Priced-Based Fair Bandwidth Allocation for Networked Multimedia
The high demand of bandwidth from multimedia applications, specially video applications which consume the great majority of the Internet bandwidth, has caused a challenge for service providers and network operators. On the one hand, the allocation of bandwidth in a fair manner for multimedia users i...
Saved in:
Published in: | 2017 IEEE International Symposium on Multimedia (ISM) pp. 19 - 24 |
---|---|
Main Authors: | , , |
Format: | Conference Proceeding |
Language: | English |
Published: |
IEEE
01-12-2017
|
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | The high demand of bandwidth from multimedia applications, specially video applications which consume the great majority of the Internet bandwidth, has caused a challenge for service providers and network operators. On the one hand, the allocation of bandwidth in a fair manner for multimedia users is necessary, so that the total utility of all users is maximized for higher quality of experience. On the other hand, optimizing the utilization of network resources such as maximizing throughput is also important for network operators to reduce cost and/or maximize profits. These two requirements could potentially be conflicting; hence, achieving both at the same time is challenging, and the reason why very few previous efforts have targeted this problem. Examples include Traffic Management Using Multipath Protocol (TRUMP) and Logarithmic-Based Multipath Protocol (LBMP), both of which achieve good results but are not without shortcomings. In this paper, we propose a Price-Based Fair Bandwidth Allocation (PBFA) method by implementing an optimized sending rate adaptation technique and combining it with an intuitive investment method to optimize the feedback prices to achieve efficient and fair bandwidth allocation. The results of our performance tests, using different simulations under different network topologies, show that PBFA achieves improvements of as much as 90% in fairness, 207% in throughput, and 91% in utility compared to TRUMP. |
---|---|
DOI: | 10.1109/ISM.2017.14 |