@inproceedings{570f0d47c89d4e56b7ca0e3ec2c642e7,
title = "Scheduling for energy efficiency and throughput maximization in a faulty cloud environment",
abstract = "There is an increasingly prominent trend in many big data scientific applications to move a substantial portion of or even all of the computing workflow executions to a cloud environment, which calls for an effective and efficient solution to optimize the performance of such workflow applications. We focus on computing workflows of streaming applications, and consider a faulty cloud environment where both nodes and links may fail at a certain probability. We tackle a triobjective optimization problem that reduces the total energy consumption while enforcing a bound on the throughput, and a constraint on the reliability. A layer-based mapping algorithm is proposed to schedule each subtask in the workflow to an appropriate node in the cloud in order to achieve three objectives (energy, throughput, and reliability) in a distributed manner. The proposed scheme automatically recomputes a mapping solution adapting to the network changes after a certain period. The performance superiority of the proposed scheme is illustrated by an extensive set of comparisons with other existing methods.",
keywords = "Cloud computing, Energy efficiency, Reliability, Throughput, Workflow scheduling",
author = "Huda Alrammah and Yi Gu and Chase Wu and Shiguang Ju",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017 ; Conference date: 15-12-2017 Through 17-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICPADS.2017.00079",
language = "English (US)",
series = "Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS",
publisher = "IEEE Computer Society",
pages = "561--569",
booktitle = "Proceedings - 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017",
address = "United States",
}