PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication

Geo-replicated data platforms are at the backbone of several large-scale online services. Transactional Causal Consistency (TCC) is an attractive consistency level for building such platforms. TCC avoids many anomalies of eventual consistency, eschews the synchronization costs of strong consistency, and supports interactive read-write transactions. Partial replication is another attractive design choice for building geo-replicated platforms, as it increases the storage capacity and reduces update propagation costs. This paper presents PaRiS, the first TCC system that supports partial replication and implements non-blocking parallel read operations, whose latency is paramount for the performance of read-intensive applications. PaRiS relies on a novel protocol to track dependencies, called Universal Stable Time (UST). By means of a lightweight background gossip process, UST identifies a snapshot of the data that has been installed by every DC in the system. Hence, transactions can consistently read from such a snapshot on any server in any replication site without having to block. Moreover, PaRiS requires only one timestamp to track dependencies and define transactional snapshots, thereby achieving resource efficiency and scalability. We evaluate PaRiS on a large-scale AWS deployment composed of up to 10 replication sites. We show that PaRiS scales well with the number of DCs and partitions, while being able to handle larger data-sets than existing solutions that assume full replication. We also demonstrate a performance gain of non-blocking reads vs. a blocking alternative (up to 1.47x higher throughput with 5.91x lower latency for read-dominated workloads and up to 1.46x higher throughput with 20.56x lower latency for write-heavy workloads)

GEO-REPLICATION IN A REVIEW OF LATENCY AND COST-EFFECTIVENESS

Replication is a data distribution technique for synchronization between databases so that data remains consistent. Replication can overcome data loss problems and perform system recovery quickly if a problem occurs on one of the servers. One of the problems is when a natural disaster occurs at the server location. As a result, if you do not have data replication in different locations, it will cause the system to not run and possibly lose data. Then, geo-replication can reduce latency because the distance between the client and the data center is much closer. The application of geo-replication in general replicates data in all data centers. As a result, the cost of implementation is high because it requires a lot of resources. Because of the various advantages and disadvantages in its application, it is necessary to group geo-replication techniques to make it easier for researchers and technicians to adjust as needed. Therefore, this paper surveys the articles on Geo-replication techniques to implement cost-effectiveness and latency. The articles surveyed included a method for selecting replication sites, a method for reducing round trip time, a method according to data type, and selecting a leader to determine which server node to use. The results of the article survey show that implementing geo-replication for cost-effectiveness is more suitable for use in systems where all users do not need to access all data. Meanwhile, low latency is more suitable for systems used by various types of users. This paper can utilize the techniques that have been reviewed to overcome the problem of cost-effectiveness and latency in implementing Geo-replication

PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication

Geo-replicated data platforms are at the backbone of several large-scale online services. Transactional Causal Consistency (TCC) is an attractive consistency level for building such platforms. TCC avoids many anomalies of eventual consistency, eschews the synchronization costs of strong consistency, and supports interactive read-write transactions. Partial replication is another attractive design choice for building geo-replicated platforms, as it increases the storage capacity and reduces update propagation costs. This paper presents PaRiS, the first TCC system that supports partial replication and implements non-blocking parallel read operations, whose latency is paramount for the performance of read-intensive applications. PaRiS relies on a novel protocol to track dependencies, called Universal Stable Time (UST). By means of a lightweight background gossip process, UST identifies a snapshot of the data that has been installed by every DC in the system. Hence, transactions can consistently read from such a snapshot on any server in any replication site without having to block. Moreover, PaRiS requires only one timestamp to track dependencies and define transactional snapshots, thereby achieving resource efficiency and scalability. We evaluate PaRiS on a large-scale AWS deployment composed of up to 10 replication sites. We show that PaRiS scales well with the number of DCs and partitions, while being able to handle larger data-sets than existing solutions that assume full replication. We also demonstrate a performance gain of non-blocking reads vs. a blocking alternative (up to 1.47x higher throughput with 5.91x lower latency for read-dominated workloads and up to 1.46x higher throughput with 20.56x lower latency for write-heavy workloads)

Causal Consistency and Latency Optimality: Friend or Foe? [Extended Version]

