Using Optimistic Atomic Broadcast in Transaction Processing Systems

Atomic broadcast primitives are often proposed as a mechanism to allow fault-tolerant cooperation between sites in a distributed system. Unfortunately, the delay incurred before a message can be delivered makes it difficult to implement high performance, scalable applications on top of atomic broadcast primitives. Recently, a new approach has been proposed for atomic broadcast which, based on optimistic assumptions about the communication system, reduces the average delay for message delivery to the application. In this paper, we develop this idea further and show how applications can take even more advantage of the optimistic assumption by overlapping the coordination phase of the atomic broadcast algorithm with the processing of delivered messages. In particular, we present a replicated database architecture that employs the new atomic broadcast primitive in such a way that communication and transaction processing are fully overlapped, providing high performance without relaxing transaction correctness

Unifying Thread-Level Speculation and Transactional Memory

Abstract. The motivation of this work is to ask whether Transactional Memory (TM) and Thread-Level Speculation (TLS), two prominent con-currency paradigms usually considered separately, can be combined into a hybrid approach that extracts untapped parallelism and speed-up from common programs. We show that the answer is positive by describing an algorithm, called TLSTM, that leverages an existing TM with TLS capabilities. We also show that our approach is able to achieve up to a 48 % increase in throughput over the base TM, on read dominated workloads of long transactions in a multi-threaded application, among other results.

SUPPORTING MULTIPLE ISOLATION LEVELS IN REPLICATED ENVIRONMENTS

La replicación de bases de datos aporta fiabilidad y escalabilidad aunque hacerlo de forma transparente no es una tarea sencilla. Una base de datos replicada es transparente si puede reemplazar a una base de datos centralizada tradicional sin que sea necesario adaptar el resto de componentes del sistema. La transparencia en bases de datos replicadas puede obtenerse siempre que (a) la gestión de la replicación quede totalmente oculta a dichos componentes y (b) se ofrezca la misma funcionalidad que en una base de datos tradicional. Para mejorar el rendimiento general del sistema, los gestores de bases de datos centralizadas actuales permiten ejecutar de forma concurrente transacciones bajo distintos niveles de aislamiento. Por ejemplo, la especificación del benchmark TPC-C permite la ejecución de algunas transacciones con niveles de aislamiento débiles. No obstante, este soporte todavía no está disponible en los protocolos de replicación. En esta tesis mostramos cómo estos protocolos pueden ser extendidos para permitir la ejecución de transacciones con distintos niveles de aislamiento.Bernabe Gisbert, JM. (2014). SUPPORTING MULTIPLE ISOLATION LEVELS IN REPLICATED ENVIRONMENTS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/36535TESI

Keeping checkpoint/restart viable for exascale systems

Next-generation exascale systems, those capable of performing a quintillion operations per second, are expected to be delivered in the next 8-10 years. These systems, which will be 1,000 times faster than current systems, will be of unprecedented scale. As these systems continue to grow in size, faults will become increasingly common, even over the course of small calculations. Therefore, issues such as fault tolerance and reliability will limit application scalability. Current techniques to ensure progress across faults like checkpoint/restart, the dominant fault tolerance mechanism for the last 25 years, are increasingly problematic at the scales of future systems due to their excessive overheads. In this work, we evaluate a number of techniques to decrease the overhead of checkpoint/restart and keep this method viable for future exascale systems. More specifically, this work evaluates state-machine replication to dramatically increase the checkpoint interval (the time between successive checkpoints) and hash-based, probabilistic incremental checkpointing using graphics processing units to decrease the checkpoint commit time (the time to save one checkpoint). Using a combination of empirical analysis, modeling, and simulation, we study the costs and benefits of these approaches on a wide range of parameters. These results, which cover of number of high-performance computing capability workloads, different failure distributions, hardware mean time to failures, and I/O bandwidths, show the potential benefits of these techniques for meeting the reliability demands of future exascale platforms

Rigorous Design of Distributed Transactions

Database replication is traditionally envisaged as a way of increasing fault-tolerance and availability. It is advantageous to replicate the data when transaction workload is predominantly read-only. However, updating replicated data within a transactional framework is a complex affair due to failures and race conditions among conflicting transactions. This thesis investigates various mechanisms for the management of replicas in a large distributed system, formalizing and reasoning about the behavior of such systems using Event-B. We begin by studying current approaches for the management of replicated data and explore the use of broadcast primitives for processing transactions. Subsequently, we outline how a refinement based approach can be used for the development of a reliable replicated database system that ensures atomic commitment of distributed transactions using ordered broadcasts. Event-B is a formal technique that consists of describing rigorously the problem in an abstract model, introducing solutions or design details in refinement steps to obtain more concrete specifications, and verifying that the proposed solutions are correct. This technique requires the discharge of proof obligations for consistency checking and refinement checking. The B tools provide significant automated proof support for generation of the proof obligations and discharging them. The majority of the proof obligations are proved by the automatic prover of the tools. However, some complex proof obligations require interaction with the interactive prover. These proof obligations also help discover new system invariants. The proof obligations and the invariants help us to understand the complexity of the problem and the correctness of the solutions. They also provide a clear insight into the system and enhance our understanding of why a design decision should work. The objective of the research is to demonstrate a technique for the incremental construction of formal models of distributed systems and reasoning about them, to develop the technique for the discovery of gluing invariants due to prover failure to automatically discharge a proof obligation and to develop guidelines for verification of distributed algorithms using the technique of abstraction and refinement

