scholarly journals Fog Computing: Towards Dynamically Controlling the Offloading Threshold and Managing Fog Resources in Online Dynamic Systems

2021 ◽  
Author(s):  
Faten Alenizi ◽  
Omer Rana
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Fog Computing ◽  
Location Awareness ◽  
Computing Systems ◽  
Dynamic Task ◽  
Delay Sensitive ◽  
Maximum Benefit ◽  
Computational Offloading ◽  
Iot Devices

Fog computing is a potential solution to overcome the shortcomings of the cloud computing processing of IoT tasks. These drawbacks can be high latency, location awareness and security, and it is attributed to the distance between IoT devices and servers, network congestion and other variables. Although fog computing has evolved as a solution to these challenges, it is known for having limited resources that need to be consciously utilised, or any of its ad-vantages would be lost. Computational offloading and resource management are critical concerns to be considered to get maximum benefit of the available resource at fog computing systems and benefit from its advantages. Computational offloading and resource management are important issues to be considered to get maximum benefit of the available resource at fog computing systems and benefit from its advantages. In this article, in vehicular traffic applications, we introduce a dynamic online offloading scheme that involves the execution of delay-sensitive ac-tivities. This paper proposes an architecture of a fog node that enables a fog node to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to solve an optimisation problem aimed at minimising overall delay, improving throughput, and minimising energy consumption at the fog layer, while maximising the use of resource-constrained fog nodes. Compared with other benchmarks, our approach can reduce the delay by up to 95.38% and reduce energy consumption by up to 67.71% in fog nodes. Additionally, this approach enhances throughput by 71.08%.

scholarly journals Dynamically Controlling Offloading Thresholds in Fog Systems

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2512 ◽  
Author(s):  
Faten Alenizi ◽  
Omer Rana
Keyword(s):  
Energy Consumption ◽  
Fog Computing ◽  
Limited Resources ◽  
Location Awareness ◽  
Computing Systems ◽  
Dynamic Task ◽  
Delay Sensitive ◽  
Reduce Energy Consumption ◽  
Iot Devices ◽  
Dynamic Task Scheduling

Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.

scholarly journals Minimising Delay and Energy in Online Dynamic Fog Systems

2020 ◽  
Author(s):  
Faten Alenizi ◽  
Omer Rana
Keyword(s):  
Energy Consumption ◽  
Fog Computing ◽  
Cloud Services ◽  
Dynamic Task ◽  
Delay Sensitive ◽  
Reduce Energy Consumption ◽  
Iot Devices ◽  
User Tasks ◽  
Use Of Internet

The increasing use of Internet of Things (IoT) devices generates a greater demand for data transfers and puts increased pressure on networks. Additionally, connectivity to cloud services can be costly and inefficient. Fog computing provides resources in proximity to user devices to overcome these drawbacks. However, optimisation of quality of service (QoS) in IoT applications and the management of fog resources are becoming challenging problems. This paper describes a dynamic online offloading scheme in vehicular traffic applications that require execution of delay-sensitive tasks. This paper proposes a combination of two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC) that aim to minimise overall delay, enhance throughput of user tasks and minimise energy consumption at the fog layer while maximising the use of resource-constrained fog nodes. Compared to other schemes, our experimental results show that these algorithms can reduce the delay by up to 80.79% and reduce energy consumption by up to 66.39% in fog nodes. Additionally, this approach enhances task execution throughput by 40.88%.

scholarly journals Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems

2020 ◽  
Vol 7 (1) ◽  
pp. 773-785 ◽  
Author(s):  
Qiong Wu ◽  
Hanxu Liu ◽  
Ruhai Wang ◽  
Pingyi Fan ◽  
Qiang Fan ◽  
...  
Keyword(s):  
Fog Computing ◽  
Computing Systems ◽  
Delay Sensitive ◽  
Task Offloading

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

2020 ◽  
pp. 42-52
Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Mobile Network ◽  
Resource Provisioning ◽  
Management Framework ◽  
Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

scholarly journals A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

2019 ◽  
Vol 9 (1) ◽  
pp. 178 ◽  
Author(s):  
Belal Sudqi Khater ◽  
Ainuddin Wahid Bin Abdul Wahab ◽  
Mohd Yamani Idna Bin Idris ◽  
Mohammed Abdulla Hussain ◽  
Ashraf Ahmed Ibrahim
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Fog Computing ◽  
Raspberry Pi ◽  
Space Representation ◽  
Location Awareness ◽  
Resource Limitations ◽  
System Calls ◽  
Iot Devices ◽  
Hidden Layer

Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force Academy Windows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi.

