The explosion of mobile content and usage imposes enormous pressures on mobile communication networks. To reduce content delivery latency and to ease the burden on network bottlenecks (e.g., backhaul networks), besides upgrading the infrastructures, it is promising to cache popular contents at the edge-storage on BSs (Base Stations), APs (Access Points) or other third-party devices associated with BSs and APs, which have been widely deployed in mobile networks. Then it is a challenge here to effectively match such demands of CPs (Content Providers) and the supplies of edge-storage owners because of the complexity caused by the two-fold matching requirements on both coverage and quantity combined with the multi-buyer multi-seller scenario and the divisibility of heterogeneous edge-storages. In this paper, we propose ESTRA (Edge Storage TRading Auction) mechanism to tackle such a challenge. By proposing a region-based model for edge-storage trading, we design a demand cover mechanism to transfer subscriber coverage demands into edge-storage bundle demands and design a truthful, weakly budget balanced and individually rational auction mechanism under the constraints of enabling multi-unit asks and bundle bids. Our theoretical analysis proves the economic robustness, and the simulation result shows that ESTRA achieves 74%-91% of the maximum social welfare and maintains the sustainability of the trading platform through a proper distribution of social welfare.

Recent years have witnessed the great popularity of dynamic spectrum access networks. Such an approach is adopted between three players: government, Internet Service Providers (ISPs) and end-users. ISPs need to purchase spectrum from the government before subletting it to end-users, but currently most researches focus on the subletting process and ignore the purchasing process. In this paper, we try to investigate the game between government and ISPs in spectrum access networks. In this framework, the former aims to optimize user experience yet the later want to maximize their own profits. Such a conflict of interests introduces significant challenges to ensure end-user's performance and thus leads to a severe bottleneck to the spectrum access networks. Inspired by cooperative trends among users, we proposed a novel Channel Allocation model based on Teamwork (CAT). This approach considers both ISP's respective bands and end-user's experience and enables a smart profit sharing algorithm to address the problem. The evaluation results indicate that CAT improves the overall social welfare by about 30% than the Vickrey Clarke Groves (VCG) mechanism and obtains higher stability.

Recently, the performance of mobile data networks has been evaluated from many aspects, e.g., TCP/IP protocols, comparison with WiFi or even satellite communication, under different movements within a metropolis area. Nevertheless, the result is still unknown in high-speed mobility scenarios and in a scale that crosses different metropolis and geographic areas. To fill in this blank, we carry out a comprehensive measurement study on the performance of mobile data networks under high-speed mobility, i.e., 300 km/h or above. Such speed is the current de facto standard of the China Railway High speed (CRH) network, the largest commercial high-speed railway network in the world so far. We first present an overview on the TCP performance over LTE networks. We observe that decent throughput may exist under high-speed mobility. However, comparing to the stationary and driving (100 km/h) scenarios, the throughput and RTT not only are worse, but also have a large variance. We then take an in-depth investigation into two key factors affecting the performance, i.e., The wireless channel and handoff. We believe our study on these factors is useful not only for TCP, but also for other upper-layer protocols.

Peer-to-Peer (P2P) applications have witnessed an increasing popularity in recent years, which brings new challenges to network management and traffic engineering (TE). As basic input information, P2P traffic matrices are of significant importance for TE. Because of the excessively high cost of direct measurement, many studies aim to model and estimate general traffic matrices, but few focus on P2P traffic matrices. In this paper, we propose a model to estimate P2P traffic matrices in operational networks. Important factors are considered, including the number of peers, the localization ratio of P2P traffic, and the network distance. Here, the distance can be measured with AS hop counts or geographic distance. To validate our model, we evaluate its performance using traffic traces collected from both the real P2P video-on-demand (VoD) and file-sharing applications. Evaluation results show that the proposed model outperforms the other two typical models for the estimation of the general traffic matrices in several metrics, including spatial and temporal estimation errors, stability in the cases of oscillating and dynamic flows, and estimation bias. To the best of our knowledge, this is the first research on P2P traffic matrices estimation. P2P traffic matrices, derived from the model, can be applied to P2P traffic optimization and other TE fields.

Peer-to-Peer (P2P) technology has been promoting the development of Internet applications, like Video on Demand (VoD) and file sharing. However, under the traditional pricing mechanism, the fact that most P2P traffic flows among peers can dramatically decrease the profit of ISPs, who may take actions against P2P and impede the adoption of P2P-assisted applications. So far, there is no proper profit distribution mechanism to solve this problem. In this paper, we develop a mathematical framework to analyze such economic issues. Inspired by the idea from cooperative game theory, we propose a cooperative profit-distribution model based on Nash Bargaining Solution (NBS), in which both eyeball ISPs and Peer-assisted Content Providers (PCPs) form coalitions and compute a fair Pareto point to determine profit distribution. Moreover, we design a fair and feasible mechanism for profit distribution within each coalition and give a model to discuss the potential competition among ISPs. We show that such a cooperative method not only guarantees the fair profit distribution among network participants, but also improves the economic efficiency of the network system; and the potential competition among ISPs will make the network more efficient. This paper systematically studies solutions to unbalanced profit distribution caused by P2P and presents a feasible cooperative method to increase and fairly distribute the profit.

The P2P-assisted video-on-demand (P2P VoD) service has achieved tremendous success among the Internet users. There are three core strategies in the P2P VoD system: the piece selection policy, the peer selection policy as well as the replica management policy. Different from the existing research works that only consider single policy optimization, we for the first time study the existing P2P VoD policies by using a simulation framework to understand the performance of different policy compositions. The simulation results indicate that when the bandwidth and storage resources are limited in the P2P VoD system, the composition of the sequential piece selection policy, the cascading peer selection policy and the proportional replica management policy has the best performance among all different policy compositions. However, when the bandwidth and storage resources are sufficient in the P2P VoD system, there will be little difference between different choices.