NDLI: A Weighted K-Means Clustering Based Co-scheduling Strategy towards Efficient Execution of Scientific Workflows in Collaborative Cloud Environments

Content Provider	IEEE Xplore Digital Library
Author	Kefeng Deng Lingmei Kong Junqiang Song Kaijun Ren Dong Yuan
Copyright Year	2011
Abstract	Due to the advantages of cost-effectiveness, on-demand resource provision and easy for sharing, cloud computing has grown in popularity with research community for deploying scientific applications such as workflows. When such interest continues growing and workflows are widely performed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place the application data across globally distributed data centers and schedule tasks according to the data layout to reduce both the latency and make span for workflow execution. In this paper, by utilising dependencies among datasets and tasks, we propose an efficient data and task co scheduling strategy that can place input datasets in a load balance way and meanwhile group the mostly related datasets and tasks together. We build a simulation environment on Tianhe supercomputer to evaluate the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve workflows performance while reducing the total volume of data transfer across data centers.
Starting Page	547
Ending Page	554
File Size	1139189
Page Count	8
File Format	PDF
ISBN	9781467300063
DOI	10.1109/DASC.2011.102
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cloud computing scientific workflow Runtime Computational modeling data placement Collaboration Distributed databases Clustering algorithms coscheduling Data models task scheduling cloud computing
Content Type	Text
Resource Type	Article

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