A unique challenge in P2P network is that the peer dynamics (departure or failure) cause unavoidable disruption to the downstream peers. While many works have been dedicated to consider fault resilience in peer selection, little understanding is achieved regarding the solvability and solution complexity of this problem from the optimization perspective. To this end, we propose an optimization framework based on the generalized flow theory. Key concepts introduced by this framework include resilience factor, resilience index, and generalized throughput, which collectively model the peer resilience in a probabilistic measure. Under this framework, we divide the domain of optimal peer selection along several dimensions including network topology, overlay organization, and the definition of resilience factor and generalized flow. Within each subproblem, we focus on studying the problem complexity and finding optimal solutions. Simulation study is also performed to evaluate the effectiveness of our model and performance of the proposed algorithms.

Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for network routing in multi-radio multi-channel wireless mesh networks under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By analyzing the traces collected at wireless access points, the traffic estimation component predicts future traffic demand based on its historical value using time-series analysis, and represents the prediction result in two forms - mean value and statistical distribution. The optimal mesh network routing strategies then take these two forms of traffic demand estimations as inputs. In particular, two routing algorithms are proposed based on linear programming which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate traffic dynamics in mesh network routing, where both algorithms outperform the shortest path algorithm in about 80% of the test cases.

Recent theoretical and simulation-based studies have confirmed the tremendous benefit of peer-to-peer (P2P)communication at reducing the cost of running a VoD service.To date, very limited effort has been paid to validate the concept of peer-assisted VoD service, especially in terms of system implementation and service deployment. In this paper, we present the case study of a peer-assisted video-on-demand (VoD) system. We designed and developed Bit-Tube, a BitTorrent-compliant VoD system. By combining client/server and P2P downloading, it supports seamless transition across the spectrum from pure client-server mode to BitTorrent mode. Within this framework, we experiment with a series of piece picking policies to enhance BitTubepsilas support to video streaming and promote locality-aware P2P downloading. We evaluate our system over PlanetLab,which hosts the user-side component of the BitTube system and emulates the global-scale user requests to the VoD service.

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband Internet access. Network routing plays a critical role in determining the performance of a wireless mesh network. To study the best mesh network routing strategy which can maximize the network throughput while satisfying the fairness constraints, existing research proposes to formulate the mesh network routing problem as an optimization problem. These works usually make ideal assumptions such as known static traffic input. Whether they could be applied for practical use under the highly dynamic and uncertain traffic in wireless mesh network is still an open issue. The main objective of this paper is to understand the effects of traffic uncertainty in wireless mesh networks and consider these effects in throughput maximization routing. It identifies the appropriate optimization framework that could integrate the statistical user traffic demand model into a tractable throughput maximization problem. The trace-driven simulation study demonstrates that our algorithm can effectively incorporate the traffic demand uncertainty in routing optimization, and outperform the throughput maximization routing which only considers static traffic demand.

This paper presents HyMoNet, a peer-to-peer hybrid multicast overlay network for efficient live media streaming. The system comprises two parts: service infrastructure and user space. Service infrastructure contains the data stream servers manually deployed in the backbone network by the service provider. In user space, all users are dynamically classified into several tree structures, in which data is dispatched successively. With the user space management algorithm, the system can detect IP multicast service intelligently and allow users to join and to leave dynamically. NAT is an obstacle for widespread of P2P system, so a NAT pass strategy is designed to overcome this problem. The system guarantees the reliable data transmission which is friendly to TCP data flow. HyMoNet is now deployed in Internet providing real-time TV program streaming for the public.

Overlay multicast tree is widely used to support large-scale real-time multimedia applications. The scalability and the robustness are two key issues in the overlay structure design. In this paper, we propose a degree pre-reserved hierarchical tree for multimedia multicast, called DTree. It organizes the overlay tree in two hierarchies, and combines application-level multicast with IP multicast to achieve a low delay data delivery. A degree pre-reserved mechanism is designed in DTree, which greatly shortens the time to resume the data flow, and achieve a fast recovery. Our simulation results show that DTree has a better delivery performance, compared with other overlay tree. It is quite responsive to the changes in the tree, and almost 3 times faster than other recovery strategies in some cases

The ecological footprint analysis is a concise quantitative method of the natural capital consumption measurement by tracking the footprint of energy sources and resources consumption in a region. This approach provides an estimate for the necessary land area to sustain current consumption levels of resources within a given population, and compares with the area of biologically productive land available. It can judge if the consumption pattern is sustainable. This paper, applying the methods of questionnaire and remote sensing image analysis, seeks to explore a modified method for ecological footprint analysis. The new method applies questionnaire in ecological footprint and does remote sensing image analysis in ecological capacity. It judges the ecological status by comparing the above results. In addition, this scheme predicts the ecological footprint based on the consumption and population models and the ecological capacity by Cellular Automata. Furthermore, future ecological status can be judged by comparing the ecological footprint with the ecological capacity