Erran Li

SoftRAN: software defined radio access network

An important piece of the cellular network infrastructure is the radio access network (RAN) that provides wide-area wireless connectivity to mobile devices. The fundamental problem the RAN solves is figuring out how best to use and manage limited spectrum to achieve this connectivity. In a dense wireless deployment with mobile nodes and limited spectrum, it becomes a difficult task to allocate radio resources, implement handovers, manage interference, balance load between cells, etc. We argue that LTEs current distributed control plane is suboptimal in achieving the above objective. We propose SoftRAN, a fundamental rethink of the radio access layer. SoftRAN is a software defined centralized control plane for radio access networks that abstracts all base stations in a local geographical area as a virtual big-base station comprised of a central controller and radio elements (individual physical base stations). In defining such an architecture, we create a framework through which a local geographical network can effectively perform load balancing and interference management, as well as maximize throughput, global utility, or any other objective.

SoftCell: scalable and flexible cellular core network architecture

Cellular core networks suffer from inflexible and expensive equipment, as well as from complex control-plane protocols. To address these challenges, we present SoftCell, a scalable architecture that supports fine-grained policies for mobile devices in cellular core networks, using commodity switches and servers. SoftCell enables operators to realize high-level service policies that direct traffic through sequences of middleboxes based on subscriber attributes and applications. To minimize the size of the forwarding tables, SoftCell aggregates traffic along multiple dimensions---the service policy, the base station, and the mobile device---at different switches in the network. Since most traffic originates from mobile devices, SoftCell performs fine-grained packet classification at the access switches, next to the base stations, where software switches can easily handle the state and bandwidth requirements. SoftCell guarantees that packets belonging to the same connection traverse the same sequence of middleboxes in both directions, even in the presence of mobility. We demonstrate that SoftCell improves the scalability and flexibility of cellular core networks by analyzing real LTE workloads, performing micro-benchmarks on our prototype controller as well as large-scale simulations.

Toward Software-Defined Cellular Networks

Existing cellular networks suffer from inflexible and expensive equipment, complex control-plane protocols, and vendor-specific configuration interfaces. In this position paper, we argue that software defined networking (SDN) can simplify the design and management of cellular data networks, while enabling new services. However, supporting many subscribers, frequent mobility, fine-grained measurement and control, and real-time adaptation introduces new scalability challenges that future SDN architectures should address. As a first step, we propose extensions to controller platforms, switches, and base stations to enable controller applications to (i) express high-level policies based on subscriber attributes, rather than addresses and locations, (ii) apply real-time, fine-grained control through local agents on the switches, (iii)perform deep packet inspection and header compression on packets, and (iv)remotely manage shares of base-station resources.

Locating internet bottlenecks: algorithms, measurements, and implications

The ability to locate network bottlenecks along end-to-end paths on the Internet is of great interest to both network operators and researchers. For example, knowing where bottleneck links are, network operators can apply traffic engineering either at the interdomain or intradomain level to improve routing. Existing tools either fail to identify the location of bottlenecks, or generate a large amount of probing packets. In addition, they often require access to both end points. In this paper we present Pathneck, a tool that allows end users to efficiently and accurately locate the bottleneck link on an Internet path. Pathneck is based on a novel probing technique called Recursive Packet Train (RPT) and does not require access to the destination. We evaluate Pathneck using wide area Internet experiments and trace-driven emulation. In addition, we present the results of an extensive study on bottlenecks in the Internet using carefully selected, geographically diverse probing sources and destinations. We found that Pathneck can successfully detect bottlenecks for almost 80% of the Internet paths we probed. We also report our success in using the bottleneck location and bandwidth bounds provided by Pathneck to infer bottlenecks and to avoid bottlenecks in multihoming and overlay routing.

IP-based access network infrastructure for next-generation wireless data networks

Next-generation wireless network standards are currently being defined. The access network architectures have several specialized components tailored for their respective wireless link technologies, even though the services provided by these different wireless networks are fairly similar. We propose a homogeneous IP-based network as a common access network for the different wireless technologies. The IP-based access network uses the Internet standard, Mobile IP, to support macro-mobility of mobile hosts, and HAWAII to support micro-mobility and paging functionality of current wireless networks. We also illustrate how the proposed IP-based solution can interwork with existing infrastructure so that deployment can be incremental.

CloudStream: Delivering high-quality streaming videos through a cloud-based SVC proxy

Existing media providers such as YouTube and Hulu deliver videos by turning it into a progressive download. This can result in frequent video freezes under varying network dynamics. In this paper, we present CloudStream: a cloud-based video proxy that can deliver high-quality streaming videos by transcoding the original video in real time to a scalable codec which allows streaming adaptation to network dynamics. The key is a multi-level transcoding parallelization framework with two mapping options (Hallsh-based Mapping and Lateness-first Mapping) that optimize transcoding speed and reduce the transcoding jitters while preserving the encoded video quality. We evaluate the performance of CloudStream on our campus cloud testbed.

