Towards Transactional Data Management over the Cloud

2013

Eventual consistency improves the scalability of large datasets in cloud systems. We propose a novel technique for managing different levels of replica consistency in a replicated relational DBMS. To this end, data is partitioned and managed by a partial replication protocol that is able to define a hierarchy of nodes with a lazy update propagation. Nodes in different layers of the hierarchy may maintain different versions of their assigned partitions. Transactions are tagged with an allowance parameter k that specifies the maximum degree of data outdatedness tolerated by them. As a result, different degrees of transaction criticality can be set and non-critical transactions may be completed without blocking nor compromising the critical ones.

ACM Transactions on Database Systems, 2014

As more data management software is designed for deployment in public and private clouds, or on a cluster of commodity servers, new distributed storage systems increasingly achieve high data access throughput via partitioning and replication. In order to achieve high scalability, however, today's systems generally reduce transactional support, disallowing single transactions from spanning multiple partitions. This article describes Calvin, a practical transaction scheduling and data replication layer that uses a deterministic ordering guarantee to significantly reduce the normally prohibitive contention costs associated with distributed transactions. This allows near-linear scalability on a cluster of commodity machines, without eliminating traditional transactional guarantees, introducing a single point of failure, or requiring application developers to reason about data partitioning. By replicating transaction inputs instead of transactional actions, Calvin is able to support ...

2007 International Multi-Conference on Computing in the Global Information Technology (ICCGI'07), 2007

Journal of Internet Services and Applications

Cloud computing is a general term that involves delivering hosted services over the Internet. With the accelerated growth of the volume of data used by applications, many organizations have moved their data into cloud servers to provide scalable, reliable and highly available services. A particularly challenging issue that arises in the context of cloud storage systems with geographically-distributed data replication is how to reach a consistent state for all replicas. This survey reviews major aspects related to consistency issues in cloud data storage systems, categorizing recently proposed methods into three categories: (1) fixed consistency methods, (2) configurable consistency methods and (3) consistency monitoring methods.

2009

Cloud computing platforms provide scalability and high availability properties for web applications but they sacrifice data consistency at the same time. However, many applications cannot afford any data inconsistency. We present a scalable transaction manager for cloud database services to execute ACID transactions of web applications, even in the presence of server failures. We demonstrate the scalability of our system using a prototype implementation, and show that it scales linearly to at least 40 nodes sustaining a maximum throughput of 7286 transactions per second.

2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), 2019

Existing relational database systems often suffer from rapid increases or significant variability of transactional workloads but lack support for scalability or elasticity. Database replication has been employed to scale workload performance but past approaches make various performance versus consistency tradeoffs and typically lack the mechanisms and policies for dynamically adding and removing replicas. This paper presents Hihooi, a replication-based middleware system that is able to achieve scalability, strong consistency, and elasticity for existing transactional databases. These features are enabled by (i) a novel replication algorithm for propagating database modifications asynchronously and consistently to all replicas at high speeds, and (ii) a new routing algorithm for directing incoming transactions to consistent replicas. Our experimental evaluation validates the high scalability and elasticity benefits offered by Hihooi, which form the key ingredients towards a truly auto-scaling system.

This paper introduces a new transactional "database-as-a-service" (DBaaS) called Relational Cloud. A DBaaS promises to move much of the operational burden of provisioning, configuration, scaling, performance tuning, backup, privacy, and access control from the database users to the service operator, offering lower overall costs to users. Early DBaaS efforts include Amazon RDS and Microsoft SQL Azure, which are promising in terms of establishing the market need for such a service, but which do not address three important challenges: efficient multi-tenancy, elastic scalability, and database privacy. We argue that these three challenges must be overcome before outsourcing database software and management becomes attractive to many users, and cost-effective for service providers. The key technical features of Relational Cloud include: (1) a workload-aware approach to multi-tenancy that identifies the workloads that can be co-located on a database server, achieving higher consolidation and better performance than existing approaches; (2) the use of a graph-based data partitioning algorithm to achieve near-linear elastic scale-out even for complex transactional workloads; and (3) an adjustable security scheme that enables SQL queries to run over encrypted data, including ordering operations, aggregates, and joins. An underlying theme in the design of the components of Relational Cloud is the notion of workload awareness: by monitoring query patterns and data accesses, the system obtains information useful for various optimization and security functions, reducing the configuration effort for users and operators.