Introduction to Reliable and Secure Distributed Programming

Cachin, Christian; Guerraoui, Rachid; Rodrigues, L.

doi:10.1007/978-3-642-15260-3

Cited by 304 publications

(337 citation statements)

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“…The results in this area include protocol specifications (see, e.g., [10], [5]), middleware based replication architectures (see, e.g., [11], [6]), as well as internal supports at the DBMS level (see, e.g., [12]). A key insight confirmed by several works in this area (see, e.g., [5]) is that AB-based replication protocols can avoid the well-known scalability limitations [10] of classic two-phase commit [13] based replication protocols and ensure robust performance even in scenarios of non-minimal contention.…”

Section: Introductionmentioning

confidence: 92%

SPECULA: Speculative Replication of Software Transactional Memory

Peluso

Fernandes

Romano

et al. 2012

2012 IEEE 31st Symposium on Reliable Distributed Systems

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Abstract-This paper introduces SPECULA, a novel replication protocol for Software Transactional Memory (STM) systems that seeks maximum overlap between transaction execution and replica synchronization phases via speculative processing techniques. By removing the replica synchronization phase from the critical path of execution of transactions, SPEC-ULA allows threads to speculatively pipeline the execution of both transactional and/or non-transactional code. The core of SPECULA is a multi-version concurrency control algorithm that supports speculative transaction processing while ensuring the strong consistency criteria that are desirable in non-sandboxed environments like STMs. Via an experimental study, based on a fully-fledged prototype and on both synthetic and standard STM benchmarks, we demonstrate that SPECULA can achieve speedups of up to one order of magnitude with respect to state-of-the-art non-speculative replication techniques.

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Section: Introductionmentioning

confidence: 92%

SPECULA: Speculative Replication of Software Transactional Memory

Peluso

Fernandes

Romano

et al. 2012

2012 IEEE 31st Symposium on Reliable Distributed Systems

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“…To the best of our knowledge, there are several textbooks (e.g., [18,36]) that analyse and compare some reliable causal multicast algorithms, but there is yet no published survey or tutorial about the mechanisms used in the numerous reliable causal multicast protocols existing today. This paper attempts to fill this void, by identifying approaches that are better suited for attaining scalability.…”

Section: Introductionmentioning

confidence: 99%

Scalability approaches for causal multicast: a survey

et al. 2015

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Many distributed services need to be scalable: internet search, electronic commerce, e-government... In order to achieve scalability those applications rely on replicated components. Because of the dynamics of growth and volatility of customer markets, applications need to be hosted by adaptive systems. In particular, the scalability of the reliable multicast mechanisms used for supporting the consistency of replicas is of crucial importance. Reliable multicast may propagate updates in a pre-defined order (e.g., FIFO, total or causal). Since total order needs more communication rounds than causal order, the latter appears to be the preferable candidate for achieving multicast scalability, although the consistency guarantees based on causal order are weaker than those of total order. This paper provides a historical survey of different scalability approaches for reliable causal multicast protocols.

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“…During this section, we have tried to collect those studies, adapting them to the most common failure type divisions in distributed systems [30,90,26]: crash, omission, arbitrary, network and security failures.…”

Section: Reliability In Data-intensive Processing Frameworkmentioning

confidence: 99%

“…It is based on the Leader Election Framework [30], by using Apache Zookeper [5]. This means that multiple JobTrackers (at least three) are started together, but only one of them is the leader at a particular time.…”

Section: Crash Failurementioning

confidence: 99%

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Optimizing the reliability and resource efficiency of MapReduce-based systems

Memishi¹

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Due to the increase of huge data volumes, a new parallel computing paradigm to process big data in an efficient way has arisen. Many of these systems, called dataintensive computing systems, follow the Google MapReduce programming model. The main advantage of these systems is based on the idea of sending the computation where the data resides, trying to provide scalability and efficiency.In failure-free scenarios, these frameworks usually achieve good results. However, these ones are not realistic scenarios. Consequently, these frameworks exhibit some fault tolerance and dependability techniques as built-in features. On the other hand, dependability improvements are known to imply additional resource costs. This is reasonable and providers offering these infrastructures are aware of this. Nevertheless, not all the approaches provide the same tradeoff between fault tolerant capabilities (or more generally, reliability capabilities) and cost.In this thesis, we have addressed the coexistence between reliability and resource efficiency in MapReduce-based systems, looking for methodologies that introduce the minimal cost and guarantee an appropriate level of reliability. In order to achieve this, we have proposed: (i) a formalization of a failure detector abstraction; (ii) an alternative solution to single points of failure of these frameworks, and finally (iii) a novel feedback-based resource allocation system at the container level.Finally, our generic contributions have been instantiated for the Hadoop YARN architecture, which is the state-of-the-art framework in the data-intensive computing systems community nowadays. The thesis demonstrates how all our approaches outperform Hadoop YARN in terms of reliability and resource efficiency. 3 ResumenDebido al gran incremento de datos digitales que ha tenido lugar en los últimos años, ha surgido un nuevo paradigma de computación paralela para el procesamiento eficiente de grandes volúmenes de datos. Muchos de los sistemas basados en este paradigma, también llamados sistemas de computación intensiva de datos, siguen el modelo de programación de Google MapReduce. La principal ventaja de los sistemas MapReduce es que se basan en la idea de enviar la computación donde residen los datos, tratando de proporcionar escalabilidad y eficiencia.En escenarios libres de fallo, estos sistemas generalmente logran buenos resultados. Sin embargo, la mayoría de escenarios donde se utilizan, se caracterizan por la existencia de fallos. Por tanto, estas plataformas suelen incorporar características de tolerancia a fallos y fiabilidad. Por otro lado, es reconocido que las mejoras en confiabilidad vienen asociadas a costes adicionales en recursos. Esto es razonable y los proveedores que ofrecen este tipo de infraestructuras son conscientes de ello. No obstante, no todos los enfoques proporcionan la misma solución de compromiso entre las capacidades de tolerancia a fallo (o de manera general, las capacidades de fiabilidad) y su coste.Esta tesis ha tratado la problemática de la coexist...

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Introduction to Reliable and Secure Distributed Programming

Cited by 304 publications

References 0 publications

SPECULA: Speculative Replication of Software Transactional Memory

SPECULA: Speculative Replication of Software Transactional Memory

Scalability approaches for causal multicast: a survey

Optimizing the reliability and resource efficiency of MapReduce-based systems

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