Minimizing data redundancy for high reliable cloud storage systems

Huang, Zhen; Chen, Jinbang; Lin, Yisong; You, Pengfei; Peng, Yuxing

doi:10.1016/j.comnet.2015.02.013

Cited by 17 publications

(12 citation statements)

References 17 publications

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“…The IIoT is mainly used in the design, production, management, and service of all aspects of the life cycle, which contains tremendous economic value [1]. The IIoT realizes flexible allocation of manufacturing raw materials, on-demand execution of manufacturing processes, rational optimization of manufacturing processes, and rapid adaptation of manufacturing environments through network interconnection, data exchange, and system interoperability of industrial resources, and achieves efficient utilization of resources, thus building a new service-driven industrial ecological system [2,3]. The IoT involves the information of physical objects (sensors, machines, cars, buildings, and other items) that * Correspondence: zdhlds@163.com This work is licensed under a Creative Commons Attribution 4.0 International License.…”

Section: Introductionmentioning

confidence: 99%

Sensor anomaly detection in the industrial internet of things based on edge computing

Kong¹,

Liu²,

Zhang³

et al. 2020

Turk J Elec Eng & Comp Sci

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In the industrial internet of things (IIoT), because thousands of pieces of hardware, instruments, and various controllers are involved, the core problem is the sensors. Detection using sensors is the bottom line of the IIoT, directly affecting the detection accuracy and control indicators of the IIoT system. However, when a large number of realtime data generated by IIoT devices are transferred to cloud computing centers, large-scale data will inevitably bring computing load, which will affect the computing speed of cloud computing centers and increase the computing load of cloud computing data centers. These factors directly lead to instability and delay in sensor data collected in real time in the IIoT. In this paper, a sensor outlier detection algorithm based on edge calculation is proposed. Firstly, focusing on the problem of the large amount of data in terminal equipment of the IIoT, the edge technology method of data compression is used to optimize the compression of sensor data, and different thresholds are set according to different industrial process requirements, so as to ensure the real-time aspect and authenticity of the data. Then, using the K-means clustering algorithm, the compressed test data sets are analyzed and the abnormal sensor detection values and labels are obtained. Finally, the effectiveness of such an approach is evaluated through a sample case study involving a temperature control system.

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Section: Introductionmentioning

confidence: 99%

Sensor anomaly detection in the industrial internet of things based on edge computing

Kong¹,

Liu²,

Zhang³

et al. 2020

Turk J Elec Eng & Comp Sci

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“…For the case where computer j has not failed before T and contains some uncorrupted data parts, the probability of this case for computer j is formulated as equation (14) PNF computer j…”

Section: The Generalized Model Considering Computer Failure Data Thementioning

confidence: 99%

“…12 It was assumed that data might be destroyed or stolen, restricting to the case where the destruction probability and the theft probability are constant. 12,13 The minimization of data redundancy in cloud storage systems, 14 the reliability of a distributed computing system considering virus epidemics, 15 and the configuration for the distributed storage systems for the internet of things 16 were, respectively, analyzed. Li et al 17 concerned the information security in a distributed storage system, and proposed an intelligence cryptography scheme.…”

Section: Related Workmentioning

confidence: 99%

Reliability analysis of distributed storage systems considering data loss and theft

Jia

Peng

Ding

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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With the advancement of cloud computing and internet of things, data are usually stored on distributed computers and these data may risk being lost or stolen. In this article, we consider a common case where the entirety of the data is partitioned into several parts and each data part can be allocated to one or more computers. In the case where a computer fails, all the data parts on it are lost. Before the failure of any computer, the data parts may also be stolen by hackers. The basic model of computer failure and computer intrusion resulting in the theft of all the data parts on the computer is considered first. Then, the case is extended to a general model where computer failure, as well as data part corruption and theft caused by hacking are embedded. It is essential to study the reliability of distributed storage systems considering both data loss and data theft, which can be a basis for decision making on system structure optimization. In this article, a multi-valued decision diagram–based approach is developed to quantitatively evaluate system reliability for both models considering the time-dependence property of sequential events. The proposed method is applicable to systems where the random time to failure, theft, or corruption follows arbitrary distributions including the commonly used exponential distributions. Illustrative examples are provided to validate the proposed method.

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“…In order to avoid failures, copies of data objects are distributed across set of storage nodes using redundancy schemes. In [9], a novel model for the storage allocation scheme in cloud storage systems is proposed aiming to minimize the data redundancy while achieving a given (high) data reliability. To optimize the redundancy, two schemes by finding minimum number of blocks in total with fixed number of sufficient blocks for recovery and finding maximum number of sufficient blocks for recovery with fixed number of blocks in total, which allows the schemes to be deployed for different systems.…”

Section: Minimizing Data Redundancy For High Reliable Cloud Storage Smentioning

confidence: 99%

“…Huang [9] A novel model for the storage allocation scheme in cloud storage systems to minimizes the data redundancy while achieving a given (high) data reliability.…”

Section: Reviewed Papers Descriptions and Key Issue Handled Benefitsmentioning

confidence: 99%

A Survey on Storage Techniques in Cloud Computing

Akintoye¹,

Bagula²,

Djemaiel³

et al. 2017

IJCA

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In recent years, cloud computing is highly embraced and considered a must have in the IT world. Cloud computing is a computing paradigm, where a large pool of systems are connected in private, public or hybrid networks, to provide dynamically scalable infrastructure for computing resources. One of the services provided by cloud computing is cloud storage that enables the upload and the retrieval of different kinds of data. Accessing cloud storage service through Internet and pay as you used subscription have been the reasons for the emergence of methods and techniques to effectively store data and reduce storage security vulnerabilities in the cloud storage. This paper provides a survey of some proposed cloud storage methods and techniques, their advantages and drawbacks and makes stress on the current requirements for storage techniques in cloud computing.

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Minimizing data redundancy for high reliable cloud storage systems

Cited by 17 publications

References 17 publications

Sensor anomaly detection in the industrial internet of things based on edge computing

Sensor anomaly detection in the industrial internet of things based on edge computing

Reliability analysis of distributed storage systems considering data loss and theft

A Survey on Storage Techniques in Cloud Computing

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