CumuloNimbo: A Cloud Scalable Multi-tier SQL Database

This article presents an overview of the CumuloNimbo platform. CumuloNimbo is a framework for multi-tier applications that provides scalable and fault-tolerant processing of OLTP workloads. The main novelty of CumuloNimbo is that it provides a standard SQL interface and full transactional support without resorting to sharding and no need to know the workload in advance. Scalability is achieved by distributing request execution and transaction control across many compute nodes while data is persisted in a distributed data store. In this paper we present an overview of the platform.

[1]  Carlo Curino,et al.  Relational Cloud: a Database Service for the cloud , 2011, CIDR.

[2]  José Pereira,et al.  An Effective Scalable SQL Engine for NoSQL Databases , 2013, DAIS.

[3]  Ricardo Jiménez-Peris,et al.  Transactional Failure Recovery for a Distributed Key-Value Store , 2013, Middleware.

[4]  Philip A. Bernstein,et al.  Optimistic concurrency control by melding trees , 2011, Proc. VLDB Endow..

[5]  Akon Dey,et al.  Scalable Transactions across Heterogeneous NoSQL Key-Value Data Stores , 2013, Proc. VLDB Endow..

[6]  Benjamin Reed,et al.  Omid: Lock-free transactional support for distributed data stores , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[7]  Divyakant Agrawal,et al.  ElasTraS: An elastic, scalable, and self-managing transactional database for the cloud , 2013, TODS.

[8]  Sudipta Sengupta,et al.  High Performance Transactions in Deuteronomy , 2015, CIDR.

[9]  Mohamed F. Mokbel,et al.  Locking Key Ranges with Unbundled Transaction Services , 2009, Proc. VLDB Endow..

[10]  Xin Chen,et al.  F1: the fault-tolerant distributed RDBMS supporting google's ad business , 2012, SIGMOD Conference.

[11]  Leslie Lamport,et al.  The part-time parliament , 1998, TOCS.

[12]  Ali Ghodsi,et al.  Coordination Avoidance in Database Systems , 2014, Proc. VLDB Endow..

[13]  Marin Litoiu,et al.  Exploring Alternative Approaches to Implement an Elasticity Policy , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[14]  Tim Kraska,et al.  Building a database on S3 , 2008, SIGMOD Conference.

[15]  Ali Ghodsi,et al.  Eventual Consistency Today: Limitations, Extensions, and Beyond , 2013 .

[16]  Gustavo Alonso,et al.  High availability, elasticity, and strong consistency for massively parallel scans over relational data , 2014, The VLDB Journal.

[17]  Ali Ghodsi,et al.  Bolt-on causal consistency , 2013, SIGMOD '13.

[18]  Ricard Gavaldà,et al.  Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.

[19]  Gerhard Weikum,et al.  Unbundling Transaction Services in the Cloud , 2009, CIDR.

[20]  Raul Castro Fernandez,et al.  Integrating scale out and fault tolerance in stream processing using operator state management , 2013, SIGMOD '13.

[21]  Michael J. Freedman,et al.  Don't settle for eventual: scalable causal consistency for wide-area storage with COPS , 2011, SOSP.

[22]  Brian F. Cooper.  Spanner: Google's globally-distributed database , 2013, SYSTOR '13.

[23]  Divyakant Agrawal,et al.  MaaT: Effective and scalable coordination of distributed transactions in the cloud , 2014, Proc. VLDB Endow..

[24]  Michael Stonebraker,et al.  Staring into the Abyss: An Evaluation of Concurrency Control with One Thousand Cores , 2014, Proc. VLDB Endow..

[25]  Wilson C. Hsieh,et al.  Bigtable: A Distributed Storage System for Structured Data , 2006, TOCS.

[26]  S. Martello,et al.  Algorithms for Knapsack Problems , 1987 .

[27]  Marcos K. Aguilera,et al.  Transactional storage for geo-replicated systems , 2011, SOSP.

[28]  Indranil Gupta,et al.  Client-Centric Benchmarking of Eventual Consistency for Cloud Storage Systems , 2014, ICDCS.

[29]  Xiaohui Gu,et al.  CloudScale: elastic resource scaling for multi-tenant cloud systems , 2011, SoCC.

[30]  Zhou Wei,et al.  CloudTPS: Scalable Transactions for Web Applications in the Cloud , 2012, IEEE Trans. Serv. Comput..

[31]  Barbara Liskov,et al.  Granola: Low-Overhead Distributed Transaction Coordination , 2012, USENIX Annual Technical Conference.

[32]  Yawei Li,et al.  Megastore: Providing Scalable, Highly Available Storage for Interactive Services , 2011, CIDR.

[33]  Samuel Kounev,et al.  Elasticity in Cloud Computing: What It Is, and What It Is Not , 2013, ICAC.

[34]  João Leitão,et al.  ChainReaction: a causal+ consistent datastore based on chain replication , 2013, EuroSys '13.

[35]  Claudio Soriente,et al.  StreamCloud: An Elastic and Scalable Data Streaming System , 2012, IEEE Transactions on Parallel and Distributed Systems.

[36]  Carlo Curino,et al.  Schism , 2010, Proc. VLDB Endow..

[37]  Luís E. T. Rodrigues,et al.  When Scalability Meets Consistency: Genuine Multiversion Update-Serializable Partial Data Replication , 2012, 2012 IEEE 32nd International Conference on Distributed Computing Systems.

[38]  Beng Chin Ooi,et al.  Towards elastic transactional cloud storage with range query support , 2010, Proc. VLDB Endow..

[39]  Philip A. Bernstein,et al.  Adapting microsoft SQL server for cloud computing , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[40]  Tim Kraska,et al.  MDCC: multi-data center consistency , 2012, EuroSys '13.

[41]  Joseph M. Hellerstein,et al.  Flux: an adaptive partitioning operator for continuous query systems , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[42]  José Antonio Lozano,et al.  A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments , 2014, Journal of Grid Computing.

[43]  Divyakant Agrawal,et al.  G-Store: a scalable data store for transactional multi key access in the cloud , 2010, SoCC '10.

[44]  Paolo Romano,et al.  SCORe: A Scalable One-Copy Serializable Partial Replication Protocol , 2012, Middleware.

[45]  Divyakant Agrawal,et al.  Low-Latency Multi-Datacenter Databases using Replicated Commit , 2013, Proc. VLDB Endow..

[46]  Frank Dabek,et al.  Large-scale Incremental Processing Using Distributed Transactions and Notifications , 2010, OSDI.

[47]  Daniel J. Abadi,et al.  Calvin: fast distributed transactions for partitioned database systems , 2012, SIGMOD Conference.