Abstract
In order to attain the promises of the Cloud Computing paradigm, systems need to be able to transparently adapt to environment changes. Such behavior benefits from the ability to predict those changes in order to handle them seamlessly. In this paper, we present a mechanism to accurately predict the resource usage of distributed key-value datastores. Our mechanism requires offline training but, in contrast with other approaches, it is sufficient to run it only once per hardware configuration and subsequently use it for online prediction of database performance under any circumstance. The mechanism accurately estimates the database resource usage for any request distribution with an average accuracy of 94 %, only by knowing two parameters: (i) cache hit ratio; and (ii) incoming throughput. Both input values can be observed in real time or synthesized for request allocation decisions. This novel approach is sufficiently simple and generic, while simultaneously being suitable for other practical applications.

Abstract
Recently, tuple-stores have become pivotal structures in many information systems. Their ability to handle large datasets makes them important in an era with unprecedented amounts of data being produced and exchanged. However, these tuple-stores typically rely on structured peer-to-peer protocols which assume moderately stable environments. Such assumption does not always hold for very large scale systems sized in the scale of thousands of machines. In this paper we present a novel approach to the design of a tuple-store. Our approach follows a stratified design based on an unstructured substrate. We focus on this substrate and how the use of epidemic protocols allow reaching high dependability and scalability.

Abstract
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.

Abstract
Deduplication of primary storage volumes in a cloud computing environment is increasingly desirable, as the resulting space savings contribute to the cost effectiveness of a large scale multi-tenant infrastructure. However, traditional archival and backup deduplication systems impose prohibitive overhead for latency-sensitive applications deployed at these infrastructures while, current primary deduplication systems rely on special cluster filesystems, centralized components, or restrictive workload assumptions. We present DEDIS, a fully-distributed and dependable system that performs exact and cluster-wide background deduplication of primary storage. DEDIS does not depend on data locality and works on top of any unsophisticated storage backend, centralized or distributed, that exports a basic shared block device interface. The evaluation of an open-source prototype shows that DEDIS scales out and adds negligible overhead

Abstract
Althought optimistic concurrency control protocols have increasingly been used in distributed database management systems, they imply a trade-off between the number of transactions that can be executed concurrently, hence, the peak throughput, and transactions aborted due to conflicts. We propose a novel optimistic concurrency control mechanism that controls transaction abort rate by minimizing the time during which transactions are vulnerable to abort, without compromising throughput. Briefly, we throttle transaction execution with an adaptive mechanism based on the state of the transaction queues while allowing out-of-order execution based on expected transaction latency. Preliminary evaluation shows that this provides a substantial improvement in committed transaction throughput. Copyright 2013 ACM.

Abstract
NoSQL databases manage the bulk of data produced by modern Web applications such as social networks. This stems from their ability to partition and spread data to all available nodes, allowing NoSQL systems to scale. Unfortunately, current solutions' scale out is oblivious to the underlying data access patterns, resulting in both highly skewed load across nodes and suboptimal node configurations. In this paper, we first show that judicious placement of HBase partitions taking into account data access patterns can improve overall throughput by 35%. Next, we go beyond current state of the art elastic systems limited to uninformed replica addition and removal by: i) reconfiguring existing replicas according to access patterns and ii) adding replicas specifically configured to the expected access pattern. MeT is a prototype for a Cloud-enabled framework that can be used alone or in conjunction with OpenStack for the automatic and heterogeneous reconfiguration of a HBase deployment. Our evaluation, conducted using the YCSB workload generator and a TPC-C workload, shows that MeT is able to i) autonomously achieve the performance of a manual configured cluster and ii) quickly reconfigure the cluster according to unpredicted workload changes. © 2013 ACM.