Data grids, large scale web applications generating dynamic content and database service providing pose significant scalability challenges to database engines. Replication is the most common solution but it involves di#cult trade-o#s. The most di#cult one is the choice between scalability and consistency. Commercial systems give up consistency. Research solutions typically either o#er a compromise (limited scalability in exchange for consistency) or impose limitations on the data schema and the workload. In this paper we introduce Ganymed, a database replication middleware intended to provide scalability without sacrificing consistency and avoiding the limitations of existing approaches. The main idea is to use a novel transaction scheduling algorithm that separates update and read-only transactions. Transactions can be submitted to Ganymed through a special JDBC driver. Ganymed then routes updates to a main server and queries to a potentially unlimited number of readonly copies. The system guarantees that all transactions see a consistent data state (snapshot isolation). In the paper we describe the scheduling algorithm, the architecture of Ganymed, and present an extensive performance evaluation that proves the potential of the system.

The widespread use of clusters and web farms has increased the importance of data replication. In this paper, we show how to implement consistent and scalable data replication at the middleware level. We do this by combining transactional concurrency control with group communication primitives. The paper presents different replication protocols, argues their correctness, describes their implementation as part of a generic middleware tool, and proves their feasibility with an extensive performance evaluation. The solution proposed is well suited for a variety of applications including web farms and distributed object platforms.

Replication has become a central element in modern information systems playing a dual role: increase availability and enhance scalability. Unfortunately,

GlobData is a project that aims to design and implement a middleware tool offering the abstraction of a global object database repository. This tool, called Copla, supports transactional access to geographically distributed persistent objects independent of their location. Additionally, it supports replication of data according to different consistency criteria. For this purpose, Copla implements a number of consistency protocols offering different tradeoffs between performance and fault-tolerance. This paper presents the work on strong consistency protocols for the Glob-Data system. Two protocols are presented: a voting protocol and a nonvoting protocol. Both these protocols rely on the use of atomic broadcast as a building block to serialize conflicting transactions. The paper also introduces the total order protocol being developed to support large-scale replication. 1

Abstract. Dynamically adaptive systems sense their environment and adjust themselves to accommodate to changes in order to maximize performance. Depending on the type of change (e.g., modifications of the load, the type of workload, the available resources, the client distribution, etc.), different adjustments have to be made. Coordinating them is already difficult in a centralized system. Doing so in the currently prevalent component-based distributed systems is even more challenging. In this paper, we present an adaptive distributed middleware for data replication that is able to adjust to changes in the amount of load submitted to the different replicas and to the type of workload submitted. Its novelty lies in combining load-balancing techniques with feedback driven adjustments of multiprogramming levels (number of transactions that are allowed to execute concurrently). An extensive performance analysis shows that the proposed adaptive replication solution can provide high throughput, good scalability, and low response times for changing loads and workloads with little overhead. 1

GLOBDATA is a project that aims to design and implement a middleware tool offering the abstraction of a global object database repository. This tool, called COPLA, supports transactional access to geographically distributed persistent objects independent of their location. Additionally, it supports replication of data according to different consistency criteria. For this purpose, COPLA implements a number of consistency protocols offering different tradeoffs between performance and fault-tolerance.

In this paper, we study the safety guarantees of group communication-based database replication techniques. We show that there is a model mismatch between group communication and database, and because of this, classical group communication systems cannot be used to build 2-safe database replication. We propose a new group communication primitive called \emph{end-to-end atomic broadcast} that solves the problem, i.e., can be used to implement 2-safe database replication. We also introduce a new safety criterion, called \emph{group-safety}, that has advantages both over 1-safety and 2-safety. Experimental results show the gain of efficiency of group-safety over lazy replication, which ensures only 1-safety.

In this paper we design a generic, consistent replication architecture that enables transparent database replication and we present the optimizations and tradeo#s of the chosen design. We demonstrate the practicality of our approach by building a prototype that replicates a PostgreSQL database system. We provide experimental results for consistent wide-area database replication. We claim that the use of an optimized synchronization engine is the key to building a practical synchronous replication system for wide-area network settings.

Sprint is a middleware infrastructure for high performance and high availability data management. It extends the functionality of a standalone in-memory database (IMDB) server to a cluster of commodity shared-nothing servers. Applications accessing an IMDB are typically limited by the memory capacity of the machine running the IMDB. Sprint partitions and replicates the database into segments and stores them in several data servers. Applications are then limited by the aggregated memory of the machines in the cluster. Transaction synchronization and commitment rely on total-order multicast. Differently from previous approaches, Sprint does not require accurate failure detection to ensure strong consistency, allowing fast reaction to failures. Experiments conducted on a cluster with 32 data servers using TPC-C and a micro-benchmark showed that Sprint can provide very good performance and scalability.

Middleware-based database replication protocols require few or no changes in the database engine. Thus, they are more portable and flexible than kernel-based protocols, but have coarser-grain information about transaction access data, resulting in reduced concurrency and increased aborts. This paper proposes conflict-aware load-balancing techniques to increase the concurrency and reduce the abort rate of middleware-based replication protocols. Our algorithms assign transactions to replicas so that the number of conflicting transactions executing on distinct servers is reduced and the processing load is equitably distributed over the servers. Experimental evaluation using a prototype of our system running the TPC-C benchmark showed that aborts can be reduced with no penalty in response time.