Multi-objective Task Scheduling in cloud-fog computing using goal programming approach

Najafizadeh, Abbas; Salajegheh, Afshin; Rahmani, Amir Masoud; Sahafi, Amir

doi:10.1007/s10586-021-03371-8

Cited by 58 publications

(26 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to secure the allocation of tasks on cloud and fog nodes, Najafizadeh, Salajegheh [28] proposed a multi-objective simulated annealing algorithm. A compromise solution is found by applying the goal-programming approach.…”

Section: Related Workmentioning

confidence: 99%

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Yang¹

2023

IJACSA

View full text Add to dashboard Cite

Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.

show abstract

Section: Related Workmentioning

confidence: 99%

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Yang¹

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…In this study 87 taking completion time as deadline author proposed multiobjective simulated annealing (MOSA) algorithm for allocation of tasks to fog and cloud nodes. To reach at the desired results GPA approach is used to get solution of tasks.…”

Section: Technical Classification Of Task Schedulingmentioning

confidence: 99%

A systematic review of task scheduling approaches in fog computing

Bansal

Aggarwal

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real-time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements.The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively.Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Multi-objective Task Scheduling in cloud-fog computing using goal programming approach

Cited by 58 publications

References 33 publications

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

A systematic review of task scheduling approaches in fog computing

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

Contact Info

Product

Resources

About