An improved list-based task scheduling algorithm for fog computing environment

Madhura, Rajarethinam; Elizabeth, B. Lydia; Uthariaraj, V. Rhymend

doi:10.1007/s00607-021-00935-9

Cited by 34 publications

(20 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The drawback of the PPTS algorithm is that it does not remove the execution cost of the current task from the PCM value in the processor selection strategy, which will affect the choice of processor. In addition, [34,36,37,41,46] also proposed different list scheduling algorithms, which can also achieve good scheduling results. Next, the algorithms mentioned in the recently proposed IPPTS [34] and literature [46] will be introduced in detail, which will also be used as a comparison algorithm for the algorithms proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, [34,36,37,41,46] also proposed different list scheduling algorithms, which can also achieve good scheduling results. Next, the algorithms mentioned in the recently proposed IPPTS [34] and literature [46] will be introduced in detail, which will also be used as a comparison algorithm for the algorithms proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in literature [46] proposed a new list scheduling algorithm (referred to as RE-18 in this paper). It is divided into three phases: the level sorting phase, the task prioritization phase, and the processor selection phase.…”

Section: Related Workmentioning

confidence: 99%

“…The experiment is based on randomly generated graphs and related realworld application graphs. The results show that the BVSL algorithm can obtain better scheduling results than the algorithms proposed in IPPTS [34] and literature [46]. The main contributions of the algorithm are as follows:…”

Section: Introductionmentioning

confidence: 97%

“…However, such problems have been proven to be NP-complete [31,32]. To solve this problem, many heuristic-based algorithms [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] have been proposed in recent years, which are aimed at minimizing the scheduling length and other goals. These algorithms can be roughly divided into three categories: list scheduling, clustering scheduling, and duplication scheduling.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System

Zhou

Liao

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Edge computing needs the close cooperation of cloud computing to better meet various needs. Therefore, ensuring the efficient implementation of applications in cloud computing is not only related to the development of cloud computing itself but also affects the promotion of edge computing. However, resource management and task scheduling strategy are important factors affecting the efficient implementation of applications. Therefore, aiming at the task scheduling problem in cloud computing environment, this paper proposes a new list scheduling algorithm, namely, based on a virtual scheduling length (BVSL) table algorithm. The algorithm first constructs the predicted remaining length table based on the prescheduling results, then constructs a virtual scheduling length table based on the predicted remaining length table, the current task execution cost, and the actual start time of the task, and calculates the task priority based on the virtual scheduling length table to make the overall path the longest task is scheduled first, thus effectively shorten the scheduling length. Finally, the processor is selected for the task based on the predicted remaining length table. The selected processor may not be the earliest for the current task, but it can shorten the finish time of the task in the next phase and reduce the scheduling length. To verify the effectiveness of the scheduling method, experiments were carried out from two aspects: randomly generated graphs and real-world application graphs. Experimental results show that the BVSL algorithm outperforms the latest Improved Predict Priority Task Scheduling (IPPTS) and RE-18 scheduling methods in terms of makespan, scheduling length ratio, speedup, and the number of occurrences of better quality of schedules while maintaining the same time complexity.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System

Zhou

Liao

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

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