A single-master database has limited update capacity because a single node handles all updates. A multi-master database potentially has higher update capacity because the load is spread across multiple nodes. However, the need to coordinate updates and ensure durability can generate high network traffic. Reducing network load is particularly important in a cloud environment where the network infrastructure is shared among thousands of tenants. In this paper, we present Taurus MM, a shared-storage multi-master database optimized for cloud environments. It implements two novel algorithms aimed at reducing network traffic plus a number of additional optimizations. The first algorithm is a new type of distributed clock that combines the small size of Lamport clocks with the effective support of distributed snapshots of vector clocks. The second algorithm is a new hybrid page and row locking protocol that significantly reduces the number of lock requests sent over the network. Experimental results on a cluster with up to eight masters demonstrate superior performance compared to Aurora multi-master and CockroachDB.

We propose to demonstrate Rubato DB, a highly scalable NewSQL system, supporting various consistency levels from ACID to BASE for OLTP and big data applications. Rubato DB employs the staged grid architecture with a novel formula based protocol for distributed concurrency control. Our demonstration will present Rubato DB as one NewSQL database management system running on a collection of commodity servers against two of benchmark sets.

Big data applications demand and consequently lead to developments of large-scale data management systems, which provide high scalability by partitioning data across multiple servers. Since conventional transactional access is quite expensive, many real world large-scale distributed systems eschew transactional functionality and adopt semantics of atomic multi-partition operations. Accordingly, BASE, a consistency model weaker than ACID, is commonly used to guarantee availability. In this work, we identify a new consistency model-BASIC (Basic Availability, Scalability, Instant Consistency) that matches the requirements where extra efforts are not needed to manipulate inconsistent soft states. We present a timestamp-based formula protocol for BASIC that can enforce Instant Consistency while achieving linear scalability (via logical formula caching, dynamic timestamp ordering) and achieve Basic Availability in the presence of partial failure and network partition (via partition independence, genuine atomic commit). Our extensive experimental results verify the scalability of BASIC and demonstrate that the limited overhead induced by BASIC pays a reasonable price for keeping all soft states consistent.

This paper proposes a new formula protocol for distributed concurrency control, and specifies a staged grid architecture for highly scalable database management systems. The paper also describes novel implementation techniques of Rubato DB based on the proposed protocol and architecture. We have conducted extensive experiments which clearly show that Rubato DB is highly scalable with efficient performance under both TPC-C and YCSB benchmarks. Our paper verifies that the formula protocol and the staged grid architecture provide a satisfactory solution to one of the important challenges in the database systems: to develop a highly scalable database management system that supports various consistency levels from ACID to BASE.

In the medical field, we are amassing phenomenal amounts of data. Because of understandable ethical and legal responsibility to maintain the privacy, many techniques of anonymization have been proposed to provide means of data publishing without jeopardizing privacy. The strictness of the techniques is putting in question the utility of the health data after severe anonymization.