Advancing the state of mobile cloud computing

The capabilities of mobile devices have been improving very quickly in terms of computing power, storage, feature support, and developed applications. However, these mobile applications are still intrinsically limited by a relative lack of bandwidth, computing power, and energy compared to their tethered counterparts. Cloud computing offers abundant computing power that can be tapped easily. Apple iCloud and Amazon Silk browser are two recent mobile applications that leverage the cloud. In this paper, we systematically explore the fundamental research questions when combining mobile and cloud computing. We will highlight some of the challenges we face and some of the solutions we are pursuing.

Market sharing games applied to content distribution in ad-hoc networks

In third-generation (3G) wireless data networks, repeated requests for popular data items can exacerbate the already scarce wireless spectrum. In this paper, we propose an architectural and protocol framework that allows 3G service providers to host efficient content distribution services. We offload the spectrum intensive task of content distribution to an ad hoc network. Less mobile users (resident subscribers) are provided incentives to cache popular data items, while mobile users (transit subscribers) access this data from resident subscribers through the ad hoc network. Since the participants of this data distribution network act as selfish agents, they may collude to maximize their individual payoff. Our proposed protocol discourages potential collusion scenarios. In this architecture, the goal (social function) of the 3G service provider is to have the selfishly motivated resident subscribers service as many data requests as possible. However, the choice of which set of items to cache is left to the individual user. The caching activity among the different users can be modeled as a market sharing game. In this work, we study the Nash equilibria of market sharing games and the performance of such equilibria in terms of a social function. These games are a special case of congestion games that have been studied in the economics literature. In particular, pure strategy Nash equilibria for this set of games exist. We give a polynomial-time algorithm to find a pure strategy Nash equilibrium for a special case, while it is NP-hard to do so in the general case. As for the performance of Nash equilibria, we show that the price of anarchy-the worst case ratio between the social function at any Nash equilibrium and at the social optimum-can be upper bounded by a factor of 2. When the popularity follows a Zipf distribution, the price of anarchy is bounded by 1.45 in the special case where caching any item has a positive reward for all players. We prove that the selfish behavior of computationally bounded agents converges to an approximate Nash equilibrium in a finite number of improvements. Furthermore, we prove that, after each agent computes its response function once using a constant factor approximation algorithm, the outcome of the game is within a factor of O(logn) of the optimal social value, where n is the number of agents. Our simulation scenarios show that the price of anarchy is 30% better than that of the worst case analysis and that the system quickly (1 or 2 steps) converges to a Nash equilibrium.

Incentive-compatible opportunistic routing for wireless networks

User-contributed wireless mesh networks are a disruptive technology that may fundamentally change the economics of edge network access and bring the benefits of a computer network infrastructure to local communities at low cost, anywhere in the world. To achieve high throughput despite highly unpredictable and lossy wireless channels, it is essential that such networks take advantage of transmission opportunities wherever they emerge. However, as opportunistic routing departs from the traditional but less effective deterministic, shortest-path based routing, user nodes in such networks may have less incentive to follow protocols and contribute. In this paper, we present the first routing protocols in which it is incentive-compatible for each user node to honestly participate in the routing despite opportunistic transmissions. We not only rigorously prove the properties of our protocols but also thoroughly evaluate a complete implementation of our protocols. Experiments show that there is a 5.8%-58.0% gain in throughput when compared with an opportunistic routing protocol that does not provide incentives and users can act selfishly.

Identifying suspicious activities through DNS failure graph analysis

As a key approach to securing large networks, existing anomaly detection techniques focus primarily on network traffic data. However, the sheer volume of such data often renders detailed analysis very expensive and reduces the effectiveness of these tools. In this paper, we propose a light-weight anomaly detection approach based on unproductive DNS traffic, namely, the failed DNS queries, with a novel tool - DNS failure graphs. A DNS failure graph captures the interactions between hosts and failed domain names. We apply a graph decomposition algorithm based on the tri-nonnegative matrix factorization technique to iteratively extract coherent co-clusters (dense subgraphs) from DNS failure graphs. By analyzing the co-clusters in the daily DNS failure graphs from a 3-month DNS trace captured at a large campus network, we find these co-clusters represent a variety of anomalous activities, e.g., spamming, trojans, bots, etc.. In addition, these activities often exhibit distinguishable subgraph structures. By exploring the temporal properties of the co-clusters, we show our method can identify new anomalies that likely correspond to unreported domain-flux bots.