Abstract
Nova proposes a departure from today’s complex monolithic database management systems (DBMSs) as a service using the cloud. It advocates a server-less alternative consisting of a cloud of simple components that communicate using high speed networks. Nova will monitor the workload of an application continuously, configuring the DBMS to use the appropriate implementation of a component most suitable for processing the workload. In response to load fluctuations, it will adjust the knobs of a component to scale it to meet the performance requirements of the application. The vision of Nova is compelling because it adjusts resource usage, preventing either over-provisioning of resources that sit idle or over-utilized resources that yield a low performance, optimizing total cost of ownership. In addition to introducing Nova, this vision paper presents key research challenges that must be addressed to realize Nova. We explore two challenges in detail.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The impact is on the amount of required memory because HDD slows down the rate at which buffered writes are applied and deleted.
References
Alwagait, E., Ghandeharizadeh, S.: A comparison of alternative web service allocation and scheduling policies. In: 2004 IEEE International Conference on Services Computing, Shanghai, China, pp. 319–326, September 2004. https://doi.org/10.1109/SCC.2004.1358021
Alwagait, E., Ghandeharizadeh, S.: DeW: a dependable web services framework. In: RIDE-WS-ECEG 2004, March, Boston, MA, pp. 111–118 (2004). https://doi.org/10.1109/RIDE.2004.1281710
Annamalai, M., et al.: Sharding the shards: managing datastore locality at scale with akkio. In: OSDI, pp. 445–460. USENIX Association, Carlsbad (2018). https://www.usenix.org/conference/osdi18/presentation/annamalai
Binnig, C., Crotty, A., Galakatos, A., Kraska, T., Zamanian, E.: The end of slow networks: it’s time for a redesign. Proc. VLDB Endow. 9(7), 528–539 (2016). https://doi.org/10.14778/2904483.2904485
Bronson, N., Lento, T., Wiener, J.L.: Open data challenges at Facebook. In: 31st IEEE International Conference on Data Engineering, ICDE, Seoul, South Korea, 13–17 April 2015, pp. 1516–1519 (2015)
Cai, Q., et al.: Efficient distributed memory management with RDMA and caching. Proc. VLDB Endow. 11(11), 1604–1617 (2018). https://doi.org/10.14778/3236187.3236209
Cao, Z., Tarasov, V., Tiwari, S., Zadok, E.: Towards better understanding of black-box auto-tuning: a comparative analysis for storage systems. In: USENIX Annual Technical Conference, Berkeley, CA, USA, pp. 893–907 (2018). http://dl.acm.org/citation.cfm?id=3277355.3277441
Carey, M.J., et al.: Towards heterogeneous multimedia information systems: the garlic approach. In: RIDE-DOM, pp. 124–131 (1995)
Cattell, R.: Scalable SQL and NoSQL data stores. SIGMOD Rec. 39, 12–27 (2011)
Das, S., Li, F., Narasayya, V.R., König, A.C.: Automated demand-driven resource scaling in relational Database-as-a-Service. In: Proceedings of the 2016 International Conference on Management of Data, SIGMOD 2016, pp. 1923–1934. ACM, New York (2016). https://doi.org/10.1145/2882903.2903733
Dewitt, D.J., Ghandeharizadeh, S., Schneider, D.A., Bricker, A., Hsiao, H.-I., Rasmussen, R.: The Gamma database machine project. IEEE Trans. Knowl. Data Eng. 2(1), 44–62 (1990). https://doi.org/10.1109/69.50905
Dittrich, K.R., Geppert, A. (eds.): Component Database Systems. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Dong, S., Callaghan, M., Galanis, L., Borthakur, D., Savor, T., Strum, M.: Optimizing space amplification in RocksDB. In: CIDR 2017, 8th Biennial Conference on Innovative Data Systems Research, Chaminade, CA, USA, 8–11 January 2017, Online Proceedings (2017). http://cidrdb.org/cidr2017/papers/p82-dong-cidr17.pdf
Elmore, A., et al.: A demonstration of the BigDAWG polystore system. Proc. VLDB Endow. 8(12), 1908–1911 (2015). https://doi.org/10.14778/2824032.2824098
Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15(3), 200–222 (2001)
Ghandeharizadeh, S., Yap, J., Nguyen, H.: IQ-Twemcached. http://dblab.usc.edu/users/iq/, https://github.com/scdblab/IQ-Twemcached
Ghandeharizadeh, S., Yap, J., Nguyen, H.: Strong consistency in cache augmented SQL systems. In: Middleware, December 2014
Ghandeharizadeh, S., Nguyen, H.: Design, implementation, and evaluation of write-back policy with cache augmented data stores. Technical report 2018-07, USC Database Lab (2018). Submitted to VLDB 2019: In Revision
Ghemawat, S., Dean, J.: LevelDB. https://github.com/google/leveldb. Accessed 15 Nov 2018
Gray, C., Cheriton, D.: Leases: an efficient fault-tolerant mechanism for distributed file cache consistency. SIGOPS Oper. Syst. Rev. 23(5), 202–210 (1989). https://doi.org/10.1145/74851.74870
