Keqiang He

Scalable Name Lookup in NDN Using Effective Name Component Encoding

Name-based route lookup is a key function for Named Data Networking (NDN). The NDN names are hierarchical and have variable and unbounded lengths, which are much longer than IPv4/6 address, making fast name lookup a challenging issue. In this paper, we propose an effective Name Component Encoding (NCE) solution with the following two techniques: (1) A code allocation mechanism is developed to achieve memory-efficient encoding for name components, (2) We apply an improved State Transition Arrays to accelerate the longest name prefix matching and design a fast and incremental update mechanism which satisfies the special requirements of NDN forwarding process, namely to insert, modify, and delete name prefixes frequently. Furthermore, we analyze the memory consumption and time complexity of NCE. Experimental results on a name set containing 3,000,000 names demonstrate that compared with the character trie NCE reduces overall 30% memory. Besides, NCE performs a few millions lookups per second (on an Intel 2.8 GHz CPU), a speedup of over 7 times compared with the character trie. Our evaluation results also show that NCE can scale up to accommodate the potential future growth of the name sets.

Presto: Edge-based Load Balancing for Fast Datacenter Networks

Datacenter networks deal with a variety of workloads, ranging from latency-sensitive small flows to bandwidth-hungry large flows. Load balancing schemes based on flow hashing, e.g., ECMP, cause congestion when hash collisions occur and can perform poorly in asymmetric topologies. Recent proposals to load balance the network require centralized traffic engineering, multipath-aware transport, or expensive specialized hardware. We propose a mechanism that avoids these limitations by (i) pushing load-balancing functionality into the soft network edge (e.g., virtual switches) such that no changes are required in the transport layer, customer VMs, or networking hardware, and (ii) load balancing on fine-grained, near-uniform units of data (flowcells) that fit within end-host segment offload optimizations used to support fast networking speeds. We design and implement such a soft-edge load balancing scheme, called Presto, and evaluate it on a 10 Gbps physical testbed. We demonstrate the computational impact of packet reordering on receivers and propose a mechanism to handle reordering in the TCP receive offload functionality. Prestos performance closely tracks that of a single, non-blocking switch over many workloads and is adaptive to failures and topology asymmetry.

Next stop, the cloud: understanding modern web service deployment in EC2 and azure

An increasingly large fraction of Internet services are hosted on a cloud computing system such as Amazon EC2 or Windows Azure. But to date, no in-depth studies about cloud usage by Internet services has been performed. We provide a detailed measurement study to shed light on how modern web service deployments use the cloud and to identify ways in which cloud-using services might improve these deployments. Our results show that: 4% of the Alexa top million use EC2/Azure; there exist several common deployment patterns for cloud-using web service front ends; and services can significantly improve their wide-area performance and failure tolerance by making better use of existing regional diversity in EC2. Driving these analyses are several new datasets, including one with over 34 million DNS records for Alexa websites and a packet capture from a large university network.

Measuring control plane latency in SDN-enabled switches

Timely interaction between an SDN controller and switches is crucial to many SDN applications---e.g., fast rerouting during link failure and fine-grained traffic engineering in data centers. However, it is not well understood how the control plane in SDN switches impacts these applications. To this end, we conduct a comprehensive measurement study using four types of production SDN switches. Our measurements show that control actions, such as rule installation, have surprisingly high latency, due to both software implementation inefficiencies and fundamental traits of switch hardware.

AC/DC TCP: Virtual Congestion Control Enforcement for Datacenter Networks

Multi-tenant datacenters are successful because tenants can seamlessly port their applications and services to the cloud. Virtual Machine (VM) technology plays an integral role in this success by enabling a diverse set of software to be run on a unified underlying framework. This flexibility, however, comes at the cost of dealing with out-dated, inefficient, or misconfigured TCP stacks implemented in the VMs. This paper investigates if administrators can take control of a VMs TCP congestion control algorithm without making changes to the VM or network hardware. We propose AC/DC TCP, a scheme that exerts fine-grained control over arbitrary tenant TCP stacks by enforcing per-flow congestion control in the virtual switch (vSwitch). Our scheme is light-weight, flexible, scalable and can police non-conforming flows. In our evaluation the computational overhead of AC/DC TCP is less than one percentage point and we show implementing an administrator-defined congestion control algorithm in the vSwitch (i.e., DCTCP) closely tracks its native performance, regardless of the VMs TCP stack.

