scholarly journals Workflow Scheduling to Minimize Data Movement Using Multi-constraint Graph Partitioning

2012 ◽  
Author(s):  
Masahiro Tanaka ◽  
Osamu Tatebe
Keyword(s):  
Graph Partitioning ◽  
Workflow Scheduling ◽  
Data Movement ◽  
Constraint Graph

scholarly journals Bandwidth-Aware Scheduling of Workflow Application on Multiple Grid Sites

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Harshadkumar B. Prajapati ◽  
Vipul A. Shah
Keyword(s):  
Open Source ◽  
Scheduling Algorithm ◽  
Scientific Workflows ◽  
Workflow Scheduling ◽  
Grid Environment ◽  
Data Movement ◽  
Workflow Mapping ◽  
Workflow Application ◽  
Algorithm Level ◽  
Better Than

Bandwidth-aware workflow scheduling is required to improve the performance of a workflow application in a multisite Grid environment, as the data movement cost between two low-bandwidth sites can adversely affect the makespan of the application. Pegasus WMS, an open-source and freely available WMS, cannot fully utilize its workflow mapping capability due to unavailability of integration of any bandwidth monitoring infrastructure in it. This paper develops the integration of Network Weather Service (NWS) in Pegasus WMS to enable the bandwidth-aware mapping of scientific workflows. Our work demonstrates the applicability of the integration of NWS by making existing Heft site-selector of Pegasus WMS bandwidth aware. Furthermore, this paper proposes and implements a new workflow scheduling algorithm—Level based Highest Input and Processing Weight First. The results of the performed experiments indicate that the bandwidth-aware workflow scheduling algorithms perform better than bandwidth-unaware algorithms: Random and Heft of Pegasus WMS. Moreover, our proposed workflow scheduling algorithm performs better than the bandwidth-aware Heft algorithms. Thus, the proposed bandwidth-aware workflow scheduling enhances capability of Pegasus WMS and can increase performance of workflow applications.

Parallel static and dynamic multi-constraint graph partitioning

2002 ◽  
Vol 14 (3) ◽  
pp. 219-240 ◽  
Author(s):  
Kirk Schloegel ◽  
George Karypis ◽  
Vipin Kumar
Keyword(s):  
Graph Partitioning ◽  
Constraint Graph

scholarly journals Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 177063-177081 ◽  
Author(s):  
Peerasak Wangsom ◽  
Kittichai Lavangnananda ◽  
Pascal Bouvry
Keyword(s):  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Data Movement ◽  
Multi Objective ◽  
Movement Awareness

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.

FINAL VERSION.pdf

2019 ◽  
Author(s):  
Nasir Saeed ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Graph Partitioning ◽  
Three Dimensional ◽  
Smart Objects ◽  
Localization Technique ◽  
Optical Internet ◽  
Spectral Graph ◽  
Accurate Localization ◽  
Spectral Graph Partitioning ◽  
Link Connectivity

<div>Localization is a fundamental task for optical internet</div><div>of underwater things (O-IoUT) to enable various applications</div><div>such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for OIoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor’s location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor’s location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable</div><div>sensors.</div>

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