Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT

Zhou, Xiumin; Zhang, Gongxuan; Sun, Jin; Zhou, Jianjiang; Wei, Tongquan; Hu, Shiyan

doi:10.1016/j.future.2018.10.046

Cited by 218 publications

(86 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study evaluated the efficiency and effectiveness of existing cost-efficient task scheduling algorithms and proposed an algorithm based on the given heart beat datasets. For the existing algorithms, annotated as Baseline1 and Baseline 2, [40,41] have conducted their experimental results by exploiting datasets, as discussed above. However, it is convenient to evaluate the performance of all algorithms based on similar dataset functions when the algorithms run on the system for experiment purposes.…”

Section: Heartbeat Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Mastoi

Wah

Raj

et al. 2020

Sensors

View full text Add to dashboard Cite

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

show abstract

Section: Heartbeat Datasetsmentioning

confidence: 99%

“…Baseline 1: This study implement the heterogeneous earliest finish time [40] method to schedule autonomous healthcare application tasks to the heterogeneous clouds. This study processed all tasks through its different phases until completion.…”

mentioning

confidence: 99%

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Mastoi

Wah

Raj

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…A Deadline-constrained Cost Optimization Approaches was designed in [19] with the application of ant colony optimization for workflow scheduling in clouds. A fuzzy dominance sort based heterogeneous earliest-finish-time (FDHEFT) algorithm was presented in [20] to diminish cost and makespan for workflow scheduling in the cloud. An improved round robin (IRR) algorithm was presented in [21] for workflow scheduling in cloud.…”

Section: Related Workmentioning

confidence: 99%

Gene Optimized Deep Neural Round Robin Workflow Scheduling in Cloud

Shanmugasundaram¹,

Kumar²,

Kittur³

2019

IJACSA

View full text Add to dashboard Cite

Workflow scheduling is a key problem to be solved in the cloud to increases the quality of services. Few research works have been designed for performing workflow scheduling using different techniques. But, scheduling performance of existing techniques was not effective when considering a larger number of user tasks. Besides, the makespan of workflow scheduling was higher. In order to solve such limitations, Gene Optimized Deep Neural Round Robin Scheduling (GODNRRS) Technique is proposed. The designed GODNRRS Technique contains three layers namely input, hidden and output layer to efficiently perform workflow scheduling in the cloud. The GODNRRS Technique initially gets the number of user tasks as input in the input layer and forwards it to the hidden layer. After taking input, GODNRRS Technique initializes gene population with the assist of virtual machines in Amazon cloud server at the first hidden layer. Next, GODNRRS Technique determines fitness function for each virtual machine using their energy, memory, CPU time, bandwidth capacity at the second hidden layer. Afterward, GODNRRS Technique defines a weight for each virtual machine at the third hidden layer depends on their fitness function estimation. Consequently, GODNRRS Technique distributes the user tasks to optimal virtual machines according to their weight value at the fourth hidden layer in a cyclic manner. At last, the output layer renders the scheduled tasks result. Thus, GODNRRS Technique handles workflows in the cloud with improved scheduling efficiency and lower energy and makespan. The GODNRRS Technique conduct the experimental evaluation using metrics such as scheduling efficiency, makespan, and energy consumption with respect to a different number of user tasks from LIGO , Montage and cybershake real-time applications. The experimental result show that the GODNRRS Technique is able to increases the efficiency and also reduces the makespan of workflows scheduling in the cloud as compared to state-of-the-art works. 1000 88 89 92 99 85 89 90 95 87 90 91 98

show abstract

“…In the pack, each member has social dominant and consisting according to four different levels. The belowgiven figure shows the social hierarchy of the wolves which plays an important role in hunting [17]. follow and hunt and work as a team.…”

Section: Proposed Approachmentioning

confidence: 99%

Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

Khurana¹,

Singh²

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.

show abstract

Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT

Cited by 218 publications

References 17 publications

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Gene Optimized Deep Neural Round Robin Workflow Scheduling in Cloud

Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

Contact Info

Product

Resources

About