Abstract not found

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.

Large web or e-commerce sites are frequently hosted on clusters. Successful open-source tools exist for clustering the front tiers of such sites (web servers and application servers). No comparable success has been achieved for scaling the backend databases. An expensive SMP machine is required if the database tier becomes the bottleneck. The few tools that exist for clustering databases are often database-specific and/or proprietary. Clustered JDBC (C-JDBC) addresses this problem. It is a freely available, open-source, flexible and efficient middleware for database clustering. C-JDBC presents a single virtual database to the application through the JDBC interface. It does not require any modification to JDBC-based applications. It furthermore works with any database engine that provides a JDBC driver, without modification to the database engine. The flexible architecture of C-JDBC supports large and complex database cluster architectures offering various performance, fault tolerance and availability tradeoffs. We present the design and the implementation of C-JDBC, as well as some uses of the system in various scenarios. Finally, performance measurements using a clustered implementation of the TPC-W benchmark show the efficiency and scalability of C-JDBC. 1.

This paper studies the problem of updates in decentralised and self-organising P2P systems in which peers have low online probabilities and only local knowledge. The update strategy we propose for this environment is based on a hybrid push/pull rumor spreading algorithm and provides a fully decentralised, efficient and robust communication scheme which offers probabilistic guarantees rather than ensuring strict consistency. We describe a generic analytical model to investigate the utility of our hybrid update propagation scheme from the perspective of communication overhead.

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.

This paper presents in detail an efficient and provably correct algorithm for database replication over partitionable networks. Our algorithm avoids the need for end-toend acknowledgments for each action while supporting network partitions and merges and allowing dynamic instantiation of new replicas. One round of end-to-end acknowledgments is required only upon a membership change event such as a network partition. New actions may be introduced to the system at any point, not only while in a primary component. We show how performance can be further improved for applications that allow relaxation of consistency requirements. We provide experimental results that demonstrate the efficiency of our approach.

The widespread use of clusters and web farms has increased the importance of data replication. In existing protocols, typical distributed system solutions emphasize fault tolerance at the price of performance while database solutions emphasize performance at the price of consistency. In this paper, we explore the use of data replication in a cluster configuration with the objective of providing both fault tolerance and good performance without compromising consistency. We do this by combining transactional concurrency control with group communication primitives. In our approach, transactions are executed at only one site so that not all nodes incur in the overhead of parsing, optimizing, and producing results. To further reduce latency, we use an optimistic multicast approach that overlaps transaction execution with the total order message delivery. The techniques we present in the paper provide correct executions while minimizing overhead and providing higher scalability.

Dynamic content Web sites consist of a front-end Web server, an application server and a back-end database. In this paper we introduce distributed versioning, a new method for scaling the back-end database through replication.

We present a new lazy replication technique, suitable for scaling the back-end database of a dynamic content site using a cluster of commodity computers. Our technique, called conflict-aware scheduling, provides both throughput scaling and 1-copy serializability. It has generally been believed that this combination is hard to achieve through replication because of the growth of the number of conflicts. We take advantage of the presence in a database cluster of a scheduler through which all incoming requests pass. We require that transactions specify the tables that they access at the beginning of the transaction. Using that information, a conflictaware scheduler relies on a sequence-numbering scheme to implement 1-copy serializability, and directs incoming queries in such a way that the number of conflicts is reduced. We evaluate conflict-aware scheduling using the TPC-W e-commerce benchmark. For small clusters of up to eight database replicas, our evaluation is performed through measurements of a web site implementing the TPC-W specification. We use simulation to extend our measurement results to larger clusters, faster database engines, and lower conflict rates. Our results show that conflict-awareness brings considerable benefits compared to both eager and conflictoblivious lazy replication for a large range of cluster sizes, database speeds, and conflict rates. Conflict-aware scheduling provides near-linear throughput scaling up to a large number of database replicas for the browsing and shopping workloads of TPC-W. For the write-heavy ordering workload, throughput scales, but only to a smaller number of replicas. 1

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