SoFA: A Spark-oriented Fog Architecture

Maleki, Neda; Loni, Mohammad; Daneshtalab, Masoud; Conti, Mauro; Fotouhi, Hossein

doi:10.1109/iecon.2019.8927065

Cited by 14 publications

(10 citation statements)

References 19 publications

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“…The model is formulated as a Mixed Integer Linear Problem (MILP) with an objective function minimizing the total computational cost (load) of requests using performance and QoS parameters. In [38], authors propose an architecture entitled SoFA, which is a Sparkoriented Fog architecture that leverages Spark functionalities to provide higher system utilization. This method leverages the remaining processing capacity of edge devices.…”

Section: A Iot Applications Platforms In Cloud/fog Environmentsmentioning

confidence: 99%

READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

Yahyaoui

Abdellatif²,

Yangui³

et al. 2021

IEEE Access

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Internet of Things (IoT) enables a myriad of applications by interconnecting software to physical objects. The objects range from wireless sensors to robots and include surveillance cameras. The applications are often critical (e.g. physical intrusion detection, fire fighting) and latency-sensitive. On the one hand, such applications rely on specific protocols (e.g. MQTT, COAP) and the network to communicate with the objects under very tight timeframe. On the other hand, anomalies (e.g. communication noise, sensors' failures, security attacks) are likely to occur in open IoT systems and can result by sending false alerts or the failure to properly detect critical events. To address that, IoT systems have to be equipped with anomaly detection processing in addition to the required event detection capability. This is a key feature that enables reliability and efficiency in IoT. However, anomaly detection systems can be themselves object of failures and attacks, and then can easily fall short to accomplish their mission. This paper introduces a Reliable Event and Anomaly Detection Framework for the Internet of Things (READ-IoT for short). The designed framework integrates events and anomalies detection into a single and common system that centralizes the management of both concepts. To enforce its reliability, the system relies on a reputationaware provisioning of detection capabilities that takes into account the vulnerability of the deployment hosts. As for validation, READ-IoT was implemented and evaluated using two real life applications, i.e. a fire detection and an unauthorized person detection applications. Several scenarios of anomalies and events were conducted using NSL-KDD public dataset, as well as, generated data to simulate routing attacks. The obtained results and performance measurements show the efficiency of READ-IoT in terms of event detection accuracy and real-time processing.

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Section: A Iot Applications Platforms In Cloud/fog Environmentsmentioning

confidence: 99%

READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

Yahyaoui

Abdellatif²,

Yangui³

et al. 2021

IEEE Access

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“…Regarding the implementation of a Fog node, it is worth noting that the architecture chosen can play a relevant role as well. Maleki et al [41] showed that Spark-based Fog nodes can considerably improve Fog nodes scalability and reduce their power consumption, with respect to traditional Fog nodes implementations. It is also interesting to highlight that Spark brings noticeable benefits when dealing with tasks that can be parallelized, as noted by the authors.…”

Section: Impact Of Fog Computing Versus Cloudmentioning

confidence: 99%

S×C4IoT: A Security-by-contract Framework for Dynamic Evolving IoT Devices

Giaretta

Dragoni

Massacci

2021

ACM Trans. Sen. Netw.

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The Internet of Things (IoT) revolutionised the way devices, and human beings, cooperate and interact. The interconnectivity and mobility brought by IoT devices led to extremely variable networks, as well as unpredictable information flows. In turn, security proved to be a serious issue for the IoT, far more serious than it has been in the past for other technologies. We claim that IoT devices need detailed descriptions of their behaviour to achieve secure default configurations, sufficient security configurability, and self-configurability. In this article, we propose S×C4IoT, a framework that addresses these issues by combining two paradigms: Security by Contract (S×C) and Fog computing. First, we summarise the necessary background such as the basic S×C definitions. Then, we describe how devices interact within S×C4IoT and how our framework manages the dynamic evolution that naturally result from IoT devices life-cycles. Furthermore, we show that S×C4IoT can allow legacy S×C-noncompliant devices to participate with an S×C network, we illustrate two different integration approaches, and we show how they fit into S×C4IoT. Last, we implement the framework as a proof-of-concept. We show the feasibility of S×C4IoT and we run different experiments to evaluate its impact in terms of communication and storage space overhead.

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“…the Map task and Reduce task for making the decision. According to [38], authors conducted a comprehensive MapReduce job profiling by executing a smaller input dataset and observed the execution time of all phases of the job, i.e. initialization, Map, shuffle and Reduce.…”

Section: Preprocessingmentioning

confidence: 99%

TMaR: a two-stage MapReduce scheduler for heterogeneous environments

Maleki

Faragardi

Rahmani

et al. 2020

Hum. Cent. Comput. Inf. Sci.

Self Cite

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In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. In this paper, we propose a two-stage Map and Reduce task scheduler for heterogeneous environments, called TMaR. TMaR schedules Map and Reduce tasks on the servers that minimize the task finish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shuffling traffic. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network traffic. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network traffic and achieves by an average of 29%, 36%, and 14% performance using Wordcount, Sort, and Grep benchmarks. Besides, the power reduction of TMaR is up to 12%.

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SoFA: A Spark-oriented Fog Architecture

Cited by 14 publications

References 19 publications

READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

S×C4IoT: A Security-by-contract Framework for Dynamic Evolving IoT Devices

TMaR: a two-stage MapReduce scheduler for heterogeneous environments

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