TY - GEN
T1 - ExBox
T2 - 12th ACM Conference on Emerging Networking Experiments and Technologies, ACM CoNEXT 2016
AU - Chakraborty, Ayon
AU - Sanadhya, Shruti
AU - Dasy, Samir R.
AU - Kim, Dongho
AU - Kim, Kyu Han
PY - 2016/12/6
Y1 - 2016/12/6
N2 - Enterprise wireless networks face significant challenges to deliver Quality-of-Experience (QoE) with the variety of mobile applications. One of the fundamental challenges is that the traditional definition of network capacity (often defined as throughput capacity) is not sufficient to reflect applications' requirements in wireless networks. In this paper, we propose to rethink the network capacity of wireless networks to better incorporate QoE. Specifically, we first propose a novel concept of an Experiential Capacity Region (ExCR) for wireless networks. ExCR is defined as a set of simultaneous application flows whose QoE requirements can be satisfied by the network. Next, we present the infrastructure based ExBox system that measures perapplication QoE metrics and determines the ExCR for wireless networks to better serve a set of mobile application flows. In its core, ExBox employs light-weight machine learning techniques that are tailored for dynamic wireless environments. Through both large-scale simulations and extensive real-life experiments on WiFi and LTE networks, we show that ExBox delivers QoE in admission control decision with a precision of 0.8 - 0.9, even when clients experience diverse channel quality. Moreover, ExBox quickly adapts to changing network environments without much overhead.
AB - Enterprise wireless networks face significant challenges to deliver Quality-of-Experience (QoE) with the variety of mobile applications. One of the fundamental challenges is that the traditional definition of network capacity (often defined as throughput capacity) is not sufficient to reflect applications' requirements in wireless networks. In this paper, we propose to rethink the network capacity of wireless networks to better incorporate QoE. Specifically, we first propose a novel concept of an Experiential Capacity Region (ExCR) for wireless networks. ExCR is defined as a set of simultaneous application flows whose QoE requirements can be satisfied by the network. Next, we present the infrastructure based ExBox system that measures perapplication QoE metrics and determines the ExCR for wireless networks to better serve a set of mobile application flows. In its core, ExBox employs light-weight machine learning techniques that are tailored for dynamic wireless environments. Through both large-scale simulations and extensive real-life experiments on WiFi and LTE networks, we show that ExBox delivers QoE in admission control decision with a precision of 0.8 - 0.9, even when clients experience diverse channel quality. Moreover, ExBox quickly adapts to changing network environments without much overhead.
UR - https://www.scopus.com/pages/publications/85009831544
U2 - 10.1145/2999572.2999597
DO - 10.1145/2999572.2999597
M3 - Conference contribution
AN - SCOPUS:85009831544
T3 - CoNEXT 2016 - Proceedings of the 12th International Conference on Emerging Networking EXperiments and Technologies
SP - 145
EP - 159
BT - CoNEXT 2016 - Proceedings of the 12th International Conference on Emerging Networking EXperiments and Technologies
PB - Association for Computing Machinery, Inc
Y2 - 12 December 2016 through 15 December 2016
ER -