A pragmatic protocol for database replication in interconnected clusters

Multi-master update everywhere database replication, as achieved by protocols based on group communication such as DBSM and Postgres-R, addresses both performance and availability. By scaling it to wide area networks, one could save costly bandwidth and avoid large round-trips to a distant master server. Also, by ensuring that updates are safely stored at a remote site within transaction boundaries, disaster recovery is guaranteed. Unfortunately, scaling existing cluster based replication protocols is troublesome. In this paper we present a database replication protocol based on group communication that targets interconnected clusters. In contrast with previous proposals, it uses a separate multicast group for each cluster and thus does not impose any additional requirements on group communication, easing implementation and deployment in a real setting. Nonetheless, the protocol ensures one-copy equivalence while allowing all sites to execute update transactions. Experimental evaluation using the workload of the industry standard TPC-C benchmark confirms the advantages of the approach

Rethinking State-Machine Replication for Parallelism

State-machine replication, a fundamental approach to designing fault-tolerant services, requires commands to be executed in the same order by all replicas. Moreover, command execution must be deterministic: each replica must produce the same output upon executing the same sequence of commands. These requirements usually result in single-threaded replicas, which hinders service performance. This paper introduces Parallel State-Machine Replication (P-SMR), a new approach to parallelism in state-machine replication. P-SMR scales better than previous proposals since no component plays a centralizing role in the execution of independent commands---those that can be executed concurrently, as defined by the service. The paper introduces P-SMR, describes a "commodified architecture" to implement it, and compares its performance to other proposals using a key-value store and a networked file system

Parallel Deferred Update Replication

Deferred update replication (DUR) is an established approach to implementing highly efficient and available storage. While the throughput of read-only transactions scales linearly with the number of deployed replicas in DUR, the throughput of update transactions experiences limited improvements as replicas are added. This paper presents Parallel Deferred Update Replication (P-DUR), a variation of classical DUR that scales both read-only and update transactions with the number of cores available in a replica. In addition to introducing the new approach, we describe its full implementation and compare its performance to classical DUR and to Berkeley DB, a well-known standalone database

Optimistic Parallel State-Machine Replication

State-machine replication, a fundamental approach to fault tolerance, requires replicas to execute commands deterministically, which usually results in sequential execution of commands. Sequential execution limits performance and underuses servers, which are increasingly parallel (i.e., multicore). To narrow the gap between state-machine replication requirements and the characteristics of modern servers, researchers have recently come up with alternative execution models. This paper surveys existing approaches to parallel state-machine replication and proposes a novel optimistic protocol that inherits the scalable features of previous techniques. Using a replicated B+-tree service, we demonstrate in the paper that our protocol outperforms the most efficient techniques by a factor of 2.4 times

DCCast: Efficient Point to Multipoint Transfers Across Datacenters

Using multiple datacenters allows for higher availability, load balancing and reduced latency to customers of cloud services. To distribute multiple copies of data, cloud providers depend on inter-datacenter WANs that ought to be used efficiently considering their limited capacity and the ever-increasing data demands. In this paper, we focus on applications that transfer objects from one datacenter to several datacenters over dedicated inter-datacenter networks. We present DCCast, a centralized Point to Multi-Point (P2MP) algorithm that uses forwarding trees to efficiently deliver an object from a source datacenter to required destination datacenters. With low computational overhead, DCCast selects forwarding trees that minimize bandwidth usage and balance load across all links. With simulation experiments on Google's GScale network, we show that DCCast can reduce total bandwidth usage and tail Transfer Completion Times (TCT) by up to $50\%$ compared to delivering the same objects via independent point-to-point (P2P) transfers.Comment: 9th USENIX Workshop on Hot Topics in Cloud Computing, https://www.usenix.org/conference/hotcloud17/program/presentation/noormohammadpou

Maintaining consistency in distributed systems

In systems designed as assemblies of independently developed components, concurrent access to data or data structures normally arises within individual programs, and is controlled using mutual exclusion constructs, such as semaphores and monitors. Where data is persistent and/or sets of operation are related to one another, transactions or linearizability may be more appropriate. Systems that incorporate cooperative styles of distributed execution often replicate or distribute data within groups of components. In these cases, group oriented consistency properties must be maintained, and tools based on the virtual synchrony execution model greatly simplify the task confronting an application developer. All three styles of distributed computing are likely to be seen in future systems - often, within the same application. This leads us to propose an integrated approach that permits applications that use virtual synchrony with concurrent objects that respect a linearizability constraint, and vice versa. Transactional subsystems are treated as a special case of linearizability

