Abstract
Traditional reliable broadcast protocols fail to scale to large settings. The paper proposes a reliable multicast protocol that integrates two approaches to deal with the large-scale dimension in group communication protocols: gossip-based probabilistic broadcast and semantic reliability. The aim of the resulting protocol is to improve the resiliency of the probabilistic protocol to network congestion by allocating scarce resources to semantically relevant messages. Although intuitively it seems that a straightforward combination of probabilistic and semantic reliable protocols is possible, we show that it offers disappointing results. Instead, we propose an architecture based on a specialized probabilistic semantically reliable layer and show that it produces the desired results. The combined primitive is thus scalable to large number of participants, highly resilient to network and process failures, and delivers a high quality data flow even when the load exceeds the available bandwidth. We present a summary of simulation results that compare different protocol configurations. © 2001 IEEE.

Abstract

Abstract
Unstructured peer-to-peer overlay networks are very resilient to churn and topology changes, while requiring little maintenance cost. Therefore, they are an infrastructure to build highly scalable large-scale services in dynamic networks. Typically, the overlay topology is defined by a peer sampling service that aims at maintaining, in each process, a random partial view of peers in the system. The resulting random unstructured topology is suboptimal when a specific performance metric is considered. On the other hand, structured approaches (for instance, a spanning tree) may optimize a given target performance metric but are highly fragile. In fact, the cost for maintaining structures with strong constraints may easily become prohibitive in highly dynamic networks. This chapter discusses different techniques that aim at combining the advantages of unstructured and structured networks. Namely we focus on two distinct approaches, one based on optimizing the overlay and another based on optimizing the gossip mechanism itself. © 2010 Springer Science+Business Media, LLC.

Abstract
Massive-scale distributed computing is a challenge at our doorstep. The current exponential growth of data calls for massive-scale capabilities of storage and processing. This is being acknowledged by several major Internet players embracing the cloud computing model and offering first generation distributed tuple stores. Having all started from similar requirements, these systems ended up providing a similar service: A simple tuple store interface, that allows applications to insert, query, and remove individual elements. Furthermore, while availability is commonly assumed to be sustained by the massive scale itself, data consistency and freshness is usually severely hindered. By doing so, these services focus on a specific narrow trade-off between consistency, availability, performance, scale, and migration cost, that is much less attractive to common business needs. In this paper we introduce Data Droplets, a novel tuple store that shifts the current trade-off towards the needs of common business users, providing additional consistency guarantees and higher level data processing primitives smoothing the migration path for existing applications. We present a detailed comparison between Data Droplets and existing systems regarding their data model, architecture and trade-offs. Preliminary results of the system's performance under a realistic workload are also presented.

Abstract
Key-value stores hold the unprecedented bulk of the data produced by applications such as social networks. Their scalability and availability requirements often outweigh sacrificing richer data and processing models, and even elementary data consistency. Moreover, existing key-value stores have only random or order based placement strategies. In this paper we exploit arbitrary data relations easily expressed by the application to foster data locality and improve the performance of complex queries common in social network read-intensive workloads. We present a novel data placement strategy, supporting dynamic tags, based on multidimensional locality-preserving mappings. We compare our data placement strategy with the ones used in existing key-value stores under the workload of a typical social network application and show that the proposed correlation-aware data placement strategy offers a major improvement on the system's overall response time and network requirements.

Abstract