system. In USENIX SOSP, 2006.Google Scholar
- B. Welch, M. Unangst, Z. Abbasi, G. Gibson, B. Mueller, J. Small, J. Zelenka, and B. Zhou. Scalable performance of the Panasas parallel file system. In USENIX FAST, 2008. Google ScholarDigital Library
- H. Wu, Z. Feng, C. Guo, and Y. Zhang. ICTCP: Incast congestion control for TCP in data center networks. In Proceedings of CoNEXT, 2010. Google ScholarDigital Library
- Y. Zhang and N. Ansari. On mitigating TCP incast in data center networks. In Proc. of IEEE INFOCOM, 2011.Google ScholarCross Ref
Index Terms
- Trevi: watering down storage hotspots with cool fountain codes
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