Abstract
The sheer volumes of data handled by today's Internet services demand uncompromising scalability from the persistence substrates. Such demands have been successfully addressed by highly decentralized key-value stores invariably governed by a distributed hash table. The availability of these structured overlays rests on the assumption of a moderately stable environment. However, as scale grows with unprecedented numbers of nodes the occurrence of faults and churn becomes the norm rather than the exception, precluding the adoption of rigid control over the network's organization. In this position paper we outline the major ideas of a novel architecture designed to handle today's very large scale demand and its inherent dynamism. The approach rests on the well-known reliability and scalability properties of epidemic protocols to minimize the impact of churn. We identify several challenges that such an approach implies and speculate on possible solutions to ensure data availability and adequate access performance. © 2011 IEEE.

Abstract
In epidemic or gossip-based multicast protocols, each node simply relays each message to some random neighbors, such that all destinations receive it at least once with high probability. In sharp contrast, structured multicast protocols explicitly build and use a spanning tree to take advantage of efficient paths, and aim at having each message received exactly once. Unfortunately, when failures occur, the tree must be rebuilt. Gossiping thus provides simplicity and resilience at the expense of performance and resource efficiency. In this paper we propose a novel technique that exploits knowledge about the environment to schedide payload transmission when gossiping. The resulting protocol retains the desirable qualities of gossip, but approximates the performance of structured multicast. In some sense, instead of imposing structure by construction, we let it emerge from the operation of the gossip protocol. Experimental evaluation shows that this approach is effective even when knowledge about the environment is only approximate.

Abstract
Database replication based on group communication systems has recently been proposed as an efficient and resilient solution for large-scale data management. However, its evaluation has been conducted either on simplistic simulation models, which fail to assess concrete implementations, or on complete system implementations which are costly to test with realistic large-scale scenarios. This paper presents a tool that combines implementations of replication and communication protocols under study with simulated network, database engine, and traffic generator models. Replication components can therefore be subjected to realistic large scale loads in a variety of scenarios, including fault-injection, while at the same time providing global observation and control. The paper shows first how the model is configured and validated to closely reproduce the behavior of a real system, and then how it is applied, allowing us to derive interesting conclusions both on replication and communication protocols and on their implementations.

Abstract
Partial replication is an alluring technique to ensure the reliability of very large and geographically distributed databases while, at the same time, offering good performance. By correctly exploiting access locality most transactions become confined to a small subset of the database replicas thus reducing processing, storage access and communication overhead associated with replication. The advantages of partial replication have however to be weighted against the added complexity that is required to manage it. In fact, if the chosen replica configuration prevents the local execution of transactions or if the overhead of consistency protocols offsets the savings of locality, potential gains cannot be realized. These issues are heavily dependent on the application used for evaluation and render simplistic benchmarks useless. In this paper, we present a detailed analysis of Partial Database State Machine (PDBSM) replication by comparing alternative partial replication protocols with full replication. This is done using a realistic scenario based on a detailed network simulator and access patterns from an industry standard database benchmark. The results obtained allow us to identify the best configuration for typical on-line transaction processing applications.

Abstract
Slicing a large-scale distributed system is the process of autonomously partitioning its nodes into k groups, named slices. Slicing is associated to an order on node-specific criteria, such as available storage, uptime, or bandwidth. Each slice corresponds to the nodes between two quantiles in a virtual ranking according to the criteria. For instance, a system can be split in three groups, one with nodes with the lowest uptimes, one with nodes with the highest uptimes, and one in the middle. Such a partitioning can be used by applications to assign different tasks to different groups of nodes, e.g., assigning critical tasks to the more powerful or stable nodes and less critical tasks to other slices. Assigning a slice to each node in a large-scale distributed system, where no global knowledge of nodes' criteria exists, is not trivial. Recently, much research effort was dedicated to guaranteeing a fast and correct convergence in comparison to a global sort of the nodes. Unfortunately, state-of-the-art slicing protocols exhibit flaws that preclude their application in real scenarios, in particular with respect to cost and stability. In this paper, we identify steadiness issues where nodes in a slice border constantly exchange slice and large memory requirements for adequate convergence, and provide practical solutions for the two. Our solutions are generic and can be applied to two different state-of-the-art slicing protocols with little effort and while preserving the desirable properties of each. The effectiveness of the proposed solutions is extensively studied in several simulated experiments. © 2012 IFIP International Federation for Information Processing.

Abstract
Consensus is an abstraction of a variety of important challenges in dependable distributed systems. Thus a large body of theoretical knowledge is focused on modeling and solving consensus within different system assumptions. However, moving from theory to practice imposes compromises and design decisions that may impact the elegance, trade-offs and correctness of theoretical appealing consensus protocols. In this paper we present the implementation and detailed analysis, in a real environment with a large number of nodes, of mutable consensus, a theoretical appealing protocol able to offer a wide range of trade-offs (called mutations) between decision latency and message complexity. The analysis sheds light on the fundamental behavior of the mutations, and leads to the identification of problems related to the real environment. Such problems are addressed without ever affecting the correctness of the theoretical proposal.