Towards Metaheuristic Scheduling Techniques in Cloud and Fog: An Extensive Taxonomic Review

Singh, Raj Mohan; Awasthi, Lalit Kumar; Sikka, Geeta

doi:10.1145/3494520

Cited by 40 publications

(19 citation statements)

References 161 publications

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“…Search metaheuristics [95] guide procedures that use transformations or moves to traverse the space of alternative solutions and exploit the associated environment structures. Evolutionary metaheuristics [96] are focused on procedures based on solution sets that evolve over the solution space.…”

Section: Metaheuristic Learningmentioning

confidence: 99%

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

Ariza-Colpas

Vicario

Carrascal

et al. 2022

Sensors

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The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.

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Section: Metaheuristic Learningmentioning

confidence: 99%

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

Ariza-Colpas

Vicario

Carrascal

et al. 2022

Sensors

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“…In this section, we review meta-heuristic scheduling algorithms which are more related to our work. References [6,7] represent recent literature surveys on meta-heuristic scheduling methods in cloud and fog. The previous works that use metaheuristic algorithms can be further categorized based on their used algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Ding et al [10] also use PSO to optimize the execution cost of the workflow under deadline constraints. While several other previous works leverage PSO-based task scheduling [11][12][13], they did not consider the deadline as one of their optimization objectives [6].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

PCP-ACO: a deadline-constrained workflow scheduling algorithm for cloud environment

Shobeiri

Rastaghi

Abrishami

et al. 2022

Preprint

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A cloud computing environment is the most popular choice for workflow execution, as it gives customers on-demand access to computing resources. However, in cloud workflow scheduling, cloud-native requirements regarding QoS requirements such as monetary cost and execution time should be taken into account. This paper proposes PCP-ACO, a list scheduling algorithm for minimizing the execution cost of a workflow, while meeting its user-defined deadline in cloud environments. In PCP-ACO, first a topological sort of the workflow tasks is computed to assign a priority to each task. Then, Ant Colony Optimization (ACO) meta-heuristic is used to assign a proper resource to each task of the workflow, in order of their priorities. The Partial Critical Path (PCP) concept is also used as a heuristic to guide ACO algorithm. Several experiments are conducted using real scientific workflows, and the cost saving is compared with PSO and IC-PCP algorithms. The experimental results show that the proposed algorithm outperforms other compared algorithms in terms of cost saving.

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“…Because metaheuristic algorithms can produce nearly optimal solutions within a reasonable time frame, the use of these algorithms in job scheduling has garnered significant attention in recent times. In the cloud and fog environment, Singh R M et al [8] provide a thorough taxonomic overview and analysis of modern metaheuristic scheduling techniques utilizing extensive evaluation criteria. A novel metaheuristic approach for maximizing collaborative job scheduling in cloud data centers was put out by Alboaneen, Dabiah, et al [9].…”

Section: Introductionmentioning

confidence: 99%

An Exploration of Multitasking Scheduling Considering Interruptible Job Assignments, Machine Aging Effects, the Influence of Deteriorating Maintenance, and Symmetry

Zeng

2024

Symmetry

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The unique topic of allocating and scheduling tasks on a single machine in a multitasking environment is the main emphasis of this research, which also takes into account the effects of worsening maintenance and job-dependent aging effects. In this scenario, the performance and efficiency of the machine in handling different tasks should be symmetric, without significant bias due to the nature or size of the tasks. In a multitasking environment, waiting for jobs can disrupt the processing of the primary job being currently handled. As a result, the actual time required to complete a task becomes erratic and contingent upon the duration of the disruption. In addition to figuring out the best time for maintenance, where to put the due-window, and how big it should be in a multitasking environment, the primary objective is to minimize the costs associated with meeting due-window regulations. To tackle this problem, we propose two optimal algorithms. Additionally, we conduct numerical experiments to compare our approach with the classic due date assignment problem. Interestingly, we observe that in most cases, the average and minimum percentage costs tend to increase as the quantity of jobs increases. However, it is noteworthy that, when the number of jobs is relatively small, specifically when it does not exceed 20, there are instances where these costs decrease with an increase in the number of jobs.

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Towards Metaheuristic Scheduling Techniques in Cloud and Fog: An Extensive Taxonomic Review

Cited by 40 publications

References 161 publications

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

PCP-ACO: a deadline-constrained workflow scheduling algorithm for cloud environment

An Exploration of Multitasking Scheduling Considering Interruptible Job Assignments, Machine Aging Effects, the Influence of Deteriorating Maintenance, and Symmetry

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