The network is the database

Master'sMASTER OF SCIENC

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

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

Comparison of Database Replication Techniques Based on Total Order Broadcast

In this paper, we present a performance comparison of database replication techniques based on total order broadcast. While the performance of total order broadcast-based replication techniques has been studied in previous papers, this paper presents many new contributions. First, it compares with each other techniques that were presented and evaluated separately, usually by comparing them to a classical replication scheme like distributed locking. Second, the evaluation is done using a finer network model than previous studies. Third, the paper compares techniques that offer the same consistency criterion (one-copy serializability) in the same environment using the same settings. The paper shows that, while networking performance has little influence in a LAN setting, the cost of synchronizing replicas is quite high. Because of this, total order broadcast-based techniques are very promising as they minimize synchronization between replicas

Group communications and database replication:techniques, issues and performance

Databases are an important part of today's IT infrastructure: both companies and state institutions rely on database systems to store most of their important data. As we are more and more dependent on database systems, securing this key facility is now a priority. Because of this, research on fault-tolerant database systems is of increasing importance. One way to ensure the fault-tolerance of a system is by replicating it. Replication is a natural way to deal with failures: if one copy is not available, we use another one. However implementing consistent replication is not easy. Database replication is hardly a new area of research: the first papers on the subject are more than twenty years old. Yet how to build an efficient, consistent replicated database is still an open research question. Recently, a new approach to solve this problem has been proposed. The idea is to rely on some communication infrastructure called group communications. This infrastructure offers some high-level primitives that can help in the design and the implementation of a replicated database. While promising, this approach to database replication is still in its infancy. This thesis focuses on group communication-based database replication and strives to give an overall understanding of this topic. This thesis has three major contributions. In the structural domain, it introduces a classification of replication techniques. In the qualitative domain, an analysis of fault-tolerance semantics is proposed. Finally, in the quantitative domain, a performance evaluation of group communication-based database replication is presented. The classification gives an overview of the different means to implement database replication. Techniques described in the literature are sorted using this classification. The classification highlights structural similarities of techniques originating from different communities (database community and distributed system community). For each category of the classification, we also analyse the requirements imposed on the database component and group communication primitives that are needed to enforce consistency. Group communication-based database replication implies building a system from two different components: a database system and a group communication system. Fault-tolerance is an end-to-end property: a system built from two components tends to be as fault-tolerant as the weakest component. The analysis of fault-tolerance semantics show what fault-tolerance guarantee is ensured by group communication based replication techniques. Additionally a new faulttolerance guarantee, group-safety, is proposed. Group-safety is better suited to group communication-based database replication. We also show that group-safe replication techniques can offer improved performance. Finally, the performance evaluation offers a quantitative view of group communication based replication techniques. The performance of group communication techniques and classical database replication techniques is compared. The way those different techniques react to different loads is explored. Some optimisation of group communication techniques are also described and their performance benefits evaluated

Transactional concurrency control for resource constrained applications

PhD ThesisTransactions have long been used as a mechanism for ensuring the consistency of databases. Databases, and associated transactional approaches, have always been an active area of research as different application domains and computing architectures have placed ever more elaborate requirements on shared data access. As transactions typically provide consistency at the expense of timeliness (abort/retry) and resource (duplicate shared data and locking), there has been substantial efforts to limit these two aspects of transactions while still satisfying application requirements. In environments where clients are geographically distant from a database the consistency/performance trade-off becomes acute as any retrieval of data over a network is not only expensive, but relatively slow compared to co-located client/database systems. Furthermore, for battery powered clients the increased overhead of transactions can also be viewed as a significant power overhead. However, for all their drawbacks transactions do provide the data consistency that is a requirement for many application types. In this Thesis we explore the solution space related to timely transactional systems for remote clients and centralised databases with a focus on providing a solution, that, when compared to other's work in this domain: (a) maintains consistency; (b) lowers latency; (c) improves throughput. To achieve this we revisit a technique first developed to decrease disk access times via local caching of state (for aborted transactions) to tackle the problems prevalent in real-time databases. We demonstrate that such a technique (rerun) allows a significant change in the typical structure of a transaction (one never before considered, even in rerun systems). Such a change itself brings significant performance success not only in the traditional rerun local database solution space, but also in the distributed solution space. A byproduct of our improvements also, one can argue, brings about a "greener" solution as less time coupled with improved throughput affords improved battery life for mobile devices

An Optimizing Java Translation Framework for Automated Checkpointing and Strong Mobility

Long-running programs, e.g., in high-performance computing, need to write periodic checkpoints of their execution state to disk to allow them to recover from node failure. Manually adding checkpointing code to an application, however, is very tedious. The mechanisms needed for writing the execution state of a program to disk and restoring it are similar to those needed for migrating a running thread or a mobile object. We have extended a source-to-source translation scheme that allows the migration of mobile Java objects with running threads to make it more general and allow it to be used for automated checkpointing. Our translation scheme allows serializable threads to be written to disk or migrated with a mobile agent to a remote machine. The translator generates code that maintains a serializable run-time stack for each thread as a Java data structure. While this results in significant run-time overhead, it allows the checkpointing code to be generated automatically. We improved the locking mechanism that is needed to protect the run-time stack as well as the translation scheme. Our experimental results demonstrate an speedup of the generated code over the original translator and show that the approach is feasible in practice

Analysis of Transaction Management Performance

There is currently much interest in incorporating transactions into both operating systems and general purpose programming languages. This paper provides a detailed examination of the design and performance of the“¢ transaction manager of the Camelot system. Camelot is a transaction facility that provides a rich model of transactions intended to support a wide variety of general-purpose applications. The transaction manager's principal function is to execute the protocols that ensure atomicity. The conclusions of this study are: a simple optimization to two-phase commit reduces logging activity of distributed transactions; non-blocking commit is practical for some applications; multithreaded design improves throughput provided that log batching is used; multi-casting reduces the variance of distributed commit protocols in a LAN environment; and the performance of transaction mechanisms such as Camelot depend heavily upon kernel performance