Big Data and Fog Computing

Hussain, Fatima; Al-Karkhi, Ameera

doi:10.1007/978-3-319-55405-1_3

Cited by 9 publications

(12 citation statements)

References 6 publications

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“…Indeed, the data veracity (including data quality and security) difficulties can be controlled by applying different types of policies through F2C layers. On top of that, there are several ongoing studies and active researchers in these days to prove all our above-mentioned idea [6,7,[27][28][29][30][31], to name a few.…”

Section: B the Zen Data Management Architecturementioning

confidence: 97%

A Zero Emission Neighbourhoods Data Management Architecture for Smart City Scenarios: Discussions toward 6Vs challenges

Sinaeepourfard

Krogstie

Petersen

et al. 2018

2018 International Conference on Information and Communication Technology Convergence (ICTC)

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A huge volume of data are being generated from multiple sources, including smart cities, the IoT devices, scientific modeling, or different big data simulations; but also from users' daily activities. These daily new data are added to historical repositories, providing the huge and complex universe of the digital data. Recently, the Fog-to-Cloud (F2C) data management architecture is envisioned to handle all big data complexities, from IoT devices (the closest layer to the users) to cloud technologies (the farthest layer to the IoT devices), as well as different data phases from creation to usage from fog to cloud scenario. Moreover, the F2C data management architecture can have several benefits from the combined advantages of fog (distributed) and cloud (centralized) technologies including reducing network traffic, reducing latencies drastically while improving security. In this paper, we have several novel contributions. First, we described the previous studies of the Zero Emission Buildings (ZEB) in the context of the data flow and movement architecture. Second, we have proposed Zero Emission Neighbourhoods (ZEN) data management architecture for smart city scenarios based on a distributed hierarchical F2C data management. Indeed, we used the 6Vs big data challenges (Volume, Variety, Velocity, Variability, Veracity, and Value) for evaluating the data management architectures (including ZEB and ZEN). The result of the evaluation shows that our proposed ZEN data management architecture can address 6Vs challenges and is able to manage the data lifecycle from its production up to its usage.

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Section: B the Zen Data Management Architecturementioning

confidence: 97%

A Zero Emission Neighbourhoods Data Management Architecture for Smart City Scenarios: Discussions toward 6Vs challenges

Sinaeepourfard

Krogstie

Petersen

et al. 2018

2018 International Conference on Information and Communication Technology Convergence (ICTC)

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“…With the focus on data management in a centralized place, there are several studies [4,15,21] argue that all data acquisition stages (including data aggregation, data filtering, data classification and so on) and data preservation stages (including data storage, data sharing and so on) can be handled through centralized technologies (such as cloud). In a particular example in a smart city scenario (as shown in Fig.…”

Section: 1mentioning

confidence: 99%

“…This section revisited the two previous studies of the D2C-DM architecture (based on fog-to-cloud data management) in two different smart city scenarios and described below. First use case adapted to the Barcelona smart city [8][9][10][11] under Sentilo platform [21]. Then, we explore further through the previous studies and their practical results to extract new insight for the Data Acquisition and Data Preservation blocks through D2C-DM architecture.…”

Section: Scenario Explanation: Optimization Of Data Acquisition and Data Preservation Blocks Through D2c-dm Architecturementioning

confidence: 99%

“…Then, several physical sensors positioned across the Barcelona city in order to develop the quality of services to the city residents [23]. Nowadays Barcelona has further research interests to manage all obtained sensors data through different platforms (for instance, Sentilo [21]) in Barcelona smart cities [8][9][10]24]. With respect to the idea of data management in Barcelona's smart city, in [8][9][10] were extended the traditional sensors data management architecture of Barcelona smart city through Sentilo platform [21,24].…”

Section: Case Study In Barcelona Spain Through Sentilo Platformmentioning

confidence: 99%

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D2C-DM: Distributed-to-Centralized Data Management for Smart Cities Based on Two Ongoing Case Studies

Sinaeepourfard

Krogstie

Petersen

2019

Advances in Intelligent Systems and Computing

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Smart city environments follow different technological management strategies (such as resource management, data management, and so on) between end-users to city planners and technological devices (e.g., sensor, camera surveillance, etc.) to enhance citizens' quality of life through the variety of the smart services. Data management is one of the most critical issues in smart cities because data is a core resource in the smart city. Without proper data, no smart services in the smart cities exist to make a connection between end-users and technological devices. A few numbers of distributed-to-centralize data management architectures have been proposed. In addition, there are several different distributed schema by several technological options exist (e.g., cloudlet, fog, etc.) but almost all of the studies used a distributed-to-centralized data management architecture based on fog to cloud technologies. Therefore, the fogto-cloud data management architecture can use both potentials of fog and cloud technologies, including the decrease in communication latencies, organizing distinct policies (e.g., data filtering, data compression, etc.) and so on. In this paper, first, previous studies of distributed-to-centralized data management architectures through two different smart city scenarios has been revisited. Afterward, the easy use and adaptation of the distributed-to-centralized data management architecture to any smart city scenario has been shown. In addition, the advantages of this data management architecture have been highlighted including efficiency rates for the data collection and data storage, and reducing data and network traffic. Finally, a number of the lesson learned from previous case studies have been addressed.

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“…IoE systems like smart healthcare monitoring, smart traffic, smart farming, etc, follow the architecture of Cloud‐centric Internet of Things (CIoT) for storage, analytics, and processing . CIoT data centers usually reside at multi‐hop distance from a source node, thus resulting in long delays.…”

Section: Introductionmentioning

confidence: 99%

A job scheduling algorithm for delay and performance optimization in fog computing

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

107

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Summary Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

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Big Data and Fog Computing

Cited by 9 publications

References 6 publications

A Zero Emission Neighbourhoods Data Management Architecture for Smart City Scenarios: Discussions toward 6Vs challenges

A Zero Emission Neighbourhoods Data Management Architecture for Smart City Scenarios: Discussions toward 6Vs challenges

D2C-DM: Distributed-to-Centralized Data Management for Smart Cities Based on Two Ongoing Case Studies

A job scheduling algorithm for delay and performance optimization in fog computing

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