Cloud computing infrastructures are the most recent approach to the development and conception of computational systems. Cloud infrastructures are complex environments with various subsystems, each one with their own challenges. Cloud systems should be able to provide the following fundamental property: elasticity. Elasticity is the ability to automatically add and remove instances according to the needs of the system. This is a requirement for pay-per-use billing models.Various open source software solutions allow companies and institutions to build their own Cloud infrastructure. However, in most of these, the elasticity feature is quite immature. Monitoring and timely adapting the active resources of a Cloud computing infrastructure is key to provide the elasticity required by diverse, multi-tenant and pay-peruse business models.In this paper, we propose Elastack, an automated monitoring and adaptive system, generic enough to be applied to existing IaaS frameworks, and intended to enable the elasticity they currently lack. Our approach offers any Cloud infrastructure the mechanisms to implement automated monitoring and adaptation as well as the flexibility to go beyond these. We evaluate Elastack by integrating it with the OpenStack showing how easy it is to add these important features with a minimum, almost imperceptible, amount of modifications to the default installation.

Recently, third party solutions for database replication have been enjoying an increasing popularity. Such proposals address a diversity of user requirements, namely preventing conflicting updates without the overhead of synchronous replication; clustering for scalability and availability; and heterogeneous replicas for specialized queries. Unfortunately, the lack of native support from database vendors for third party replication forces implementors to either modify the database server, restricting portability, or to develop a middleware wrapper, which causes a performance overhead. This paper addresses this problem with a novel architecture and programming interface for replication, such that different strategies can be efficiently implemented on * Parts of this extended report were published in the Proceedings of the 6th IEEE Internacional Symposium on Network Computing and Applications (NCA '07), Boston, MA, USA. 2007. any compliant database management system in a cost-effective manner. The contribution is twofold. First we propose a reflective model of transaction processing and explain how it can be used to achieve replication. Then we implement the proposed architecture in Apache Derby, PostgreSQL, and Sequoia and evaluate the PostgreSQL implementation with the TPC-W industry standard benchmark.

Very large scale distributed systems provide some of the most interesting research challenges while at the same time being increasingly required by nowadays applications. The escalation in the amount of connected devices and data being produced and exchanged, demands new data management systems. Although new data stores are continuously being proposed, they are not suitable for very large scale environments. The high levels of churn and constant dynamics found in very large scale systems demand robust, proactive and unstructured approaches to data management. In this paper we propose a novel data store solely based on epidemic (or gossip-based) protocols. It leverages the capacity of these protocols to provide data persistence guarantees even in highly dynamic, massive scale systems. We provide an open source prototype of the data store and correspondent evaluation.