| Title |
A genetic algorithm-based job scheduling model for big data analytics
|
|---|---|
| Published in |
EURASIP Journal on Wireless Communications and Networking, June 2016
|
| DOI | 10.1186/s13638-016-0651-z |
| Pubmed ID | |
| Authors |
Qinghua Lu, Shanshan Li, Weishan Zhang, Lei Zhang |
| Abstract |
Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 75 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 31 | 40% |
| Student > Ph. D. Student | 11 | 14% |
| Researcher | 6 | 8% |
| Lecturer | 5 | 6% |
| Student > Bachelor | 4 | 5% |
| Other | 11 | 14% |
| Unknown | 9 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 29 | 38% |
| Social Sciences | 13 | 17% |
| Arts and Humanities | 7 | 9% |
| Engineering | 6 | 8% |
| Business, Management and Accounting | 4 | 5% |
| Other | 7 | 9% |
| Unknown | 11 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 August 2016.
All research outputs
#14,276,973
of 25,371,288 outputs
Outputs from EURASIP Journal on Wireless Communications and Networking
#260
of 549 outputs
Outputs of similar age
#186,634
of 367,717 outputs
Outputs of similar age from EURASIP Journal on Wireless Communications and Networking
#5
of 9 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 549 research outputs from this source. They receive a mean Attention Score of 2.4. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 367,717 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.