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There are several scheduling algorithms for Voice over IP (VoIP) services in IEEE 802.16e systems, such as unsolicited grant service (UGS), real-time polling service (rtPS), and extended real-time polling service (ertPS). The ertPS is a new scheduling algorithm for VoIP services with variable data rates and silence suppression, and this algorithm is recently proposed and accepted in the IEEE 802.16e standard. In this paper, we analyze and discuss the performance of the scheduling algorithms recommended in IEEE 802.16e systems including the ertPS algorithm. Through the analysis of resource utilization efficiency and VoIP capacity, we show that the UGS and rtPS algorithms have some problems, which are the waste of uplink resources in the UGS algorithm, and additional access delay and MAC overhead due to bandwidth request process in the rtPS algorithm, to support the VoIP services. In addition, for analysis of VoIP capacity, we utilize OPNET simulation, and show that the ertPS algorithm can support more 21% and 35% voice users compared with the UGS and rtPS algorithms, respectively.

Several structured peer-to-peer networks have been created to solve the scalability problem of previous peer-to-peer systems such as Gnutella and Napster. These peer-to-peer networks which support distributed hash table functionality construct a sort of structured overlay network, which can cause a topology mismatch between the overlay and the underlying physical network. To solve this mismatch problem, we propose a topology-aware hierarchical overlay framework for DHTs. The hierarchical approach for the overlay is based on the concept that the underlying global Internet is also a hierarchical architecture, that is, a network of networks. This hierarchical approach for the overlay puts forth two benefits: finding data in a physically near place with a high probability, and smaller lookup time. Our hierarchical overlay framework is different from other hierarchical architecture systems in a sense that it provides a specific selforganizing grouping algorithm. Our additional optimization schemes complete the basic algorithm which constructs a hierarchical structure without any central control.

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