A comprehensive conceptual system-level approach to fault tolerance in Cloud Computing

Jhawar, Ravi; Piuri, Vincenzo; Santambrogio, Marco D.

doi:10.1109/syscon.2012.6189503

Cited by 75 publications

(33 citation statements)

References 10 publications

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“…The demonstrated solutions to these issues are the use of their newly developed architecture along with request redirection. A similar, though only conceptual approach, is developed in [31,32] where a Fault Tolerance Manager (FTM) is developed and inserted between System and Application layers of the CCS. Another approach to this issue is an optimal checkpointing strategy that is used to ensure the availability of a given system [33,34].…”

Section: Cloud Computing Reliabilitymentioning

confidence: 99%

Evaluation and design of highly reliable and highly utilized cloud computing systems

et al. 2015

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Cloud computing paradigm has ushered in the need to provide resources to users in a scalable, flexible, and transparent fashion much like any other utility. This has led to a need for developing evaluation techniques that can provide quantitative measures of reliability of a cloud computing system (CCS) for efficient planning and expansion. This paper presents a new, scalable algorithm based on non-sequential Monte Carlo Simulation (MCS) to evaluate large scale cloud computing system (CCS) reliability, and it develops appropriate performance measures. Also, a new iterative algorithm is proposed and developed that leverages the MCS method for the design of highly reliable and highly utilized CCSs. The combination of these two algorithms allows CCSs to be evaluated by providers and users alike, providing a new method for estimating the parameters of service level agreements (SLAs) and designing CCSs to match those contractual requirements posed in SLAs. Results demonstrate that the proposed methods are effective and applicable to systems at a large scale. Multiple insights are also provided into the nature of CCS reliability and CCS design.

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Section: Cloud Computing Reliabilitymentioning

confidence: 99%

Evaluation and design of highly reliable and highly utilized cloud computing systems

et al. 2015

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“…To achieve the reliability and resilience they propose an innovative perspective on creating and managing fault-tolerance .By this particular methodology user can specify and apply the desire level of fault-tolerance without requiring any knowledge about its implementation. FTM architecture this can primarily be viewed as an assemblage of several web services components, each with a specific functionality [21].…”

Section: Fault-tolerance Models In Cloud Computingmentioning

confidence: 99%

Fault-Tolerance Techniques in Cloud Storage: A Survey

Hosseini¹,

Ghobaei‐Arani²

2015

IJDTA

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“…Big data has also opened doors of significant interest in various fields, including remote healthcare, telebanking, social networking services (SNS), and satellite imaging [1]. Failures in many of these systems may represent significant economic or market share loss and negatively affect an organization's reputation [2]. Hence, it is always intended that whenever a fault occurs, the damage done should be within an acceptable threshold rather than beginning the whole task from scratch, due to which fault tolerance becomes an integral part in cloud computing and big data [3].…”

Section: Introductionmentioning

confidence: 99%

SEDC-Based Hardware-Level Fault Tolerance and Fault Secure Checker Design for Big Data and Cloud Computing

Siddiqui

Lee

Park

2018

Scientific Programming

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Fault tolerance is of great importance for big data systems. Although several software-based application-level techniques exist for fault security in big data systems, there is a potential research space at the hardware level. Big data needs to be processed inexpensively and efficiently, for which traditional hardware architectures are, although adequate, not optimum for this purpose. In this paper, we propose a hardware-level fault tolerance scheme for big data and cloud computing that can be used with the existing software-level fault tolerance for improving the overall performance of the systems. The proposed scheme uses the concurrent error detection (CED) method to detect hardware-level faults, with the help of Scalable Error Detecting Codes (SEDC) and its checker. SEDC is an all unidirectional error detection (AUED) technique capable of detecting multiple unidirectional errors. The SEDC scheme exploits data segmentation and parallel encoding features for assigning code words. Consequently, the SEDC scheme can be scaled to any binary data length "n" with constant latency and less complexity, compared to other AUED schemes, hence making it a perfect candidate for use in big data processing hardware. We also present a novel area, delay, and power efficient, scalable fault secure checker design based on SEDC. In order to show the effectiveness of our scheme, we (1) compared the cost of hardwarebased fault tolerance with an existing software-based fault tolerance technique used in HDFS and (2) compared the performance of the proposed checker in terms of area, speed, and power dissipation with the famous Berger code and m-out-of-2m code checkers. The experimental results show that (1) the proposed SEDC-based hardware-level fault tolerance scheme significantly reduces the average cost associated with software-based fault tolerance in a big data application, and (2) the proposed fault secure checker outperforms the state-of-the-art checkers in terms of area, delay, and power dissipation.

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A comprehensive conceptual system-level approach to fault tolerance in Cloud Computing

Cited by 75 publications

References 10 publications

Evaluation and design of highly reliable and highly utilized cloud computing systems

Evaluation and design of highly reliable and highly utilized cloud computing systems

Fault-Tolerance Techniques in Cloud Storage: A Survey

SEDC-Based Hardware-Level Fault Tolerance and Fault Secure Checker Design for Big Data and Cloud Computing

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