In this article we introduce GMU, a genuine partial replication protocol for transactional systems, which exploits an innovative, highly scalable, distributed multiversioning scheme. Unlike existing multiversion-based solutions, GMU does not rely on any global logical clock, which may represent a contention point and a major impairment to system scalability. Also, GMU never aborts read-only transactions and spares them from undergoing distributed validation schemes. This makes GMU particularly efficient in presence of read-intensive workloads, as typical of a wide range of real-world applications. GMU guarantees the Extended Update Serializability (EUS) isolation level. This consistency criterion is particularly attractive as it is sufficiently strong to ensure correctness even for very demanding applications (such as TPC-C), but is also weak enough to allow efficient and scalable implementations, such as GMU. Further, unlike several relaxed consistency models proposed in literature, EUS shows simple and intuitive semantics, thus being an attractive consistency model for ordinary programmers. We integrated GMU in a popular open source in-memory transactional data grid, namely Infinispan. On the basis of a wide experimental study performed on heterogeneous platforms and using industry standard benchmarks (namely TPC-C and YCSB), we show that GMU achieves almost linear scalability and that it introduces reduced overhead, with respect to solutions ensuring non-serializable semantics, in a wide range of workloads.

Replication plays an essential role for in-memory distributed transactional platforms, given that it represents the primary means to ensure data durability. Unfortunately, no single replication technique can ensure optimal performance across a wide range of workloads and system configurations. This paper tackles this problem by presenting MORPHR, a framework that allows to automatically adapt the replication protocol of in-memory transactional platforms according to the current operational conditions. MORPHR presents two key innovative aspects. On one hand, it allows to plug in, in a modular fashion, specialized algorithms to regulate the switching between arbitrary replication protocols. On the other hand, MORPHR relies on state of the art machine learning techniques to autonomously determine the best replication in face of varying workloads. We integrated MORPHR in an open-source in-memory NoSQL data grid, and evaluated it by means of an extensive experimental study. The results highlight that MORPHR is accurate in identifying the best replication strategy in presence of complex realistic workloads, and does so with minimal overhead.

GMU is a recently proposed genuine partial replication protocol for transactional systems that relies on an innovative, fully-decentralized multiversioning scheme to maximize efficiency and scalability. In this paper we tackle one of the key issues that affect the efficiency of GMU-based applications: enhancing their data locality, i.e. the ability to serve transactional reads locally, avoiding remote inter-node communication. To this end we introduce a lightweight caching mechanism that allows for safely accessing asynchronously replicated copies of remote data items while preserving GMU's original consistency criterion and its scalability. We assess the efficiency and effectiveness of the presented solution by means of an extensive experimental analysis, evaluating deployments on large scale public and private cloud infrastructures, and using well known benchmarks for transactional platforms. The results show striking speedups of up to 14 times in read dominated workloads, and a reduction of the network bandwidth by up to one order of magnitude.

This article addresses the problem of self-tuning the data placement in replicated key-value stores. The goal is to automatically optimize replica placement in a way that leverages locality patterns in data accesses, such that internode communication is minimized. To do this efficiently is extremely challenging, as one needs not only to find lightweight and scalable ways to identify the right assignment of data replicas to nodes but also to preserve fast data lookup. The article introduces new techniques that address these challenges. The first challenge is addressed by optimizing, in a decentralized way, the placement of the objects generating the largest number of remote operations for each node. The second challenge is addressed by combining the usage of consistent hashing with a novel data structure, which provides efficient probabilistic data placement. These techniques have been integrated in a popular open-source key-value store. The performance results show that the throughput of the optimized system can be six times better than a baseline system employing the widely used static placement based on consistent hashing.

Replication plays an essential role for in-memory distributed transactional platforms, such as NoSQL data grids, given that it represents the primary mean to ensure data durability. Unfortunately, no single replication technique can ensure optimal performance across a wide range of workloads and system configurations. This paper tackles this problem by presenting MORPHR, a framework that allows to automatically adapt the replication protocol of in-memory transactional platforms according to the current operational conditions. MORPHR presents two key innovative aspects. On one hand, it allows to plug in, in a modular fashion, specialized algorithms to regulate the switching between arbitrary replication protocols. On the other hand, MORPHR relies on state of the art machine learning techniques to autonomously determine the optimal replication in face of varying workloads. We integrated MORPHR in a popular open-source in-memory NoSQL data grid, and evaluated it by means of an extensive experimental study. The results highlight that MORPHR is accurate in identifying the optimal replication strategy in presence of complex, realistic workloads, and does so with minimal overhead.

In this article we introduce GMU, a genuine partial replication protocol for transactional systems, which exploits an innovative, highly scalable, distributed multiversioning scheme. Unlike existing multiversion-based solutions, GMU does not rely on a global logical clock, which represents a contention point and can limit system scalability. Also, GMU never aborts read-only transactions and spares them from distributed validation schemes. This makes GMU particularly efficient in presence of read-intensive workloads, as typical of a wide range of real-world applications. GMU guarantees the Extended Update Serializability (EUS) isolation level. This consistency criterion is particularly attractive as it is sufficiently strong to ensure correctness even for very demanding applications (such as TPC-C), but is also weak enough to allow efficient and scalable implementations, such as GMU. Further, unlike several relaxed consistency models proposed in literature, EUS has simple and intuitive semantics, thus being an attractive, scalable consistency model for ordinary programmers. We integrated the GMU protocol in a popular open source in-memory transactional data grid, namely Infinispan. On the basis of a large scale experimental study performed on heterogeneous experimental platforms and using industry standard benchmarks (namely TPC-C and YCSB), we show that GMU achieves linear scalability and that it introduces negligible overheads (less than 10%), with respect to solutions ensuring non-serializable semantics, in a wide range of workloads.

Nowadays, distributed in-memory caches are increasingly used as a way to improve the performance of applications that require frequent access to large amounts of data. In order to maximize performance and scalability, these platforms typically rely on weakly consistent partial replication mechanisms. These schemes partition the data across the nodes and ensure a predefined (and typically very small) replication degree, thus maximizing the global memory capacity of the platform and ensuring that the cost to ensure replica consistency remains constant as the scale of the platform grows. Moreover, even though several of these platforms provide transactional support, they typically sacrifice consistency, ensuring guarantees that are weaker than classic 1-copy serializability, but that allow for more efficient implementations. This paper proposes and evaluates two partial replication techniques, providing different (weak) consistency guarantees, but having in common the reliance on total order multicast primitives to serialize transactions without incurring in distributed deadlocks, a main source of inefficiency of classical two-phase commit (2PC) based replication mechanisms. We integrate the proposed replication schemes into Infinispan, a prominent open-source distributed in-memory cache, which represents the reference clustering solution for the well-known JBoss AS platform. Our performance evaluation highlights speed-ups of up to 40× when using the proposed algorithms with respect to the native Infinispan replication mechanism, which relies on classic 2PC-based replication.