An improved technique for increasing availability in Big Data replication

Kaseb, Mostafa R.; Khafagy, Mohamed Helmy; Ali, Ikbal; Saad, E. M.

doi:10.1016/j.future.2018.08.015

Cited by 20 publications

(15 citation statements)

References 7 publications

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“…Information-oriented service model is proposed for fog architecture for being auto-adaptive and operate in both cloud and edge devices [31]. To cope up with exponential growth of IoT data in cloud which is referred to as big data [39], it has become a pre-requisite to have a fog layer in between the IoT devices and cloud [40] in a smart home [41,42], or smart manufacturing system [43], or smart connectivity through gateway [44] or smart grid [45]. Even this big data is ubiquitously available from cloud server through the fog layer.…”

Section: Related Work Based On Internet Of Things and Fogmentioning

confidence: 99%

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

Yadav

Mishra²,

Jayanthu

et al. 2021

IET Wireless Sensor Systems

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Fog computing (FC) archetypes is in focus recently for its potential to utilise resources optimally by the Internet of Things (IoT) network bearing ample end-devices. This enhances the quality of service (QoS) to the proximity of end-users as latency-free processing is obtained in cloud-IoT-based environs. Mining industry will encounter a level-up due to these technological advancements like fog-IoT-based systems. Monitoring mining activities such as blasting, slope monitoring, and miner tracking are time-sensitive. Hence, instant response is demand of extreme mine environment so that life and property are not compromised. New opportunities emerge in mining since the FC approach addresses these demands depending on the demand of the situation/user. The parameters of integrating the state-of-art of fog, IoT, and cloud architectures are provided, and a Fog-IoT mines monitoring (FIoTMM) system for real-time monitoring of open-cast mines is proposed. This work also envisions the applications and future trends of FC concerning IoT and cloud. It emphasises how key features of fog help the system in achieving lower network influx, reduced latency, resource utilisation, and immediate data computation and storage because these services take place closer to point of data generation. This data can be immediately visualised/accessed and used to generate early warning in the mines. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Work Based On Internet Of Things and Fogmentioning

confidence: 99%

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

Yadav

Mishra²,

Jayanthu

et al. 2021

IET Wireless Sensor Systems

View full text Add to dashboard Cite

show abstract

“…Nabi et al [28] stated that the availability technique, in general, is categorized as protective redundancy, fault tolerance, and overload protection, as visualized in Figure 4. In order to maintain and balance the performance when a server is straining with tasks and processing requests, the overload protection is implemented where some tasks and requests are delegated to another server that has fewer tasks.…”

Section: High Availability Of Application Servicesmentioning

confidence: 99%

“…In order to maintain and balance the performance when a server is straining with tasks and processing requests, the overload protection is implemented where some tasks and requests are delegated to another server that has fewer tasks. The cloud providers may embrace the high availability service, which may be achieved through replication and switchover [28] of the server as part of protective redundancy and fault tolerance where cloud providers usually position the data centers in the geographically distributed location around the world [29]. The replication of the server and the use of middleware as fault tolerance mechanisms have enabled the computer system to become better in terms of availability.…”

Section: High Availability Of Application Servicesmentioning

confidence: 99%

Segmented Network Architecture for Promoting High Availability in Fog Computing through Middleware

Naim¹,

Zain²,

Jalil³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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This paper proposes an architecture for deploying applications on a fog computing environment by adding another layer of fog nodes in a network segment that gains high software application availability. The conventional fog computing architecture would permanently shift the storage, applications, and data from cloud servers to fog nodes, thus reducing the dependency on the cloud. As a result, fog nodes are burdened with the task previously done by cloud servers and have become "mini cloud servers." Instead of permanently shifting the tasks from cloud servers to fog nodes, the proposed architecture would only do the shifting, when necessary, like if an internet outage. Additionally, this research also introduced the middleware application that acts as a detector and replacement if service outage so that the availability of the services is not interrupted, especially during the internet outage, by adding another layer of fog node in a network segment. The computational process occurs between end-users and the fog nodes without having to rely on cloud servers. An experiment was conducted to test the performance of the proposed architecture. From the experiment, it can be concluded that the deployment of fog nodes in a segmented network is possible and able to increase the availability of data and services if an internet outage.

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“…DFSs create numerous duplicates of the similar data block, which are stored as backups, and can store these copies on another computer in a network environment [Kaseb et al 2019]. DFSs are used for data storage in many fields, such as commercial enterprises, banks, military and defense, healthcare, and educational systems.…”

Section: Introductionmentioning

confidence: 99%

Design of a DFS to Manage Big Data in Distance Education Environments

Ünver¹,

Ergüzen²,

Erdal³

2022

jucs

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Information technologies have invaded every aspect of our lives. Distance education was also affected by this phase and became an accepted model of education. The evolution of education into a digital platform has also brought unexpected problems, such as the increase in internet usage, the need for new software and devices that can connect to the Internet. Perhaps the most important of these problems is the management of the large amounts of data generated when all training activities are conducted remotely. Over the past decade, studies have provided important information about the quality of training and the benefits of distance learning. However, Big Data in distance education has been studied only to a limited extent, and to date no clear single solution has been found. In this study, a Distributed File Systems (DFS) is proposed and implemented to manage big data in distance education. The implemented ecosystem mainly contains the elements Dynamic Link Library (DLL), Windows Service Routines and distributed data nodes. DLL codes are required to connect Learning Management System (LMS) with the developed system. 67.72% of the files in the distance education system have small file size (<=16 MB) and 53.10% of the files are smaller than 1 MB. Therefore, a dedicated Big Data management platform was needed to manage and archive small file sizes. The proposed system was designed with a dynamic block structure to address this shortcoming. A serverless architecture has been chosen and implemented to make the platform more robust. Moreover, the developed platform also has compression and encryption features. According to system statistics, each written file was read 8.47 times, and for video archive files, this value was 20.95. In this way, a framework was developed in the Write Once Read Many architecture. A comprehensive performance analysis study was conducted using the operating system, NoSQL, RDBMS and Hadoop. Thus, for file sizes 1 MB and 50 MB, the developed system achieves a response time of 0.95 ms and 22.35 ms, respectively, while Hadoop, a popular DFS, has 4.01 ms and 47.88 ms, respectively.

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An improved technique for increasing availability in Big Data replication

Cited by 20 publications

References 7 publications

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey

Segmented Network Architecture for Promoting High Availability in Fog Computing through Middleware

Design of a DFS to Manage Big Data in Distance Education Environments

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