Parallel Name Lookup for Named Data Networking

Name-based route lookup is a key function for Named Data Networking (NDN). The NDN names are hierarchical and have variable and unbounded lengths, which are much longer than IPv4/6 address, making fast name lookup a challenging issue. In this paper, we propose a parallel architecture for NDN name lookup called Parallel Name Lookup (PNL) which leverages hardware parallelism to achieve high lookup speedup while keeping a low and controllable memory redundancy. The core of PNL is an allocation algorithm that maps the logically tree-based structure to physically parallel modules, with low computational complexity. We evaluate the PNLs performance and show that PNL dramatically accelerates the name lookup process. Furthermore, with certain knowledge of prior probability, the speedup can be significantly improved.

Greening the Internet Using Multi-frequency Scaling Scheme

In this paper, we have designed a Multi-Frequency Scaling scheme for energy conservation of network devices, especially routers and switches. The frequency of components in a network device is scaled dynamically according to the real time workload. A Markov model is developed for performance analysis of this mechanism. We implement a prototype of this scheme in the data path of a general IPv4 router based on a real hardware platform - NetFPGA. Experimental results show excellent energy savings at the cost of a tolerable latency, under various ranges of traffic loads. Our work indicates the feasibility and possibility of deploying this mechanism into real network devices for energy saving.

Virtualized and self-configurable utility communications enabled by software-defined networks

Utility communications are increasingly required to support machine-to-machine (M2M) communications for hundreds to millions of end devices ranging from meters and PMUs to tiny sensors, high-powered sensors (e.g., intelligent electric devices), and electric vehicles. The Software Defined Network (SDN) concept provides inherent features to support in a self-configurable and scalable manner the deployment and management of existing and envisioned utility communication networks that will connect between end devices and application servers, or among end devices. The programmability of SDN technology allows the agile, elastic, and scalable deployment of present and future utility applications with varying requirements on security and time criticality. In this work, we first show that a well-known standard solution (i.e., IEEE 802.1Q [1]), which is popularly employed for virtual networking in industry, is limited to support large-scale utility M2M applications. Next, with some utility application use cases, we demonstrate that using the SDN technology (i.e., OpenFlow [2]), we enable elastically adaptable virtual utility network slices per-application to securely, dynamically, and cost-efficiently meet the utility communication needs. Specifically, we design a SDN-based architectural solution for virtual utility networks that will support self-configurable, secure, and scalable deployment of utility applications that leverage many end devices. Using two SDN-enabled Ethernet switches [3] available in todays market, the feasibility of our idea is discussed.

GreenVLAN: An energy-efficient approach for VLAN design

The greening of the Internet has become an important research issue due to the huge energy consumption of the Internet. In this paper, we explore network-level power saving mechanisms in the context of virtual local area networks (VLANs) given their importance and prevalence in enterprise, campus and Data Center Networks (DCNs). After investigating the disadvantages of current VLAN design method, we propose the GreenVLAN approach, which aims at reducing the power usage in the next generation Energy-Efficient Ethernet. The proposed GreenVLAN approach is formulated as a nonlinear integer programming problem with the total energy consumption as the objective to be minimized. Besides, we develop a practical heuristic algorithm to solve the proposed model and the complexity of the algorithm is analyzed. Our experiments on both simulated topology and real-world campus network demonstrate that Green-VLAN could achieve considerable power saving (reduce the power consumption of Ethernet links by about 27%~53% for a typical VLAN configuration) compared with current VLAN practice.

Parallel Architecture for High Throughput DFA-Based Deep Packet Inspection

Multi-pattern matching is a key technique for implementing network security applications such as Network Intrusion Detection/Protection Systems (NIDS/NIPSes) where every packet is inspected against predefined attack signatures written in regular expressions (regexes). To this end, Deterministic Finite Automaton (DFA) is widely used for multi-regex matching, but existing DFAbased researches have claimed high throughput at an expenses of extremely high memory cost. In this paper, we propose a parallel architecture of DFA called Parallel DFA (PDFA), using multiple flow aggregations to increase the throughput with nearly no extra memory cost. The basic idea is to selectively store the DFA in multiple memory modules which can be accessed in parallel and to explore the potential parallelism. The memory cost of our system in both the average cases and the worst cases is analyzed, optimized and evaluated by numerical results. The evaluation shows that we obtain an average speedup of about 0.5k to 0.7k where k is the number of parallel memory modules under our synthetic trace and compressed real trace in a statistical average case, compared with the traditional DFA-based matching approaches.