Title
A Highly-Accurate and Low-Overhead Prediction Model for Transfer Throughput Optimization
Abstract
An important bottleneck for data-intensive scalable computing systems is efficient utilization of the network links that connect the collaborating institutions with their remote partners, data sources, and computational sites. To alleviate this bottleneck, we propose an application-layer throughput optimization model based on parallel stream number prediction. This new model extends our two previous models (Partial C-order and Full Second-order) to achieve higher accuracy and lower overhead predictions. Our new model, called Full C-order, outperforms both of our previous models as well as the most relevant model by others, the Partial Second-order, in terms of both accuracy and efficiency. We test and compare these four models on emulated testbeds and on production environments using a wide variety of data set sizes, RTT, and bandwidth combinations. Our comprehensive experiments confirm the superiority of our new model to the other three models.
Year
DOI
Venue
2012
10.1109/SC.Companion.2012.109
Cluster Computing
Keywords
DocType
Volume
Big-data,Throughput optimization,High-accuracy,Low-overhead,Parallel streams,Prediction
Conference
18
Issue
ISBN
Citations 
1
978-1-4673-6218-4
16
PageRank 
References 
Authors
0.77
14
3
Name
Order
Citations
PageRank
Jangyoung Kim1352.28
Esma Yildirim21145.91
Kosar, Tevfik361448.67