Abstract
Cloud computing is becoming one of the most used paradigins to deploy highly available and scalable systems. These systems usually demand the management of huge amounts of data, which cannot be solved with traditional nor replicated database systems as we know them. Recent solutions store data in special key-value structures, in an approach that commonly lacks the consistency provided by transactional guarantees, as it is traded for high scalability and availability. In order to ensure consistent access to the information, the use of transactions is required. However, it is well-known that traditional replication protocols do not scale well for a cloud environment. Here we take a look at current proposals to deploy transactional systems in the cloud and we propose a new system aiming at being a step forward in achieving this goal. We proceed to focus on data partitioning and describe the key role it plays in achieving high scalability.

Abstract
This chapter illustrates how the concepts and algorithms described earlier in this book can be used to build practical database replication systems. This is achieved first by addressing architectural challenges on how required functionality is provided by generally available software componentes and then how different components can be efficiently integrated. A second set of practical challenges arises from experience on how performance assumptions map to actual environments and real workloads. The result is a generic architecture for replicated database management systems, focusing on the interfaces between key components, and then on how different algorithmic and practical optimization options map to real world gains. This shows how consistent database replication is achievable in the current state of the art.

Abstract
Although epidemic or gossip-based multicast is a robust and scalable approach to reliable data dissemination, its inherent redundancy results in high resource consumption on both links and nodes. Tins problem is aggravated in settings that have costlier or resource constrained links as happens in Cloud Computing infrastructures composed by several interconnected data centers across the globe. The goal of this work is therefore to improve the efficiency of gossip-based reliable multicast by reducing the load imposed on those constrained links. hi detail, the proposed CLON protocol combines an overlay that gives preference to local links and a dissemination strategy that takes into account locality. Extensive experimental evaluation using a very large number of simulated nodes shows that this results in a reduction of traffic in constrained links by an order of magnitude, while at the same time preserving the resilience properties that make gossip-based protocols so attractive.

Abstract
Several techniques for database replication using group communication have recently been proposed, namely, the Database State Machine, PostgresR, and the NODO protocol. Although all rely on a totally ordered multicast for consistency, they differ substantially on how multicast is used. This results in different performance trade-offs which are hard to compare as each protocol is presented using a different load scenario and evaluation method. In this paper we evaluate the suitability of such protocols for replication of On-Line Transaction Processing (OLTP) applications in clusters of servers and over wide area networks. This is achieved by implementing them using a common infra-structure and by using a standard workload. The results allows us to select the best protocol regarding performance and scalability in a demanding but realistic usage scenario.

Abstract
Adaptation of system parameters is acknowledged as a requirement to scalable and dependable distributed systems. Unfortunately, adaptation cannot be effective when provided solely by individual system components as the correct decision is often tied to the composition itself and the system as a whole. In fact, proper adaption is a cross-cutting issue: Diagnostic and feedback operations must target multiple components and do it at different abstraction levels. We address this problem with the SERPENTINE middleware platform. By relying on the industry standard JMX as a service interface, it can monitor and operate on a wide range of distributed middleware and application components. By building on a JMX-enabled OSGi runtime, SERPENTINE is able to control the life-cycle of components themselves. The scriptable stateless server and cascading architecture allow for increased dependability and flexibility.

Abstract