Paxos made live

Chandra, Tushar; Griesemer, Robert; Redstone, Joshua

doi:10.1145/1281100.1281103

Cited by 390 publications

(33 citation statements)

References 10 publications

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“…Such disregard of practical aspects often stem from the use of simplifying models and simulations. This gap was observed previously even in fundamental primitives such as consensus [4,20]. We consider the same happens currently in another useful distributed systems primitive: slicing.…”

Section: Introductionsupporting

confidence: 66%

Slicing as a Distributed Systems Primitive

Maia

Matos

Oliveira

et al. 2013

2013 Sixth Latin-American Symposium on Dependable Computing

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Large-scale distributed systems appear as the major infrastructures for supporting planet-scale services. These systems call for appropriate management mechanisms and protocols.Slicing is an example of an autonomous, fully decentralized protocol suitable for large-scale environments. It aims at organizing the system into groups of nodes, called slices, according to an application-specific criteria where the size of each slice is relative to the size of the full system. This allows assigning a certain fraction of nodes to different task, according to their capabilities.Although useful, current slicing techniques lack some features of considerable practical importance. This paper proposes a slicing protocol, that builds on existing solutions, and addresses some of their frailties. We present novel solutions to deal with non-uniform slices and to perform online and dynamic slices schema reconfiguration. Moreover, we describe how to provision a slice-local Peer Sampling Service for upper protocol layers and how to enhance slicing protocols with the capability of slicing over more than one attribute.Slicing is presented as a complete, dependable and integrated distributed systems primitive for large-scale systems.

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Section: Introductionsupporting

confidence: 66%

Slicing as a Distributed Systems Primitive

Maia

Matos

Oliveira

et al. 2013

2013 Sixth Latin-American Symposium on Dependable Computing

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“…Later, in-memory data are updated across all replicating nodes so that improved write throughput can be achieved. Similarly, BigTable implements transactional consistency on rows using Chubby service (Burrows, 2006) and the Paxos algorithm (Chandra et al, 2007) for persistent replicas and consistency lock management. Client acquires the lock and maintains the session during which it performs atomic reads and writes.…”

Section: Analysis Of Big Data Storage Technologies Based On Brewer's mentioning

confidence: 99%

Big data storage technologies: a survey

Siddiqa

Karim

Gani

2017

Frontiers Inf Technol Electronic Eng

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Abstract:There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed 'big data'. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mechanism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the existing approaches using Brewer's CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.

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“…Besides the very software design process and the resulting blueprints, we consider that the major contributions of our project are the following operational units: i) a parameterized IDA module that can be easily adapted to different conditions, ii) our own oracle design and its corresponding module implementation, iii) an implementation of the Paxos protocol [24,25], enforcing metadata consistency,…”

Section: The Keys To Babelmentioning

confidence: 99%

“…The team that built GFS developed its proprietary implementation of Paxos [24] called Chubby. They mention that there is a long road from the description that appears on the initial paper, to the final programming of Chubby.…”

Section: Paxos and Medata Consistencymentioning

confidence: 99%

The Babel File System

Naquid

Jiménez

Compeán

2014

2014 IEEE International Congress on Big Data

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The Babel File System is a dependable, scalable and flexible storage system. Among its main features we underline the availability of different types of data redundancy, a careful decoupling between data and metadata, a middleware that enforces metadata consistency, and its own load-balance and allocation procedure which adapts to the number and capacities of the supporting storage devices. It can be deployed over different hardware platforms, including commodity hardware. Our proposal has been designed to allow developers to settle a trade-off between price and performance, depending on their particular applications.

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Paxos made live

Cited by 390 publications

References 10 publications

Slicing as a Distributed Systems Primitive

Slicing as a Distributed Systems Primitive

Big data storage technologies: a survey

The Babel File System

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