Speculation in Parallel and Distributed Event Processing Systems

Event stream processing (ESP) applications enable the real-time processing of continuous flows of data. Algorithmic trading, network monitoring, and processing data from sensor networks are good examples of applications that traditionally rely upon ESP systems. In addition, technological advances are resulting in an increasing number of devices that are network enabled, producing information that can be automatically collected and processed. This increasing availability of on-line data motivates the development of new and more sophisticated applications that require low-latency processing of large volumes of data. ESP applications are composed of an acyclic graph of operators that is traversed by the data. Inside each operator, the events can be transformed, aggregated, enriched, or filtered out. Some of these operations depend only on the current input events, such operations are called stateless. Other operations, however, depend not only on the current event, but also on a state built during the processing of previous events. Such operations are, therefore, named stateful. As the number of ESP applications grows, there are increasingly strong requirements, which are often difficult to satisfy. In this dissertation, we address two challenges created by the use of stateful operations in a ESP application: (i) stateful operators can be bottlenecks because they are sensitive to the order of events and cannot be trivially parallelized by replication; and (ii), if failures are to be tolerated, the accumulated state of an stateful operator needs to be saved, saving this state traditionally imposes considerable performance costs. Our approach is to evaluate the use of speculation to address these two issues. For handling ordering and parallelization issues in a stateful operator, we propose a speculative approach that both reduces latency when the operator must wait for the correct ordering of the events and improves throughput when the operation in hand is parallelizable. In addition, our approach does not require that user understand concurrent programming or that he or she needs to consider out-of-order execution when writing the operations. For fault-tolerant applications, traditional approaches have imposed prohibitive performance costs due to pessimistic schemes. We extend such approaches, using speculation to mask the cost of fault tolerance.:1 Introduction 1 1.1 Event stream processing systems ......................... 1 1.2 Running example ................................. 3 1.3 Challenges and contributions ........................... 4 1.4 Outline ...................................... 6 2 Background 7 2.1 Event stream processing ............................. 7 2.1.1 State in operators: Windows and synopses ............................ 8 2.1.2 Types of operators ............................ 12 2.1.3 Our prototype system........................... 13 2.2 Software transactional memory.......................... 18 2.2.1 Overview ................................. 18 2.2.2 Memory operations............................ 19 2.3 Fault tolerance in distributed systems ...................................... 23 2.3.1 Failure model and failure detection ...................................... 23 2.3.2 Recovery semantics............................ 24 2.3.3 Active and passive replication ...................... 24 2.4 Summary ..................................... 26 3 Extending event stream processing systems with speculation 27 3.1 Motivation..................................... 27 3.2 Goals ....................................... 28 3.3 Local versus distributed speculation ....................... 29 3.4 Models and assumptions ............................. 29 3.4.1 Operators................................. 30 3.4.2 Events................................... 30 3.4.3 Failures .................................. 31 4 Local speculation 33 4.1 Overview ..................................... 33 4.2 Requirements ................................... 35 4.2.1 Order ................................... 35 4.2.2 Aborts................................... 37 4.2.3 Optimism control ............................. 38 4.2.4 Notifications ............................... 39 4.3 Applications.................................... 40 4.3.1 Out-of-order processing ......................... 40 4.3.2 Optimistic parallelization......................... 42 4.4 Extensions..................................... 44 4.4.1 Avoiding unnecessary aborts ....................... 44 4.4.2 Making aborts unnecessary........................ 45 4.5 Evaluation..................................... 47 4.5.1 Overhead of speculation ......................... 47 4.5.2 Cost of misspeculation .......................... 50 4.5.3 Out-of-order and parallel processing micro benchmarks ........... 53 4.5.4 Behavior with example operators .................... 57 4.6 Summary ..................................... 60 5 Distributed speculation 63 5.1 Overview ..................................... 63 5.2 Requirements ................................... 64 5.2.1 Speculative events ............................ 64 5.2.2 Speculative accesses ........................... 69 5.2.3 Reliable ordered broadcast with optimistic delivery .................. 72 5.3 Applications .................................... 75 5.3.1 Passive replication and rollback recovery ................................ 75 5.3.2 Active replication ............................. 80 5.4 Extensions ..................................... 82 5.4.1 Active replication and software bugs ..................................... 82 5.4.2 Enabling operators to output multiple events ........................ 87 5.5 Evaluation .................................... 87 5.5.1 Passive replication ............................ 88 5.5.2 Active replication ............................. 88 5.6 Summary ..................................... 93 6 Related work 95 6.1 Event stream processing engines ......................... 95 6.2 Parallelization and optimistic computing ................................ 97 6.2.1 Speculation ................................ 97 6.2.2 Optimistic parallelization ......................... 98 6.2.3 Parallelization in event processing .................................... 99 6.2.4 Speculation in event processing ..................... 99 6.3 Fault tolerance .................................. 100 6.3.1 Passive replication and rollback recovery ............................... 100 6.3.2 Active replication ............................ 101 6.3.3 Fault tolerance in event stream processing systems ............. 103 7 Conclusions 105 7.1 Summary of contributions ............................ 105 7.2 Challenges and future work ............................ 106 Appendices Publications 107 Pseudocode for the consensus protocol 10