Cloud storage reliability for Big Data applications: A state of the art survey

Nachiappan, Rekha; Javadi, Bahman; Calheiros, Rodrigo N.; Matawie, Kenan M

doi:10.1016/j.jnca.2017.08.011

Cited by 97 publications

(47 citation statements)

References 52 publications

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“…Both techniques have their own trade-offs in various parameters such as durability, availability, storage overhead, network bandwidth and traffic, energy consumption and recovery performance. Future research should include the challenges involved in employing both techniques in Cloud storage systems for Big Data applications with respect to the aforementioned parameters [163]. This hybrid technique applies proactive dynamic data replication of erasure coded data based on node failure prediction, which significantly reduces network traffic and improves the performance of Big Data applications with less storage overhead.…”

Section: Reliabilitymentioning

confidence: 99%

A Manifesto for Future Generation Cloud Computing

et al. 2018

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The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to * Corresponding

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Section: Reliabilitymentioning

confidence: 99%

A Manifesto for Future Generation Cloud Computing

et al. 2018

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“…RDDs achieve fault tolerance through the notion of lineage. Each RDD tracks the graph of transformations that was used to build it and reruns these operations on base data to reconstruct any lost partitions [25]. The other key concept in Spark is its DAG execution engine, which is similar to Tez and is our basis for extending the Tez model to Spark.…”

Section: System Architecture and Application Structurementioning

confidence: 99%

Analytical composite performance models for Big Data applications

Karimian-Aliabadi

Ardagna

Entezari‐Maleki

et al. 2019

Journal of Network and Computer Applications

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In the era of Big Data, whose digital industry is facing the massive growth of data size and development of data intensive software, more and more companies are moving to use new frameworks and paradigms capable of handling data at scale. The outstanding MapReduce (MR) paradigm and its implementation framework, Hadoop are among the most referred ones, and basis for later and more advanced frameworks like Tez and Spark. Accurate prediction of the execution time of a Big Data application helps improving design time decisions, reduces over allocation charges, and assists budget management. In this regard, we propose analytical models based on the Stochastic Activity Networks (SANs) to accurately model the execution of MR, Tez and Spark applications in Hadoop environments governed by the YARN Capacity scheduler. We evaluate the accuracy of the proposed models over the TPC-DS industry benchmark across different configurations. Results obtained by numerically solving analytical SAN models show an average error of 6% in estimating the execution time of an application compared to the data gathered from experiments and moreover the model evaluation time is lower than simulation time of state of the art solutions.

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“…Reliability is defined in the context of resource failure, in the context of VM failure, in the context of service failure, or in the context of security. Nachiappan et al [25] have used reliability for cloud storage scheduling in big data. From the existing studies, very few reliability modelling is proposed for a cloud-based system.…”

Section: Related Workmentioning

confidence: 99%

Spectral Expansion Method for Cloud Reliability Analysis

Kotteswari¹,

Bharathi²

2019

Journal of Computer Networks and Communications

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Cloud computing is a computing hypothesis, where a huge group of systems is linked together in private, public, or hybrid network, to offer dynamically amendable infrastructure for data storage, file storage, and application. With this emerging technology, application hosting, delivery, content storage, and reduced computation cost are achieved, and it acts as an essential module for the backbone of the Internet of Things (IoT). The efficiency of cloud service providers (CSP) could be improved by considering significant factors such as availability, reliability, usability, security, responsiveness, and elasticity. Assessment of these factors leads to efficiency in designing a scheduler for CSP. These metrics also improved the quality of service (QoS) in the cloud. Many existing models and approaches evaluate these metrics. But these existing approaches do not offer efficient outcome. In this paper, a prominent performance model named the “spectral expansion method (SPM)” evaluates cloud reliability. The spectral expansion method (SPM) is a huge technique useful in reliability and performance modelling of the computing system. This approach solves the Markov model of cloud service providers (CSP) to predict the reliability. The SPM is better compared to matrix-geometric methods.

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Cloud storage reliability for Big Data applications: A state of the art survey

Cited by 97 publications

References 52 publications

A Manifesto for Future Generation Cloud Computing

A Manifesto for Future Generation Cloud Computing

Analytical composite performance models for Big Data applications

Spectral Expansion Method for Cloud Reliability Analysis

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