Scheduling of Scientific Workflows in Multi-Fog Environments Using Markov Models and a Hybrid Salp Swarm Algorithm

Ahmed, Omed Hassan; Lu, Joan; Ahmed, Aram Mahmood; Rahmani, Amir Masoud; Hosseinzadeh, Mehdi; Màsdàrí, Mohàmmàd

doi:10.1109/access.2020.3031472

Cited by 29 publications

(8 citation statements)

References 47 publications

(47 reference statements)

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“…𝑙 = (𝑎2 − 1). 𝑟 + 1 (11) r is the random variable between [0,1] whereas variable a2 decreases linearly from -1 to -2 with iterations according to equation (13).…”

Section: …………………(9)mentioning

confidence: 99%

A Hybrid Particle Whale Optimization Algorithm for Workflow Scheduling in Cloud-Fog-Mobile Computing Environment

Bansal

Aggarwal

2022

Preprint

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Cloud computing is the extensively used technology these days. Due to the usage of smart devices, a huge amount of data is produced. The processing of this data in real time is a big challenge for cloud servers. Fog computing is the solution for this, but fog has its own limitation in form of storage. To overcome, this cloud-fog architecture is preferred. In cloud-fog architecture, workflow scheduling is an open research area but finding an optimal algorithm is a major challenge. Some researchers proposed meta-heuristic algorithms to solve workflow scheduling issues but they are trapped locally and fails to give the global optimal solution. To solve workflow scheduling problems, we propose the PWOA algorithm, a hybrid of Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The goal of this algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) of dependent tasks in a cloud-fog-mobile computing environment. Because it uses the features of both the standard PSO and WOA algorithms, the proposed algorithm overcomes the trapping problem also. In this article, the simulation results were compared to standard PSO and WOA algorithms using several benchmarks of four different scientific workflows (Cybershake, Epigenomics, Inspiral, Montage, and Sipht) with different numbers of tasks assigned in the proposed algorithm performed better.

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“…𝑙 = (𝑎2 − 1). 𝑟 + 1 (11) r is the random variable between [0,1] whereas variable a2 decreases linearly from -1 to -2 with iterations according to equation (13).…”

Section: …………………(9)mentioning

confidence: 99%

A Hybrid Particle Whale Optimization Algorithm for Workflow Scheduling in Cloud-Fog-Mobile Computing Environment

Bansal

Aggarwal

2022

Preprint

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“…Ahmed et al [17] used two algorithms particle swarm optimization (PSO) and salp swarm algorithm (SSA) for scheduling work in a fog environment and proposed Markov chain models that consisted of two stages, the first to calculate average bandwidth in-network and the second to obtain an average number of virtual machines in network to detect DDoS attacks, in order to improve the accuracy of their results. In their evaluation, they discovered that the suggested technique may minimize the amount of tasks while also detecting threats.…”

Section: Related Workmentioning

confidence: 99%

Detection and mitigation of DDoS attacks in internet of things using a fog computing hybrid approach

Hassan

Manaa

2022

Bulletin EEI

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The introduction of a new technology has aided the exponential growth of the internet of things (IoT), allowing for the connecting of more devices in the IoT network to be made possible by the availability of quicker connections and reduced latency. As IoT networks have become more prevalent and widely used, security has become one of the fundamental requirements, and a distributed denial of service (DDoS) attack poses a significant security threat due to the limited resources (CPU, memory, open source, persistent connection) that can be used to either intentionally or unintentionally increase DDOS attacks. Fog computing is proposed in this study as a framework for real-time detection and mitigation of DDoS assaults. Fog computing is accurate and quick in detecting attacks due to its proximity to IoT devices. DDOS assaults are detected using an approach that combines randomness measurement of traffic with k-nearest neighbors (KNN) machine learning algorithm. Suggested system obtained 100% detection accuracy for transmission control protocol TCP attacks, 98.79% detection accuracy for UDP attacks, and 100% detection accuracy for internet control message protocol ICMP attacks.

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“…Fog computing resources are therefore ideal for terminal users today [13]. Due to the dynamic and uncertainty of resources and the high variability and unpredictability of the fog computing environment, rational resource scheduling and allocation are particularly important, which have become a research hotspot in the industry [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Task Scheduling Strategy for Intelligent Manufacturing in the Big Data-Driven Fog Computing Environment

Zhou

2022

Mobile Information Systems

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A heuristic task scheduling strategy for intelligent manufacturing in the big data-driven fog computing environment is proposed to address the problem that current resource scheduling and allocation methods in the fog computing environment cannot comprehensively consider the dynamics and uncertainty of resources, resulting in the prolonged delay and high energy consumption. First, a system model with three computing modes for intelligent manufacturing is constructed based on intelligent terminal devices, fog nodes, fog servers, fog gateways, and the cloud. Then, the objective function is optimized by jointly considering the delay matrix, the energy consumption matrix, and the reliability matrix, and corresponding constraints are given based on the selection of computing modes, the decision variables of fog nodes as well as the constraints about the delay. Finally, the intervals of crossover-mutation operators are divided according to the fitness value, and individuals of the population are updated by taking different operations based on the operators in different intervals, so as to achieve an improvement on the traditional genetic algorithms. Meanwhile, a fog resource scheduling algorithm is proposed based on the improved adaptive genetic algorithm. Simulation experiments are conducted to compare and analyze the delay, energy consumption, and reliability of the proposed method with three other methods under the same conditions. The results show that the proposed method has the lowest delay and energy consumption and the highest reliability, with values of 361.3 s, 352.4 J, and 94.6%, respectively, when the number of task requests is 500. The performance is better than the other three comparison methods.

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Scheduling of Scientific Workflows in Multi-Fog Environments Using Markov Models and a Hybrid Salp Swarm Algorithm

Cited by 29 publications

References 47 publications

A Hybrid Particle Whale Optimization Algorithm for Workflow Scheduling in Cloud-Fog-Mobile Computing Environment

A Hybrid Particle Whale Optimization Algorithm for Workflow Scheduling in Cloud-Fog-Mobile Computing Environment

Detection and mitigation of DDoS attacks in internet of things using a fog computing hybrid approach

A Heuristic Task Scheduling Strategy for Intelligent Manufacturing in the Big Data-Driven Fog Computing Environment

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