scholarly journals A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

2018 ◽  
Vol 8 (4) ◽  
pp. 538 ◽  
Author(s):  
Nazia Anwar ◽  
Huifang Deng
Keyword(s):  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Hybrid Metaheuristic ◽  
Multi Objective

scholarly journals A Hybrid Algorithm for Multi-Objective Scientific Workflow Scheduling in IaaS Cloud

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125783-125795 ◽  
Author(s):  
Yongqiang Gao ◽  
Shuyun Zhang ◽  
Jiantao Zhou
Keyword(s):  
Hybrid Algorithm ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Multi Objective

Makespan–Cost–Reliability-Optimized Workflow Scheduling Using Evolutionary Techniques in Clouds

2019 ◽  
Vol 29 (10) ◽  
pp. 2050167
Author(s):  
Xiumin Zhou ◽  
Gongxuan Zhang ◽  
Tian Wang ◽  
Mingyue Zhang ◽  
Xiji Wang ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
System Reliability ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Workflow Systems ◽  
Multi Objective ◽  
Qos Parameters ◽  
Crossover And Mutation

Most popular scientific workflow systems can now support the deployment of tasks to the cloud. The execution of workflow on cloud has become a multi-objective scheduling in order to meet the needs of users in many aspects. Cost and makespan are considered to be the two most important objects. In addition to these, there are some other Quality-of-Service (QoS) parameters including system reliability, energy consumption and so on. Here, we focus on three objectives: cost, makespan and system reliability. In this paper, we propose a Multi-objective Evolutionary Algorithm on the Cloud (MEAC). In the algorithm, we design some novel schemes including problem-specific encoding and also evolutionary operations, such as crossover and mutation. Simulations on real-world and random workflows are conducted and the results show that MEAC can get on average about 5% higher hypervolume value than some other workflow scheduling algorithms.

scholarly journals Scheduling Scientific Workflow Using Multi-Objective Algorithm With Fuzzy Resource Utilization in Multi-Cloud Environment

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 24309-24322 ◽  
Author(s):  
Mazen Farid ◽  
Rohaya Latip ◽  
Masnida Hussin ◽  
Nor Asilah Wati Abdul Hamid
Keyword(s):  
Resource Utilization ◽  
Scientific Workflow ◽  
Cloud Environment ◽  
Multi Objective ◽  
Multi Cloud

Scientific workflow scheduling based on data transformation graph for remote sensing application

2021 ◽  
Author(s):  
Zhuojing tian ◽  
Zhenchun huang ◽  
Yinong zhang ◽  
Yanwei zhao ◽  
En fu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Data Transformation ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Sensing Applications ◽  
Random Algorithm ◽  
Remote Sensing Applications ◽  
Sensing Application

<p><strong>Abstract: </strong>As the amount of data and computation of scientific workflow applications continue to grow, distributed and heterogeneous computing infrastructures such as inter-cloud environments provide this type of application with a great number of computing resources to meet corresponding needs. In the inter-cloud environment, how to effectively map tasks to cloud service providers to meet QoS(quality of service) constraints based on user requirements has become an important research direction. Remote sensing applications need to process terabytes of data each time, however frequent and huge data transmission across the cloud will bring huge performance bottlenecks for execution, and seriously affect the result of QoS constraints such as makespan and cost. Using a data transformation graph(DTG) to study the data transfer process of global drought detection application, the specific optimization strategy is obtained based on the characteristics of application and environment, and according to this, one inter-cloud workflow scheduling method based on genetic algorithm is proposed, under the condition of satisfying the user’s QoS constraints, the makespan the cost can be minimized. The experimental results show that compared with the standard genetic algorithm, random algorithm, random algorithm, and round-robin algorithm, the optimized genetic algorithm can greatly improve the scheduling performance of data computation-intensive scientific workflows such as remote sensing applications and reduce the impact of performance bottlenecks.</p><p><strong>Keywords: </strong>scientific workflow scheduling; inter-cloud environment; remote sensing application; data transformation graph;</p>

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