Hybrid cloud peer to peer （P2P） system is widely used for content distribution by utilizing user＇s capabilities to relieve the cloud bandwidth pressure. However, as demands for large-size files grow rapidly, it is ...Hybrid cloud peer to peer （P2P） system is widely used for content distribution by utilizing user＇s capabilities to relieve the cloud bandwidth pressure. However, as demands for large-size files grow rapidly, it is a challenge to support high speed downloading experience simultaneously in different swarms with limited cloud bandwidth resource in such system. Therefore, it requires an optimized cloud bandwidth allocation to improve overall downloading experience of users. In this paper, we propose a system performance model which characterizes the relationship between cloud uploading bandwidth and user download speed. Based on the model, we study the cloud uploading bandwidth allocation, with the goal of optimizing user＇s quality of experience （QoE） that mainly depends on downloading rate of desired contents. Furthermore, to decrease the computation complexity, we put forward a heuristic algorithm to approximate the optimized solution. Simulation results show that our heuristic algorithm can obtain higher user＇s QoE as compared with two typical bandwidth allocation algorithms.展开更多
基金supported by the National Natural Science Foundation of China (61271199, 61301082)the Fundamental Research Funds for the Central Universities (W14JB00500)
文摘Hybrid cloud peer to peer （P2P） system is widely used for content distribution by utilizing user＇s capabilities to relieve the cloud bandwidth pressure. However, as demands for large-size files grow rapidly, it is a challenge to support high speed downloading experience simultaneously in different swarms with limited cloud bandwidth resource in such system. Therefore, it requires an optimized cloud bandwidth allocation to improve overall downloading experience of users. In this paper, we propose a system performance model which characterizes the relationship between cloud uploading bandwidth and user download speed. Based on the model, we study the cloud uploading bandwidth allocation, with the goal of optimizing user＇s quality of experience （QoE） that mainly depends on downloading rate of desired contents. Furthermore, to decrease the computation complexity, we put forward a heuristic algorithm to approximate the optimized solution. Simulation results show that our heuristic algorithm can obtain higher user＇s QoE as compared with two typical bandwidth allocation algorithms.
