A framework for modeling and implementing QoS-aware Load Balancing solutions in WiFi Hotspots


Erturk M. A. , Vollero L., Aydin M. A. , Turna O. C. , Bernaschi M.

11th IEEE International Symposium on Wireless Communications Systems (ISWCS), Barcelona, Spain, 26 - 29 August 2014, pp.33-38 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/iswcs.2014.6933315
  • City: Barcelona
  • Country: Spain
  • Page Numbers: pp.33-38

Abstract

Access Point (AP) selection in WiFi hotspots is driven by stations and it is based on the measured strongest RSSI (Received Signal Strength Indicator) level: any station connects to the AP that provides the higher physical data rate. Although simple and effective in low crowded scenarios with low-medium traffic load, this strategy performs inefficiently when the number of mobile users is high and their distribution among APs is unbalanced, i.e. when network congestion becomes an issue. Load Balancing (LB) solutions aim at solving this problem by enforcing the connection of stations to the AP having either the smallest number of associated stations or the lowest traffic load. However, LB solutions do not account for traffic priorities or, when they consider them, they do not deal with the joint configuration of QoS (Quality of Service) and LB parameters. In this study we present a framework for modeling, analyzing and designing QoS-aware LB solutions. The proposed framework assumes that stations implement the Enhanced Distributed Channel Access (EDCA) mechanism of the IEEE 802.11e standard. Moreover, in order to make the framework concrete, we assume that the QoS goal is the weighted fair allocation of wireless resources. However, the framework is not restricted to this goal and can be easily extended in order to deal with a different cost function. The proposed framework is validated through simulations in a typical indoor LB scenario. The results show that the model is effective in capturing network performance and in designing LB solutions that account for traffic priorities and the configuration of QoS parameters.