Kulkarni, C., Kesavan, A., Zhang, T., Ricci, R., Stutsman, R.: Rocksteady: fast migration for low-latency in-memory storage. In: SOSP, pp. 390–405. ACM, New York (2017). https://doi.org/10.1145/3132747.3132784
Lloyd, W., Freedman, M.J., Kaminsky, M., Andersen, D.G.: Don’t settle for eventual: scalable causal consistency for wide-area storage with COPS. In: SOSP, October 2011
Ma, L., Aken, D.V., Hefny, A., Mezerhane, G., Pavlo, A., Gordon, G.J.: Query-based workload forecasting for self-driving database management systems. In: SIGMOD, Houston, TX, USA, 10–15 June 2018, pp. 631–645 (2018). https://doi.org/10.1145/3183713.3196908
Mehdi, S.A., Littley, C., Crooks, N., Alvisi, L., Bronson, N., Lloyd, W.: I can’t believe it’s not causal! Scalable causal consistency with no slowdown cascades. In: NSDI, Boston, MA, USA, 27–29 March 2017, pp. 453–468 (2017). https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/mehdi
Memon, B.N., et al.: RaMP: a lightweight RDMA abstraction for loosely coupled applications. In: 10th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2018). USENIX Association, Boston (2018). https://www.usenix.org/conference/hotcloud18/presentation/memon
O’Neil, P.E., Cheng, E., Gawlick, D., O’Neil, E.J.: The Log-Structured Merge-Tree (LSM-Tree). Acta Inf. 33(4), 351–385 (1996). https://doi.org/10.1007/s002360050048
O’Neil, P.E., Quass, D.: Improved query performance with variant indexes. In: SIGMOD, Tucson, Arizona, USA, 13–15 May 1997, pp. 38–49 (1997). https://doi.org/10.1145/253260.253268
Ousterhout, J., et al.: The RAMCloud storage system. ACM Trans. Comput. Syst. 33(3), 7:1–7:55 (2015). https://doi.org/10.1145/2806887
Pavlo, A., et al.: Self-driving database management systems. In: CIDR 2017, Chaminade, CA, USA, 8–11 January 2017, Online Proceedings (2017). http://cidrdb.org/cidr2017/papers/p42-pavlo-cidr17.pdf
Płuciennik, E., Zgorzałek, K.: The multi-model databases – a review. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2017. CCIS, vol. 716, pp. 141–152. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58274-0_12
Raab, F., Kohler, W., Shah, A.: Overview of the TPC-C benchmark. http://www.tpc.org/tpcc//detail.asp. Accessed 15 Nov 2018
Seltzer, M.: Beyond relational databases. Commun. ACM 51(7), 52–58 (2008). https://doi.org/10.1145/1364782.1364797
Shan, Y., Huang, Y., Chen, Y., Zhang, Y.: LegoOS: a disseminated, distributed OS for hardware resource disaggregation. In: OSDI, pp. 69–87. USENIX Association, Carlsbad (2018). https://www.usenix.org/conference/osdi18/presentation/shan
Smolinski, M.: Impact of storage space configuration on transaction processing performance for relational database in PostgreSQL. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2018. CCIS, vol. 928, pp. 157–167. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99987-6_12
Stonebraker, M., et al.: C-Store: a column-oriented DBMS. In: VLDB, Trondheim, Norway, 30 August–2 September 2005, pp. 553–564 (2005). http://www.vldb.org/archives/website/2005/program/paper/thu/p553-stonebraker.pdf
Tsakalozos, K., Verroios, V., Roussopoulos, M., Delis, A.: Live VM migration under time-constraints in share-nothing IaaS-clouds. IEEE Trans. Parallel Distrib. Syst. 28(8), 2285–2298 (2017). https://doi.org/10.1109/TPDS.2017.2658572
Van Aken, D., Pavlo, A., Gordon, G.J., Zhang, B.: Automatic database management system tuning through large-scale machine learning. In: SIGMOD, pp. 1009–1024. ACM, New York (2017). https://doi.org/10.1145/3035918.3064029
Wang, S., Li, C., Hoffmann, H., Lu, S., Sentosa, W., Kistijantoro, A.I.: Understanding and auto-adjusting performance-sensitive configurations. In: ASPLOS, pp. 154–168. ACM, New York (2018). https://doi.org/10.1145/3173162.3173206
White, B., et al.: An integrated experimental environment for distributed systems and networks. In: OSDI, December 2002
Zamanian, E., Binnig, C., Harris, T., Kraska, T.: The end of a myth: distributed transactions can scale. Proc. VLDB Endow. 10(6), 685–696 (2017). https://doi.org/10.14778/3055330.3055335
Acknowledgments
We gratefully acknowledge use of Utah Emulab network testbed [39] (“Emulab”) for all experimental results presented in this paper. We thank anonymous BDAS 2019 reviewers for their valuable comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ghandeharizadeh, S., Huang, H., Nguyen, H. (2019). Nova: Diffused Database Processing Using Clouds of Components [Vision Paper]. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis. BDAS 2019. Communications in Computer and Information Science, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-030-19093-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-19093-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19092-7
Online ISBN: 978-3-030-19093-4
eBook Packages: Computer ScienceComputer Science (R0)