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Scientific Programming
Volume 2017 (2017), Article ID 9749581, 12 pages
https://doi.org/10.1155/2017/9749581
Research Article

Fault-Aware Resource Allocation for Heterogeneous Data Sources with Multipath Routing

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Long Teng Road, Shanghai 201620, China

Correspondence should be addressed to Xiaomei Zhang; moc.liamtoh@uncc_mxz

Received 1 March 2017; Accepted 24 April 2017; Published 17 September 2017

Academic Editor: Chi-Hung Chi

Copyright © 2017 Xiaomei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the rapid development of cloud computing and big data, diverse types of traffic generated from heterogeneous data sources are delivered throughout communication networks, which consist of various node kinds such as digital sensors and smart actuators, and different applications. Due to the shared medium, communication networks are vulnerable to misbehaving nodes, and it is a crucial aspect to maintain an acceptable level of service degradation. This paper studies the fault-aware resource allocation problem by exploiting multipath routing and dynamic rate assignment for heterogeneous sources. We estimate the impacts of faults and formulate the resource allocation as a lossy network flow optimization problem based on these estimates. The traditional flow optimization solutions focus on homogeneous traffic. In our work, we model the performance of heterogeneous applications as a relaxed utility function and develop an effective utility framework of rate control for heterogeneous sources with multipath routing in presence of misbehaving nodes. We design a distributed algorithm to decide the routing strategy and obtain the rate assignments on the available paths in a lossy utility fair manner. Extensive performance evaluations corroborate the significant performance of our algorithm in effective utility and utility fairness in the presence of misbehaving nodes.