Comparative analysis of scientific workflow scheduling in cloud environment

2017 ◽  
Author(s):  
M Shanmugasundaram ◽  
R Kumar ◽  
Digvijay Shinde ◽  
H M Kittur
Keyword(s):  
Comparative Analysis ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Cloud Environment

scholarly journals Research Scope and Tools for Workflow Scheduling in Cloud Environment

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1967-1968
Keyword(s):  
Comparative Analysis ◽  
Fault Tolerant ◽  
Data Migration ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Qos Parameters ◽  
Technical Issues ◽  
Execution Environment ◽  
Legal Policies ◽  
Made In

Workflow is a prototype that executes the behavior of scientific and engineering applications for which the sequence of tasks needs to be automated based on the input parameters specified. Difficulties arise for CSPs primarily during the execution environment due to its direct impact on various QoS parameters. Existing workflow scheduling techniques has research focus with dimensions that include undetermined demands, task failure,l delay cost, ambiguous deadlines, bandwidth, cache inclusion, scheduler policies, VM cycles, QoS impact, OS support, fault-tolerant and virtualization level. The comparative analysis made in this paper for workflow scheduling strategies and tools used in the cloud environment towards QoS parameters would have a greater impact on industry task automation which in turn provides the researchers to set their objectives and tools to be used in-order to bring forth new approaches and solutions. This paper can be extended considering the research scope given in the comparative analysis and the data size pertaining to each task in workflow and data migration issues which cannot be done without cost, legal policies and technical issues.

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>

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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