Mobility aware autonomic approach for the migration of application modules in fog computing environment

Martin, John P.; Kandasamy, A.; Chandrasekaran, K.

doi:10.1007/s12652-020-01854-x

Cited by 23 publications

(9 citation statements)

References 32 publications

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“…The data demanding higher storage and processing resources than available at the fog node are transferred to the cloud. The execution cost at cloud includes the cost required for the execution of application modules in the cloud [ 57 , 58 , 59 ]. Fog computing adaptation reduces the execution cost in the cloud by minimizing the amount of data transfer to the cloud server.…”

Section: Simulation Setup and Resultsmentioning

confidence: 99%

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Hassan

Ahmad

et al. 2020

Sensors

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The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

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Section: Simulation Setup and Resultsmentioning

confidence: 99%

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Hassan

Ahmad

et al. 2020

Sensors

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“…Each device in the infrastructure can accommodate application modules [27]. Among the various types of devices available in the network, our main concern is the smart gateway device located on the second layer, which links the first and third layers and provides resources for the basic processing of tasks.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

Hassan¹,

Ahmad²,

Nebhen³

et al. 2022

Computers, Materials &Amp; Continua

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The modern paradigm of the Internet of Things (IoT) has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing. However, such applications cannot meet strict quality of service (QoS) requirements. The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data. Generally, cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers. Fog devices with different computing capabilities must process the data generated by the end device, so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task. In addition, when there is an inconsistent connection delay between the fog and the terminal device, the deployment of such applications becomes more complicated. In this article, we propose an algorithm that can effectively place application modules on network nodes while considering connection delay, processing power, and sensing data volume. Compared with traditional cloud computing deployment, we conducted simulations in iFogSim to confirm the effectiveness of the algorithm. The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption. Therein, latency and execution time is insensitive to the number of sensors.

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“…Authors in [33] propose the SEGUE framework for migration decisions and use the ARIMA method to predict system QoS. In [34], authors propose a MAPE framework and a Genetic Algorithm for delay-triggered services migration in the Fog. This work focuses only on the delay metric and does not consider the energy cost of migrations.…”

Section: B Iot Services Placement Int the Fog With Mobility Energy And Qos Considerationsmentioning

confidence: 99%

Investigating Mobility-aware Strategies for IoT Services Placement in the Fog under Energy and QoS Constraints

Djemai¹,

Stolf

Monteil

et al. 2021

JCOMSS

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Mobility of Internet of Things (IoT) objects is a key characteristic of IoT environments. It brings dynamicity, uncertainty and raises many challenges when it is associated with computation and network resources management for IoT applications. The resources management problem under objects mobility consideration is even more sensitive if we consider that various IoT applications have stringent Quality of Service (QoS) needs. Fog Computing is a distributed computation paradigm that increases data centers computation and storage abilities with nodes between end-users and the Cloud. Fog computing offers a large distributed infrastructure to support IoT applications needs by bringing services closer to end users. However, Fog infrastructures inherit the energy greediness characteristics of both data centers and network infrastructures. This work investigates the IoT services placement problem in the Fog as an optimization problem to minimize energy consumption and enhance QoS while considering mobility of IoT objects. We model the placement problem as a multi-objective optimization problem and we propose a location history based mobility model (HTM) to estimate future locations of IoT mobile nodes. We propose a framework composed of online strategies for IoT services placement and a Mobility-aware Genetic Algorithm (MGA) for services migrations. We evaluate our strategies through iFogSim simulator and compare the proposed framework to migrations and placement strategies from the literature based on Shortest Access Point migration strategy (SAP) and with Penguins Search Optimization Algorithm (PeSOA). Experiments show that the proposed framework outperforms literature approaches for the considered objectives and for various configurations of the mobile environment.

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Mobility aware autonomic approach for the migration of application modules in fog computing environment

Cited by 23 publications

References 32 publications

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

Investigating Mobility-aware Strategies for IoT Services Placement in the Fog under Energy and QoS Constraints

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