Distributed cloud architecture which consists of many cloud computing-storage resources （CCSRs） distributed across a geographic large-area has been widely implemented. It has received significant attention from academia. However, little effort has been taken to examine changes in operating cost-structure brought by distributed cloud scheme, or explore how to reap economic benefits from its geo-diversity. To tackle such issue, this paper formulated cost optimizations for cloud platforms based on a generic expense model of distributed cloud, taking into account major components of operating cost. The best deployment schemes were obtained through numerical simulation. The optimal amount of edge CCSRs and their corresponding placements were found to be determined by the ratio among various overhead components. Both model study and numerical simulation shed light on practical deployment of distributed cloud with high cost-effectiveness.
AO Nai-xiangXU Ying-yingHUANG DanZHAO Yong-xiangCHEN Chang-jia
Characterizing the features of user churn is crucial to the sustainable development of peer-to-peer （P2P） systems where peers join and leave at any arbitrary time. This paper analyzes the user churn in a P2P downloading system named QQXuanfeng by using the fine-grained log analysis over 60 days. It shows that the online and offline duration is related to up （arrive） time and down （depart） time respectively. A continuous ON/OFF process, which exhibits the diurnal patterns of users, is simulated using the churn model. In particular, the dynamic departure rate is proposed to give insight into the distribution of online duration. Further more, considering the heterogeneity of users, we cluster users based on the similarity of redefined user availability. As an example of application of this model, a high availability overlay is constructed and evaluated based on the clustering.
Cloud download service, as a new application which downloads the requested content offiine and reserves it m cloud storage until users retrieve it, has recently become a trend attracting millions of users in China. In the face of the dilemma between the growth of download requests and the limitation of storage resource, the cloud servers have to design an efficient resource allocation scheme to enhance the utilization of storage as well as to satisfy users＇ needs like a short download time. When a user＇s churn behavior is considered as a Markov chain process, it is found that a proper allocation of download speed can optimize the storage resource utilization. Accordingly, two dynamic resource allocation schemes including a speed switching （SS） scheme and a speed increasing （SI） scheme are proposed. Both theoretical analysis and simulation results prove that our schemes can effectively reduce the consumption of storage resource and keep the download time short enough for a good user experience.
Based on observation of the growing mechanism in Twitter-like online social networks, an online social network （OSN） evolution model was proposed, a renewal mechanism for the old nodes and an accelerated growth mechanism was introduced for the new nodes, comparing with the native copying model. Topological characteristics of the generated networks, such as degree distribution, average shortest-path length and clustering coefficient, are analyzed and numerized. These properties are validated with some crawled datasets of real online social networks.
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.
