Resource scheduling methods in cloud and fog computing environments: a systematic literature review

Rahimikhanghah, Aryan; Tajkey, Melika; Rezazadeh, Bahareh; Rahmani, Amir Masoud

doi:10.1007/s10586-021-03467-1

Cited by 35 publications

(8 citation statements)

References 76 publications

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“…Several research studies addressed the challenge of scheduling jobs in cloud computing [16][17][18][19]. The authors in [20] proposed an approach named MRFOSSA, which relies on utilizing SSA to enhance the local search capability of MRFO, which typically increases the speed of convergence towards the optimal solution.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Yousif,

Shohdy,

Hassan

et al. 2023

Electronics

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IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients’ jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat’s behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs.

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Section: Related Workmentioning

confidence: 99%

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Yousif,

Shohdy,

Hassan

et al. 2023

Electronics

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“…They acknowledge that human beings can plunder resources to meet their own development. There are 53 people who believe that "the balance of nature is very fragile, once it is destroyed, it is difficult to recover"; Among the respondents, 55 people believed that the main function of nature reserves was to protect natural resources, while only 4 people held negative views on this; Only 7 of the 59 employees have a positive attitude towards "people must live in harmony with nature if they want to develop", and only 7 have a skeptical or negative attitude [15][16].…”

Section: Natural Environment Protection Design Experiments Under the ...mentioning

confidence: 99%

Natural Environment Protection under the Integration of Cloud Computing and Green Energy Effects

2022

NEP

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In recent years, with the continuous improvement of people's living standards, the requirements for the environment are also rising. People began to realize the importance of natural environment protection, and also began to put environmental protection in an important position. By building a sustainable development system, they considered environmental issues from multiple perspectives. Based on this, this paper analyzes the natural environment protection under the integration of cloud computing and green energy effects, with a view to providing a reference basis for China's natural environment protection and future planning for relevant departments. Through in-depth investigation on the environmental awareness of the staff, the experimental results show that 70.59% of the scenic spot managers have negative attitudes towards human resource abuse and environmental damage; 76.48% of the employees hold negative views on "you litter in the scenic area"; Only 23.53% of the scenic area managers have a negative view of "people have the right to change the natural environment for human needs", and 64.80% of the scenic area managers think this is positive. In order to achieve effective protection of the environment, relevant departments should constantly improve the level of relevant technologies, reduce energy consumption while meeting work needs, and ensure that natural resources are used rationally.

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“…Efficient resource utilization is also a significant challenge that can directly impact energy consumption ( Bharany et al, 2022 ; Rahimikhanghah et al, 2022 ; Mansouri et al, 2023 ). Despite the various methodologies available, this field of study is still emerging due to heterogeneous workloads that fluctuate with cloud-supported services.…”

Section: Introductionmentioning

confidence: 99%

LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing

Mehmood,

Latif,

Jamail

et al. 2024

PeerJ Computer Science

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This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and real-world cloud datasets, stressing the need for appropriate drift detectors tailored to the cloud domain. A modified version of Long Short-Term Memory (LSTM) called the LSTM Drift Detector (LSTMDD) is proposed and compared with other top drift detection techniques using prediction error as the primary evaluation metric. LSTMDD is optimized to improve performance in detecting anomalies in non-Gaussian distributed cloud environments. The experiments show that LSTMDD outperforms other methods for gradual and sudden drift in the cloud domain. The findings suggest that machine learning techniques such as LSTMDD could be a promising approach to addressing the problem of concept drift in cloud computing, leading to more efficient resource allocation and improved performance.

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Resource scheduling methods in cloud and fog computing environments: a systematic literature review

Cited by 35 publications

References 76 publications

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

Natural Environment Protection under the Integration of Cloud Computing and Green Energy Effects

LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing

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