Directed Search: A New Operator in NSGA-II for Task Scheduling in IoT Based on Cloud-Fog Computing

Mousavi, Soghra; Mood, Sepehr Ebrahimi; Souri, Alireza; Javidi, Mohammad Masoud

doi:10.1109/tcc.2022.3188926

Cited by 17 publications

(9 citation statements)

References 26 publications

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“…However, there can occur starvation of long tasks. Mousavi et al 17 proposed a D‐NSGA‐II algorithm to minimize energy consumption and response time at the same time while considering the deadline. The experimental results demonstrate its efficacy as compared with other traditional meta‐heuristic algorithms.…”

Section: Related Workmentioning

confidence: 99%

Deadline‐cost aware task scheduling algorithm in fog computing networks

Sultan Hajam

2024

Int J Communication

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SummaryThe Internet of Things (IoT) has provided us with various applications that need processing in real‐time. Fog computing has arisen as a highly effective computing paradigm that provides services closer to the IoT devices and hence fulfills the need for rapid response time and delay requirements of IoT applications. However, the heterogeneity of IoT tasks and the limited resources of fog nodes pose considerable challenges to task scheduling. Therefore, the effective utilization of fog resources is highly significant and necessitates an optimal and intelligent approach to task scheduling. This paper presents the “deadline‐cost aware task scheduling” (DCaTS) algorithm, which is designed for fog‐based IoT applications. The primary objective of this algorithm is to minimize the monetary cost while taking into account the deadline requirements and priority of tasks. The efficacy of DCaTS is assessed by analyzing various parameters, including completion time, monetary cost, response time, and waiting time. The experimental results show that in comparison with traditional policies, the proposed algorithm shows superiority in all the parameters. The proposed algorithm reduces the monetary cost up to 11.07% compared with the second best result. The results also indicate that DCaTS achieves a reduction of 10.05% in completion time, 10% in response time, and 10.12% in waiting time compared with the second best result.

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Section: Related Workmentioning

confidence: 99%

Deadline‐cost aware task scheduling algorithm in fog computing networks

Sultan Hajam

2024

Int J Communication

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“…The optimal solution reduces the computation complexity in the resource allocation procedure and improves the overall performance. A modified version of NSGA‐II is presented in Mousavi et al 30 using a dominant non‐sorting genetic algorithm (DNSGA‐II) to minimize the fog computing environment's response time and energy consumption. The modified approach includes a recombination operator, which provides a better balance between exploitation and exploration characteristics.…”

Section: Related Workmentioning

confidence: 99%

An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks

Vijayasekaran,

Duraipandian

2024

Int J Communication

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SummaryWith increasing challenges and research in edge‐assisted IoT models, an improved resource scheduling approach exploiting deep learning concepts is proposed in this research work. Improvement in performance in the proposed work is achieved primarily by addressing the response time and waiting time. This could be achieved if the optimal resources are scheduled without any delay. The presented concatenated deep learning technique considers the time series IoT network source requirements and allocates optimal resources from the resource pool, considering resource availability, workload, and computation time. Two deep learning techniques, namely, CNN and GRU, are utilized for the concatenation process, while resource characteristics are considered as features that are extracted and classified to schedule optimal resources. Novelty in the proposed work is exhibited in the form of the concatenation process proposed. The proposed resource scheduling performance metrics are compared with the performance of the existing scheduling model through simulation analysis for better validation. The proposed model selects the optimal resources from the resource pool using concatenated features and schedules for respective requests with minimum delay and waiting time, which increases the overall efficiency of the edge computing IoT networks.

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“…One strategy of Edge Computing may be to focus on virtualization and industrialised applications at the edge network. Such an evolution to Edge Computing would be enabled by currently emerging technologies such as Software Defined Networking (SDN), Network Functions Virtualization (NFV) and Information-Centric Networking (ICN) [186]. These new technologies provide novel tools that increase flexibility in designing networks [103].…”

Section: Technology-driven Viewmentioning

confidence: 99%