Huandong Wang

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.

Leveraging software-defined networking for security policy enforcement

Network operators employ a variety of security policies for protecting the data and services. However, deploying these policies in traditional network is complicated and security vulnerable due to the distributed network control and lack of standard control protocol. Software-defined network provides an ideal paradigm to address these challenges by separating control plane and data plane, and exploiting the logically centralized control. In this paper, we focus on taking the advantage of software-defined networking for security policies enforcement. We propose a two layer OpenFlow switch topology designed to implement security policies, which considers the limitation of flow table size in a single switch, the complexity of configuring security policies to these switches, and load balance among these switches. Furthermore, we introduce a safe way to update the configuration of these switches one by one for better load balance when traffic distribution changes. Specifically, we model the update process as a path in a graph, in which each node represents a security policy satisfied configuration, and each edge represents a single step of safely update. Based on this model, we design a heuristic algorithm to find an optimal update path in real time. Simulations of the update scheme show that our proposed algorithm is effective and robust under an extensive range of conditions.

Virtual machine migration planning in software-defined networks

Live migration is a key technique for virtual machine (VM) management in data center networks, which enables flexibility in resource optimization, fault tolerance, and load balancing. Despite its usefulness, the live migration still introduces performance degradations during the migration process. Thus, there has been continuous efforts in reducing the migration time in order to minimize the impact. From the networks perspective, the migration time is determined by the amount of data to be migrated and the available bandwidth used for such transfer. In this paper, we examine the problem of how to schedule the migrations and how to allocate network resources for migration when multiple VMs need to be migrated at the same time. We consider the problem in the Software-defined Network (SDN) context since it provides flexible control on routing. More specifically, we propose a method that computes the optimal migration sequence and network bandwidth used for each migration. We formulate this problem as a mixed integer programming, which is NP-hard. To make it computationally feasible for large scale data centers, we propose an approximation scheme via linear approximation plus fully polynomial time approximation, and obtain its theoretical performance bound. Through extensive simulations, we demonstrate that our fully polynomial time approximation (FPTA) algorithm has a good performance compared with the optimal solution and two state-of-the-art algorithms. That is, our proposed FPTA algorithm approaches to the optimal solution with less than 10% variation and much less computation time. Meanwhile, it reduces the total migration time and the service downtime by up to 40% and 20% compared with the state-of-the-art algorithms, respectively.

Saving Energy in Partially Deployed Software Defined Networks

As power consumption of the Internet has been growing quickly in recent years, saving energy has become an important problem of networking research, for which the most promising solution is to find the minimum-power network subsets and shut down other unnecessary network devices and links to satisfy changing traffic loads. However, in traditional networks, it is difficult to implement a coordinated strategy among the network devices due to their distributed network control. On the other hand, the new networking paradigm-software defined network (SDN) provides us an efficient way of having a centralized controller with a global network view to control the power states. As an emerging technology, SDNs usually coexist with traditional networks at present. Therefore, we need to investigate how to save energy in partially deployed SDNs. In this paper, we formulate the optimization problem of finding minimum-power network subsets in partially deployed SDNs. After proving the problem is NP-hard, we propose a heuristic solution to approach its exact solution. Through extensive simulations, we demonstrate that our heuristic algorithm has a good performance; that is, on average we can save about 50 percent of total power consumption in the full SDN, having a distance less than 5 percent of the exact solutions power consumption. Moreover, it also achieves good performance in the partially deployed SDN, on average saving about 40 percent of the total power consumption when there are about 60 percent SDN nodes in the network. Meanwhile, it runs significantly faster than a general linear solver of this problem, by reducing the computation time of the network containing hundreds of nodes by 100× at least.

Characterizing the Spatio-Temporal Inhomogeneity of Mobile Traffic in Large-scale Cellular Data Networks

As the volume of mobile traffic has been growing quickly in recent years, reducing the congestion of mobile networks has become an important problem of networking research. Researchers found out that the inhomogeneity in the spatio-temporal distribution of the data traffic leads to extremely insufficient utilization of network resources. Thus, it is important to fundamentally understand this distribution to help us make better resource planning or introduce new management tools such as time-dependent pricing to reduce the congestion. However, due to the requirement of a large dataset, a detailed, radical and credible network-wide study for the spatio-temporal distribution of mobile traffic is still lacking. In this work, we conduct such a measurement study. Base on a large-scale data set obtained from 380,000 base stations in Shanghai spanning over one month, we quantitatively characterize the spatio-temporal distribution of mobile traffic and present a detailed visualized analysis. Furthermore, on the basis of quantitative analysis, we find that the mobile traffic loads uniformly follow a trimodal distribution, which is the combination of compound-exponential, power-law and exponential distributions, in terms of both spatial and temporal dimension. Extensive results show that our model is with accuracy over 99%, which provides fundamental and credible guidelines for the practical solutions of the issues in mobile traffic operations.

Self-monitoring of fracture and strain in titanium carbide reinforced silicon nitride ceramics

Si3N4 Ceramic Composites with TiC powder have been fabricated by gas-pressure sintering and their electrical conductivity has been investigated. The ceramic composites with different electrical resistivity consist of Si3N4 powder as an insulating matrix, and TiC as electrically conductive additive. Under tensile loading or compressive unloading, the ΔR/R of TiC/Si3N4 composites reversibly increased. Under compressive loading, the ΔR/R decreased gradually with the increasing of loading up to fracture. The results suggest the possibility of self-monitoring fractures and strains in the composites under tensile and compressive loading.

Co-location social networks: linking the physical world and cyberspace

Various dedicated web services in the cyberspace, e.g., social networks, e-commerce, and instant communications, play a significant role in peoples daily-life. Billions of people around the world access them through multiple online identifiers (IDs), and interact with each other in both the cyberspace and the physical world. These two kinds of interactions are highly relevant to each other. In order to link between the cyberspace and the physical world, we propose a new type of social network, i.e., co-location social network (CLSN). A CLSN contains online IDs describing peoples online presence and offline interactions when people come across each other. By analyzing real data collected from a mainstream ISP in China, which contains 32.7 million IDs across most popular web services, we build a large-scale CLSN, and evaluate its unique properties. The results verify that the CLSN is quite different from existing online and offline social networks in terms of different classic graph metrics. This paper is the first research to study CLSN at scale and paves the way for future studies of this new type of social network.