scholarly journals Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2659 ◽  
Author(s):  
Yinghui Zhang ◽  
Jiangfan Zhao ◽  
Dong Zheng ◽  
Kaixin Deng ◽  
Fangyuan Ren ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Location Awareness ◽  
False Data Injection ◽  
Injection Attacks ◽  
Iot Devices

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

scholarly journals Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

2021 ◽  
Vol 5 (2) ◽  
pp. 105
Author(s):  
Wasswa Shafik ◽  
S. Mojtaba Matinkhah ◽  
Mamman Nur Sanda ◽  
Fawad Shokoor
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Response Time ◽  
Smart Grids ◽  
Smart Cities ◽  
Fog Computing ◽  
The Internet ◽  
Delay Sensitive ◽  
Service Delay

In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.

scholarly journals Task Placement on Fog Computing Made Efficient for IoT Application Provision

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Minh-Quang Tran ◽  
Duy Tai Nguyen ◽  
Van An Le ◽  
Duc Hai Nguyen ◽  
Tran Vu Pham
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Intelligent Transportation System ◽  
Fog Computing ◽  
Simulated Data ◽  
Global Scale ◽  
Operational Cost ◽  
Network Load ◽  
Iot Devices ◽  
Location Response

Fog computing is one of the promising technologies for realizing global-scale Internet of Things (IoT) applications as it allows moving compute and storage resources closer to IoT devices, where data is generated, in order to solve the limitations in cloud-based technologies such as communication delay, network load, energy consumption, and operational cost. However, this technology is still in its infancy stage containing essential research challenges. For instance, what is a suitable fog computing scheme where effective service provision models can be deployed is still an open question. This paper proposes a novel multitier fog computing architecture that supports IoT service provisioning. Concretely, a solid service placement mechanism that optimizes service decentralization on fog landscape leveraging context-aware information such as location, response time, and resource consumption of services has been devised. The proposed approach optimally utilizes virtual resources available on the network edges to improve the performance of IoT services in terms of response time, energy, and cost reduction. The experimental results from both simulated data and use cases from service deployments in real-world applications, namely, the intelligent transportation system (ITS) in Ho Chi Minh City, show the effectiveness of the proposed solution in terms of maximizing fog device utilization while reducing latency, energy consumption, network load, and operational cost. The results confirm the robustness of the proposed scheme revealing its capability to maximize the IoT potential.

scholarly journals Load Balancing at Fog Nodes using Scheduling Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4129-4134
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Fog Computing ◽  
Scheduling Algorithms ◽  
Small Scale ◽  
Location Awareness ◽  
Time Cost ◽  
Cloud Server ◽  
Iot Devices ◽  
Cloud Servers

Cloud Computing proves to be most predominant innovative field in the area of Information technology. Cloud is best suited for small scale to large scale businesses and personal purposes such as storing, computing, managing data & resources, running applications and many more. Due to increasing large volumes of data over cloud servers created subsequent specific issues like data maintainability, network elasticity, managing Internet of Things (I.o.T’s) devices and many more. Recent progresses in Technology are given rise to fog computing or decentralized cloud to overcome cloud server issues called fog nodes. In this paper we present a brief note on how cloud issues can overcome using fog nodes benefits along with elaboration of load balancing factor. To maintain load balancing of fog nodes no much appreciable work took place in the field of fog computing. This paper proposes a scheduler which receives the devices in to a Job Queue to be connected over cloud. To apply scheduling algorithms like F.C.F.S, S.J.F, P.S, R.R and W.R.R. over fog nodes will be discussed along with their merits & demerits. At last we try to compare the various parameters of load balancing among various scheduling algorithms. In this paper we focus on how fog nodes perform functions like considerable storages, low latency, heterogeneity, allocation & interaction with limited IoT devices and Security along with architecture cloud to fog. During allocation of IoT devices to various fog nodes we will come across a serious issues i.e load balancing on fog nodes. Our detailed study presents the comparison of above mentioned scheduling algorithms load balancing factors such as rich resources allocations & Balancing among fog nodes, Identification of devices, Authentication of fog nodes, bandwidth consumption, location awareness, response time, cost maintenances, Intrusion detection, fault forbearances and maintainability.

scholarly journals An efficient population-based multi-objective task scheduling approach in fog computing systems

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zahra Movahedi ◽  
Bruno Defude ◽  
Amir mohammad Hosseininia
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Rapid Development ◽  
Fog Computing ◽  
Scheduling Problem ◽  
Distributed Resources ◽  
Iot Applications ◽  
Whale Optimization ◽  
Iot Devices

AbstractWith the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

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