Abstract
Approximate agreement has long been relegated to the sidelines compared to exact consensus, with its most notable application being clock synchronisation. Other proposed applications stemming from control theory target multi-agent consensus, namely for sensor stabilisation, coordination in robotics, and trust estimation. Several proposals for approximate agreement follow the Mean Subsequence Reduce approach, simply applying different functions at each phase. However, taking clock synchronisation as an example, applications do not fit neatly into the MSR model: Instead they require adapting the algorithms' internals. Our contribution is two-fold. First, we identify additional configuration points, establishing a more general template of MSR approximate agreement algorithms. We then show how this allows us to implement not only generic algorithms but also those tailored for specific purposes (clock synchronisation). Second, we propose a toolkit for making approximate agreement practical, providing classical implementations as well as allow these to be configured for specific purposes. We validate the implementation with classical algorithms and clock synchronisation.

Abstract
Transactions have been a key issue in database management for a long time and there are a plethora of architectures and algorithms to support and implement them. The current state-of-the-art is focused on storage management and is tightly coupled with its design, leading, for instance, to the need for completely new engines to support new features such as Hybrid Transactional Analytical Processing (HTAP). We address this challenge with a proposal to implement transactional logic in a query language such as SQL. This means that our approach can be layered on existing analytical systems but that the retrieval of a transactional snapshot and the validation of update transactions runs in the server and can take advantage of advanced query execution capabilities of an optimizing query engine. We demonstrate our proposal, TiQuE, on MonetDB and obtain an average 500x improvement in transactional throughput while retaining good performance on analytical queries, making it competitive with the state-of-the-art HTAP systems.

Abstract
Publish/subscribe mechanisms for scalable event dissemination are a core component of many distributed systems ranging from Enterprise Application Integration middleware to news dissemination in the Internet. Hence, a lot of research has been done on overlay networks for efficient decentralized topic-based routing. Specifically, in gossip-based dissemination, bringing nodes with shared interests closer in the overlay makes dissemination more efficient. Unfortunately, this usually requires fully disclosing interests to nearby nodes and impacts reliability due to clustering. In this paper we address this by starting with multiple overlays, one for each topic subscribed, that then separately self-organize to maximize the number of shared physical links, thereby leading to reduced message traffic and maintenance overhead. This is achieved without disclosing a node's topic subscription to any node that isn't subscribed to the same topic and without impacting the robustness of the overlay. Besides presenting the overlay management protocol, we evaluate it using simulation in order to validate our results. © IPTPS 2010.All right reserved.

Abstract
Cloud native database systems provide highly available and scalable services as part of cloud platforms by transparently replicating and partitioning data across automatically managed resources. Some systems, such as Google Spanner, are designed and implemented from scratch. Others, such as Amazon Aurora, derive from traditional database systems for better compatibility but disaggregate storage to cloud services. Unfortunately, because they follow an open-box approach and fork the original code base, they are difficult to implement and maintain. We address this problem with Loom, a replicated and partitioned database system built on top of PostgreSQL that delegates durable storage to a distributed log native to the cloud. Unlike previous disaggregation proposals, Loom is a closed-box approach that uses the original server through existing interfaces to simplify implementation and improve robustness and maintainability. Experimental evaluation achieves 6x higher throughput and 5x lower response time than standard replication and competes with the state of the art in cloud and HPC hardware.