@inproceedings{
 title = {Task Scheduling in Big Data - Review, Research Challenges, and Prospects},
 type = {inproceedings},
 year = {2018},
 keywords = {Big Data,Dataflow,MapReduce,Static and Dynamic Task Scheduling,Task Scheduling Model,Twister2},
 pages = {165-173},
 month = {8},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 day = {20},
 id = {1caf2685-0aa3-3c64-a879-97271e93d71d},
 created = {2019-10-01T17:21:01.239Z},
 accessed = {2019-09-04},
 file_attached = {true},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2020-05-11T14:43:31.443Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Govindarajan2018},
 private_publication = {false},
 abstract = {—In a Big data computing, the processing of data requires a large amount of CPU cycles and network bandwidth and disk I/O. Dataflow is a programming model for processing Big data which consists of tasks organized in a graph structure. Scheduling these tasks is one of the key active research areas which mainly aims to place the tasks on available resources. It is essential to effectively schedule the tasks, in a manner that minimizes task completion time and increases utilization of resources. In recent years, various researchers have discussed and presented different task scheduling algorithms. In this research study, we have investigated the state-of-art of various types of task scheduling algorithms, scheduling considerations for batch and streaming processing, and task scheduling algorithms in the well-known open-source big data platforms. Furthermore, this study proposes a new task scheduling system to alleviate the problems persists in the existing task scheduling for big data.},
 bibtype = {inproceedings},
 author = {Govindarajan, Kannan and Kamburugamuve, Supun and Wickramasinghe, Pulasthi and Abeykoon, Vibhatha and Fox, Geoffrey},
 doi = {10.1109/ICoAC.2017.8441494},
 booktitle = {2017 9th International Conference on Advanced Computing, ICoAC 2017}
}

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