Building global and scalable systems with atomic multicast

The rise of worldwide Internet-scale services demands large distributed systems. Indeed, when handling several millions of users, it is common to operate thousands of servers spread across the globe. Here, replication plays a central role, as it contributes to improve the user experience by hiding failures and by providing acceptable latency. In this thesis, we claim that atomic multicast, with strong and well-defined properties, is the appropriate abstraction to efficiently design and implement globally scalable distributed systems. Internet-scale services rely on data partitioning and replication to provide scalable performance and high availability. Moreover, to reduce user-perceived response times and tolerate disasters (i.e., the failure of a whole datacenter), services are increasingly becoming geographically distributed. Data partitioning and replication, combined with local and geographical distribution, introduce daunting challenges, including the need to carefully order requests among replicas and partitions. One way to tackle this problem is to use group communication primitives that encapsulate order requirements. While replication is a common technique used to design such reliable distributed systems, to cope with the requirements of modern cloud based ``always-on'' applications, replication protocols must additionally allow for throughput scalability and dynamic reconfiguration, that is, on-demand replacement or provisioning of system resources. We propose a dynamic atomic multicast protocol which fulfills these requirements. It allows to dynamically add and remove resources to an online replicated state machine and to recover crashed processes. Major efforts have been spent in recent years to improve the performance, scalability and reliability of distributed systems. In order to hide the complexity of designing distributed applications, many proposals provide efficient high-level communication abstractions. Since the implementation of a production-ready system based on this abstraction is still a major task, we further propose to expose our protocol to developers in the form of distributed data structures. B-trees for example, are commonly used in different kinds of applications, including database indexes or file systems. Providing a distributed, fault-tolerant and scalable data structure would help developers to integrate their applications in a distribution transparent manner. This work describes how to build reliable and scalable distributed systems based on atomic multicast and demonstrates their capabilities by an implementation of a distributed ordered map that supports dynamic re-partitioning and fast recovery. To substantiate our claim, we ported an existing SQL database atop of our distributed lock-free data structure. Here, replication plays a central role, as it contributes to improve the user experience by hiding failures and by providing acceptable latency. In this thesis, we claim that atomic multicast, with strong and well-defined properties, is the appropriate abstraction to efficiently design and implement globally scalable distributed systems. Internet-scale services rely on data partitioning and replication to provide scalable performance and high availability. Moreover, to reduce user-perceived response times and tolerate disasters (i.e., the failure of a whole datacenter), services are increasingly becoming geographically distributed. Data partitioning and replication, combined with local and geographical distribution, introduce daunting challenges, including the need to carefully order requests among replicas and partitions. One way to tackle this problem is to use group communication primitives that encapsulate order requirements. While replication is a common technique used to design such reliable distributed systems, to cope with the requirements of modern cloud based ``always-on'' applications, replication protocols must additionally allow for throughput scalability and dynamic reconfiguration, that is, on-demand replacement or provisioning of system resources. We propose a dynamic atomic multicast protocol which fulfills these requirements. It allows to dynamically add and remove resources to an online replicated state machine and to recover crashed processes. Major efforts have been spent in recent years to improve the performance, scalability and reliability of distributed systems. In order to hide the complexity of designing distributed applications, many proposals provide efficient high-level communication abstractions. Since the implementation of a production-ready system based on this abstraction is still a major task, we further propose to expose our protocol to developers in the form of distributed data structures. B- trees for example, are commonly used in different kinds of applications, including database indexes or file systems. Providing a distributed, fault-tolerant and scalable data structure would help developers to integrate their applications in a distribution transparent manner. This work describes how to build reliable and scalable distributed systems based on atomic multicast and demonstrates their capabilities by an implementation of a distributed ordered map that supports dynamic re-partitioning and fast recovery. To substantiate our claim, we ported an existing SQL database atop of our distributed lock-free data structure

Programming with process groups: Group and multicast semantics

Process groups are a natural tool for distributed programming and are increasingly important in distributed computing environments. Discussed here is a new architecture that arose from an effort to simplify Isis process group semantics. The findings include a refined notion of how the clients of a group should be treated, what the properties of a multicast primitive should be when systems contain large numbers of overlapping groups, and a new construct called the causality domain. A system based on this architecture is now being implemented in collaboration with the Chorus and Mach projects