Causal consistency is an attractive consistency model for geo-replicated data stores. It is provably the strongest model that tolerates network partitions. It avoids the long latencies associated with strong consistency, and, especially when using read-only transactions (ROTs), it prevents many of the anomalies of weaker consistency models. Recent work has shown that causal consistency allows "latency-optimal'' ROTs, that are nonblocking, single-round and single-version in terms of communication. On the surface, this latency optimality is very appealing, as the vast majority of applications are assumed to have read-dominated workloads. In this paper, we show that such "latency-optimal'' ROTs induce an extra overhead on writes that is so high that it actually jeopardizes performance even in read-dominated workloads. We show this result from a practical as well as from a theoretical angle. We present the Contrarian protocol that implements "almost latency-optimal'' ROTs, but that does not impose on the writes any of the overheads present in latency-optimal protocols. In Contrarian, ROTs are nonblocking and single-version, but they require two rounds of client-server communication. We experimentally show that this protocol not only achieves higher throughput, but, surprisingly, also provides better latencies for all but the lowest loads and the most read-heavy workloads. We furthermore prove that the extra overhead imposed on writes by latency-optimal ROTs is inherent, i.e., it is not an artifact of the design we consider, and cannot be avoided by any implementation of latency-optimal ROTs. We show in particular that this overhead grows linearly with the number of clients

A novel causally consistent replication protocol with partial geo-replication

Distributed storage systems are a fundamental component of large-scale Internet services. To keep up with the increasing expectations of users regarding availability and latency, the design of data storage systems has evolved to achieve these properties, by exploiting techniques such as partial replication, geo-replication and weaker consistency models. While systems with these characteristics exist, they usually do not provide all these properties or do so in an inefficient manner, not taking full advantage of them. Additionally, weak consistency models, such as eventual consistency, put an excessively high burden on application programmers for writing correct applications, and hence, multiple systems have moved towards providing additional consistency guarantees such as implementing the causal (and causal+) consistency models. In this thesis we approach the existing challenges in designing a causally consistent replication protocol, with a focus on the use of geo and partial data replication. To this end, we present a novel replication protocol, capable of enriching an existing geo and partially replicated datastore with the causal+ consistency model. In addition, this thesis also presents a concrete implementation of the proposed protocol over the popular Cassandra datastore system. This implementation is complemented with experimental results obtained in a realistic scenario, in which we compare our proposal withmultiple configurations of the Cassandra datastore (without causal consistency guarantees) and with other existing alternatives. The results show that our proposed solution is able to achieve a balanced performance, with low data visibility delays and without significant performance penalties

Techniques intelligentes pour la gestion de la cohérence des Big data dans le cloud

Cette thèse aborde le problème de cohérence des données de Bigdata dans le cloud. En effet, nos recherches portent sur l’étude de différentes approches de cohérence adaptative dans le cloud et la proposition d’une nouvelle approche pour l’environnement Edge computing. La gestion de la cohérence a des conséquences majeures pour les systèmes de stockage distribués. Les modèles de cohérence forte nécessitent une synchronisation après chaque mise à jour, ce qui affecte considérablement les performances et la disponibilité du système. À l’inverse, les modèles à faible cohérence offrent de meilleures performances ainsi qu’une meilleure disponibilité des données. Cependant, ces derniers modèles peuvent tolérer trop d’incohérences temporaires sous certaines conditions. Par conséquent, une stratégie de cohérence adaptative est nécessaire pour ajuster, pendant l’exécution, le niveau de cohérence en fonction de la criticité des requêtes ou des données. Cette thèse apporte deux contributions. Dans la première contribution, une analyse comparative des approches de cohérence adaptative existantes est effectuée selon un ensemble de critères de comparaison définis. Ce type de synthèse fournit à l’utilisateur/chercheur une analyse comparative des performances des approches existantes. De plus, il clarifie la pertinence de ces approches pour les systèmes cloud candidats. Dans la seconde contribution, nous proposons MinidoteACE, un nouveau système adaptatif de cohérence qui est une version améliorée de Minidote, un système de cohérence causale pour les applications Edge. Contrairement à Minidote qui ne fournit que la cohérence causale, notre modèle permet aux applications d’exécuter également des requêtes avec des garanties de cohérence plus fortes. Des évaluations expérimentales montrent que le débit ne diminue que de 3,5 % à 10 % lors du remplacement d’une opération causale par une opération forte. Cependant, la latence de mise à jour augmente considérablement pour les opérations fortes jusqu’à trois fois pour une charge de travail où le taux des opérations de mise à jour est de 25 %

Unobtrusive Deferred Update Stabilization for Efficient Geo-Replication

In this paper, we propose a novel approach to manage the throughput vs visibility latency tradeoff that emerges when enforcing causal consistency in geo-replicated systems. Our approach consists in allowing full concurrency when processing local updates and using a deferred local serialisation procedure before shipping updates to remote datacenters. This strategy allows to implement inexpensive mechanisms to ensure system consistency requirements while avoiding intrusive effects on update operations, a major performance limitation of previous systems. We have implemented our approach as a variant of Riak KV. Our evaluation shows that we outperform sequencer-based approaches by almost an order of magnitude in the maximum achievable throughput. Furthermore, unlike previous sequencer-free solutions, our approach reaches nearly optimal remote update visibility latencies without limiting throughput