Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach

Azizi, Sadoon; Shojafar, Mohammad; Abawajy, Jemal; Buyya, Rajkumar

doi:10.1016/j.jnca.2022.103333

Cited by 84 publications

(38 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A mathematical formulation of the fog node task scheduling problem is presented by Azizi, Shojafar [25] to reduce fog node energy consumption while maintaining IoT QoS requirements. The goal of their model was to reduce deadline violation time.…”

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

“…However, the centralized schemes have several challenging issues, such as scalability, single point of failure, the complexity of many-to-many offloading scenario, and etc. [12]- [15]. Therefore, distributed offloading approaches in FC network bring researchers' attention.…”

Section: Related Workmentioning

confidence: 99%

D²FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems

Ataie

Taami

Azizi

et al. 2022

2022 IEEE International Performance, Computing, and Communications Conference (IPCCC)

Self Cite

View full text Add to dashboard Cite

The Internet of Things (IoT) has grown at a rapid pace in recent years. It requires a large amount of data and massive computational resources, thus the concept of Fog Computing (FC) has emerged. FC attempts to overcome network latency by bringing computational resources closer to IoT devices. One important part of FC is an offloading m echanism t o make proper decisions for better utilizing of FC node(s), especially for real-time (low latency and high throughput) applications. Generally, offloading p olicies a re c ategorized a s c entralized and distributed. However, by growing numbers of IoT devices which leads to expansion of FC layer beyond the initial configurations, centralized scheduling solutions for time-sensitive tasks suffers from two major challenges: first, i ncreasing c omplexity, and second, non-fault tolerating. In order to address these issues, scalable decentralized/distributed approaches have been developed to schedule tasks through an autonomous collaboration between a small number of nodes (neighbors). Without a thorough picture of the network or nodes' state, it is difficult to design algorithms that make optimum decisions. This paper presents a scalable algorithm for offloading t ime-sensitive t asks t hrough a s emi-network aware distributed scheduling mechanism. Based on the evaluation results obtained for acceptance rate, response time, and network resource usage, the proposed method outperforms the state-of-the-art on average.

show abstract

“…In this paper, 78 authors formulated mathematically equations to solve the task scheduling problems in keeping in mind the QoS parameters and to minimize total energy consumption. To map IoT tasks efficiently to fog nodes, the author proposed PSG and PSG‐M, two semigreedy algorithms.…”

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

Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach

Cited by 84 publications

References 44 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

D²FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems

A systematic review of task scheduling approaches in fog computing

Contact Info

Product

Resources

About

Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach

Cited by 84 publications

References 44 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

D2FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems

A systematic review of task scheduling approaches in fog computing

Contact Info

Product

Resources

About

D²FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems