We present MetaPolar, a fully passive mmWave metasurface tag that enables robust Infrastructure-to-Vehicle (I2V) communication using standard automotive radars. Unlike conventional backscatter or spatial-encoding approaches, MetaPolar exploits the polarization domain: engineered split-ring resonators rotate the incident polarization to generate strong cross-polarized returns, while a crossed-dipole layer independently sculpts co- and cross-polarized amplitudes and phases. Through full-wave simulation, we build a codebook mapping metasurface layouts to distinct 2 × 2 polarimetric RCS signatures. A novel semi-retroreflective beam-shaping design maintains strong polarimetric contrast across a ± 7.5° field of view, ensuring reliable decoding under realistic vehicle motion. We implement MetaPolar on low-cost FR-4 PCBs (< $3 per tag) and validate it with a custom mmWave radar platform. Experiments demonstrate 2-bit encoding per tag (scalable to 2N bits with N tags), simultaneous multi-tag reading, and seamless integration into existing radar pipelines. Our results highlight MetaPolar’s potential for cost-effective, scalable radar-readable signage in next-generation intelligent transportation systems.

Millimeter wave (mmWave) networks offer multi-gigabit data rates but suffer from severe path loss and blockage, resulting in spotty coverage. Emerging reconfigurable intelligent surfaces (RIS) can mitigate these challenges, but their reliance on active control channels, power sources, and complex runtime coordination imposes significant hardware and deployment overhead. This paper introduces FlowForm, a system that expands mmWave coverage using networks of passive metasurfaces that require no power, control, or runtime coordination. FlowForm's key innovation is a hierarchical flow topology that organizes passive metasurfaces into major flows (directional relay chains using near-field focusing) and minor flows (wide-area fan beams), enabling multi-hop passive routing and over-the-air combination of analog signals. We develop a theoretical framework establishing the optimality of this topology and a hierarchical optimization algorithm that jointly determines metasurface placement and beam configurations. FlowForm operates transparently with standard mmWave network protocols, managing channel dynamics and multi-user interference through diversity-aware design rather than runtime reconfiguration. Our experimental evaluation across five indoor environments demonstrates up to 94% average rate improvement and 114% coverage expansion using low-cost 3D-printed metasurfaces ($2 per unit), achieving performance comparable to active RIS at orders of magnitude lower cost.

This paper proposes ElastiCast, a novel Bluetooth Low Energy (BLE) broadcast mode that reduces the neighbor discovery latency in offline finding networks (OFNs). ElastiCast adapts the broadcast mode of the lost devices to the scan modes of the finder devices, considering their diversity. We start with an overview of OFNs, followed by a detailed analysis of the issues and challenges of existing solutions, which motivates the design of ElastiCast. Then we provide Blender, a simulator that models the neighbor discovery behavior of different broadcasters and scanners. By adopting Blender, ElastiCast can be implemented with three components: Local Optima Estimation, Common Interest Extraction, and Interval Multiplexing, in which we capture the key features of BLE neighbor discovery and globally optimize the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget.

Millimeter-wave (mmWave) radar has become instrumental in diverse consumer applications. Yet current radar architectures face major limitations. While full-MIMO structures are feature-rich, their cost and complexity rise rapidly with more antennas. Phased-MIMO radars promise enhanced scalability by combining large phased arrays with a small number of RF chains. Nevertheless, the phased-MIMO research thus far primarily relies on simulation or theoretical analysis. In this paper, we introduce HybRadar, a novel programmable phased-MIMO radar platform to address this experimental gap. HybRadar repurposes the phased arrays on a low-cost 802.11ad radio to create a scalable low-cost array of phased subarrays. It further incorporates transmit/receive front-end, control channel, and hardware synchronization mechanisms to enable a modular phased-MIMO system. By extending recent MIMO array synthesis models, we optimize the placement of phased subarrays to maximize the spatial resolution. Our prototype validation and case studies confirm the capability and versatility of HybRadar.

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.

Recently, such industrial pioneers as Apple and Samsung have offered a new generation of offline finding network (OFN) that enables crowd search for missing devices without leaking private data. Specifically, OFN leverages nearby online finder devices to conduct neighbor discovery via Bluetooth Low Energy (BLE), so as to detect the presence of offline missing devices and report an encrypted location back to the owner via the Internet. The user experience in OFN is closely related to the success ratio (possibility) of finding the lost device, where the latency of the prerequisite stage, i.e., neighbor discovery, matters. However, the crowd-sourced finder devices show diversity in scan modes due to different power modes or different manufacturers, resulting in local optima of neighbor discovery performance. In this paper, we present a brand-new broadcast mode called ElastiCast to deal with the scan mode diversity issues. ElastiCast captures the key features of BLE neighbor discovery and globally optimizes the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget.

Millimeter-wave backscatter can simultaneously support high-precision sensing and massive communication and represent one prominent technical evolution in next-generation wireless systems. The backscatter tags should ideally work across a wide mmWave spectrum range with consistent signal strength and angular coverage to accommodate highly diverse application scenarios. However, existing tags made of resonant antennas and RFICs only achieve a few GHz of bandwidth and hardly meet these requirements. In this paper, we present UniScatter, a new backscatter tag structure based on metamaterials. The key design of UniScatter is a graphene-based modulator and a lens-based retroreflector, which have consistent electromagnetic responses across an extensive frequency range and wide angular field-of-view. We have developed a robust fabrication process for UniScatter, and tested it on various mmWave sensing and communication devices. Our field tests show that UniScatter can backscatter signals across a wide frequency band from 24 GHz to 77 GHz with consistently high signal strength and wide angular coverage in 3D space.

Multipath transport allows the simultaneous use of diverse paths on mobile devices to maximize mobile resource usage. Over the years, we have witnessed several mobile multipath deployment examples by network operators and mobile app providers. However, existing deployment methods require modifications to either the network infrastructure or both endpoints. To lower the bar of the deployment, we present Fleety, a mobile system service that provides the multi-path transport capability with client-only modification. To the best of our knowledge, we are the first to carry out a large-scale mobile multipath deployment that can support hundreds of mobile applications in the cross-ISP setting. This paper is a retrospective of our experience in building and deploying multipath transport for mobile applications. We reveal several practical deployment challenges and share our experience in dealing with them.

Radar sensors have recently been explored in the industrial and consumer Internet of Things (IoT). However, such applications often require self-sustainable or untethered operations, which are at odds with the high power consumption of radar. This paper proposes NeuroRadar, a neuromorphic radar sensor, to achieve low-power wireless sensing. NeuroRadar jointly optimizes the analog hardware and the computation model, in order to mimic the highly efficient biological sensing and neural processing system. NeuroRadar features a highly simplified radar front end, which eliminates the power-hungry components in conventional radars. It directly "encodes" ambient motion into spiking signals, which can be processed using spiking neural networks running on energy-efficient neuromorphic computing platforms. We have prototyped NeuroRadar and evaluated its performance in two use cases: gesture sensing and localization. Our experiments demonstrate that NeuroRadar can achieve high sensing accuracy, at orders of magnitude lower power consumption compared with traditional radar.

TCP incast has become a practical problem for high-bandwidth, low-latency transmissions, resulting in throughput degradation of up to 90% and delays of hundreds of milliseconds, severely impacting application performance. However, in virtualized multi-tenant data centers, host-based advancements in the TCP stack are hard to deploy from the operators’ perspective. Operators only provide infrastructure in the form of virtual machines, in which only tenants can directly modify the end-host TCP stack. In this paper, we present R-AQM, a switch-powered reverse ACK active queue management (R-AQM) mechanism for enhancing ACK-clocking effects through assisting legacy TCP. Specifically, R-AQM proactively intercepts ACKs and paces the ACK-clocked in-flight data packets, preventing TCP from suffering incast collapse. We implement and evaluate R-AQM in NS-3 simulation and NetFPGA-based hardware switch. Both simulation and testbed results show that R-AQM greatly improves TCP performance under heavy incast workloads by significantly lowering packet loss rate, reducing retransmission timeouts, and supporting 16 times (i.e., 60 to 1000) more senders. Meanwhile, the forward queuing delays are also reduced by 4.6 times.

Understanding link-level performance is key to assuring the quality of cloud-based and OTT services, optimal path selection, robust network operations and beyond. However, direct measurement of each link not only incurs high overhead at the Internet-scale but also is infeasible due to lack of access to network measurement information beyond AS boundaries and functional limitations at relay nodes. Although network tomography is well suited, existing approaches are insufficient due to their unrealistic assumptions with respect to stability, controllability, and visibility. Motivated by this, we propose PAINT, an online iterative algorithm that estimates and refines link-level performance metrics based on path-level measurement. In PAINT, the link metrics are iteratively estimated by minimizing their least square error (LSE) and calibrated based on the comparison of weight between the estimated shortest paths (SPs) and best-known paths from end-to-end path measurements. The key insight is that when there is inconsistency between these paths, then weights of links on the estimated SP are likely mis-estimated, triggering a further round of estimation to refine the estimated link metrics. Evaluation of PAINT, focusing on link delay estimation, using four different real network topologies and two real-world measurement datasets (including one we collected) shows that relative to existing approaches, it yields up to 3x gain in absolute link delay estimation accuracy and improves decisions dependent on link delay estimation by up to 5x in relative error.

Many applications have deadline requirements for their data delivery, such as real-time video, multiplayer gaming, and cloud AR/VR. However, the current transport layers' APIs are too primitive to accomplish that. Therefore, today's applications are forced to build their customized and complex deadline-aware data delivery mechanisms. In this work, we design Deadline-aware Transport Protocol (DTP) to provide deliver-before-deadline service over the wild Internet. To fulfill the diverse and sometimes conflicting requirements over the fluctuating network, we design the Active-Drop-at-Sender scheduler and adaptive redundancy. We build DTP by extending QUIC, and then develop two applications that utilize DTP. Extensive evaluations demonstrate that DTP is easy to use and can bring significant performance improvement (1.2x to 5x) compared to vanilla QUIC.

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to optimize multiple users’ experiences jointly, but users often suffer short connection durations and frequent handoffs because of their high mobility. This paper proposes an end-to-end scheme, VSiM, to support mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement on average of the averaged QoE.

Various congestion control (CC) algorithms have been designed to target specific scenarios. To automate this process, researchers have begun to use machine learning to automatically control the congestion window. These, however, often rely on heavyweight learning models (e.g., neural networks). This can make them unsuitable for resource-constrained mobile devices. On the other hand, lightweight models (e.g., decision trees) are often incapable of reflecting the complexity of diverse mobile wireless environments. To address this, we present Muses, a learning-based approach for generating lightweight congestion control algorithms. Muses relies on imitation learning to train a universal (heavy) LSTM model, which is then used to extract (lightweight) decision tree models that are each targeted at an individual environment. Muses then dynamically selects the most appropriate decision tree on a per-flow basis. We show that Muses can generate high throughput policies across a diverse set of environments, and it is sufficiently light to operate on mobile devices.

Applications that allow sharing of user-created short videos exploded in popularity in recent years. A typical short video application allows a user to swipe away the current video being watched and start watching the next video in a video queue. Such user interface causes significant bandwidth waste if users frequently swipe a video away before finishing watching. Solutions to reduce bandwidth waste without impairing the Quality of Experience (QoE) are needed. Solving the problem requires adaptively prefetching of short video chunks, which is challenging as the download strategy needs to match unknown user viewing behavior and network conditions. In our work, we first formulate the problem of adaptive multi-video prefetching in short video streaming. Then, to facilitate the integration and comparison of researchers' algorithms towards solving the problem, we design and implement a discrete-event simulator, which we release as open source. Finally, based on the organization of the Short Video Streaming Grand Challenge at ACM Multimedia 2022, we analyze and summarize the algorithms of the contestants, with the hope of promoting the research community towards addressing this problem.

The emerging applications including AR/VR inter-active gaming, ultra-high-definition live streaming, 4K wireless projection, Metaverse, etc. imply the demand for ultra-low latency and ultra-high bandwidth wireless transmission. The legacy kernel TCP stack is not fully satisfactory because it induces the CPU bottleneck on hosts. In this paper, we propose Wireless-RDMA (W-RDMA) that enables RDMA in wireless networks to tackle the CPU bottleneck issue on wireless hosts. The feasibility of W-RDMA is demonstrated through testbed experiments. Technical challenges and future opportunities are further discussed. We believe it is a small but crucial step for enabling RDMA for wireless transmission.

TCP incast has become a practical problem for high-bandwidth, low-latency transmissions, resulting in throughput degradation of up to 90% and delays of hundreds of milliseconds, severely impacting application performance. However, in virtualized multi-tenant data centers, host-based advancements in the TCP stack are hard to deploy from the operators perspective. Operators only provide infrastructure in the form of virtual machines, in which only tenants can directly modify the end-host TCP stack. In this paper, we present R-AQM, a switch-powered reverse ACK active queue management (R-AQM) mechanism for enhancing ACK-clocking effects through assisting legacy TCP. Specifically, R-AQM proactively intercepts ACKs and paces the ACK-clocked in-flight data packets, preventing TCP from suffering incast collapse. We implement and evaluate R-AQM in NS-3 simulation and NetFPGA-based hardware switch. Both simulation and testbed results show that R-AQM greatly improves TCP performance under heavy incast workloads by significantly lowering packet loss rate, reducing retransmission timeouts, and supporting 16 times (i.e., 60 → 1000) more senders. Meanwhile, the forward queuing delays are also reduced by 4.6 times.

The main iteration goal of transport protocols is to optimize the performance of specific modules. In this poster, we propose a framework named EasyTrans, that enables fast iteration of transport protocol modules. With EasyTrans, developers can focus on the modules they want to iterate and no longer need to deal with other unnecessary parts of the transport protocol. Through different module calling modes, EasyTrans enables high performance even if the modules use algorithms that require sophisticated computation such as machine learning. We implement EasyTrans based on QUIC. Evaluation results show that the overhead of EasyTrans is slight.

Delay-sensitive multimedia streaming applications require their data to be delivered before a deadline to be useful. The data transmitted by these applications can usually be partitioned into blocks with different priorities, assigned based on the impact of a block on the Quality of Experience (QoE) if it misses its delivery deadline. Meet their deadline requirements is challenging due to the dynamics of the network and these applications' high demand on network resources. To encourage the research community to address this challenge, we organize the "Meet Deadline Requirements" Grand Challenge at ACM Multimedia 2021. This grand challenge provides a simulation platform onto which the participants can implement their block scheduler and bandwidth estimator and then benchmark against each other using a common set of application traces and network traces.

The shared nature of the wireless medium induces contention between data transport and backward signaling, such as acknowledgment. The current way of TCP acknowledgment induces control overhead which is counter-productive for TCP performance especially in wireless local area network (WLAN) scenarios. In this paper, we present a new acknowledgment called TACK (“Tame ACK”), as well as its TCP implementation TCP-TACK. TACK seeks to minimize ACK frequency, which is exactly what is required by transport. TCP-TACK works on top of commodity WLAN, delivering high wireless transport goodput with minimal control overhead in the form of ACKs, without any hardware modification. Evaluation results show that TCP-TACK achieves significant advantages over legacy TCP in WLAN scenarios due to less contention between data packets and ACKs. Specifically, TCP-TACK reduces over 90% of ACKs and also obtains an improvement of up to 28% on goodput. A TACK-based protocol is a good replacement of the legacy TCP to compensate for scenarios where the acknowledgment overhead is non-negligible.

Pi-Radio's vision is to democratize wireless research by providing advanced mmWave Software Defined Radio (SDR) platforms to the community at plainly affordable price points. Pi-Radio's v1 SDR features a 4-channel fully-digital transceiver that operates in the 57-64 GHz band. Fully-digital (a.k.a. MIMO) transceiver architectures enable multiple simultaneous TX/RX beams, standing in stark contrast with phased arrays featuring analog beamformers that are capable of transmitting/receiving only one beam at a time. This opens up a whole set of research problems to work on, across virtually every layer of the protocol stack. In this demo, the team will: (1) prove the correct formation of different TX/RX beams by applying geometrically determined beamforming weights, and (2) prove the benefits of fully-digital beamforming by transmitting four independent streams of data with an OFDM-based physical layer.

RDMA communication in virtual private cloud (VPC) networks is still a challenging job due to the difficulty in fulfilling all virtualization requirements without sacrificing RDMA communication performance. To address this problem, this paper proposes a software-defined solution, namely, MasQ, which is short for "queue masquerade". The core insight of MasQ is that all RDMA communications should associate with at least one queue pair (QP). Thus, the requirements of virtualization, such as network isolation and the application of security rules, can be easily fulfilled if QP's behavior is properly defined. In particular, MasQ exploits the virtio-based paravirtualization technique to realize the control path. Moreover, to avoid performance overhead, MasQ leaves all data path operations, such as sending and receiving, to the hardware. We have implemented MasQ in the OpenFabrics Enterprise Distribution (OFED) framework and proved its scalability and performance efficiency by evaluating it against typical applications. The results demonstrate that MasQ achieves almost the same performance as bare-metal RDMA for data communication.

The shared nature of the wireless medium induces contention between data transport and backward signaling, such as acknowledgement. The current way of TCP acknowledgment induces control overhead which is counter-productive for TCP performance especially in wireless local area network (WLAN) scenarios.

Modern mobile wireless networks have been demonstrated to be high-speed but lossy, while mobile applications have more strict requirements including reliability, goodput guarantee, bandwidth efficiency, and computation efficiency. Such a complicated combination of requirements and conditions in networks pushes the pressure to transport layer protocol design. We analyze and argue that few of existing network transport layer solutions are able to handle all these requirements. We design and implement Sphinx to satisfy the four requirements in high-speed and lossy networks. Sphinx has (1) a proactive coding-based method named semi-random LT codes for loss recovery, which estimates packet loss rate and adjusts the redundancy level accordingly, (2) a reactive retransmission method named Instantaneous Compensation Mechanism (ICM) for loss retransmission, which compensates the lost packets once actual loss exceeds the estimation, and (3) a parallel coding architecture, which leverages multi-core, shared memory and kernel-bypass DPDK. Prototype and evaluation show that Sphinx outperforms TCP and other coding solutions significantly in microbenchmarks across all four requirements, and improves the performance of applications such as video streaming and block data transfer.

Live video streaming delivery over Dynamic Adaptive Video Streaming (DASH) is challenging as it requires low end-to-end latency, is more prone to stall, and the receiver has to decide online which representation at which bitrate to download and whether to adjust the playback speed to control the latency. To encourage the research community to come together to address this challenge, we organize the Live Video Streaming Grand Challenge at ACM Multimedia 2019. This grand challenge provides a simulation platform onto which the participants can implement their adaptive bitrate (ABR) logic and latency control algorithm, and then benchmark against each other using a common set of video traces and network traces. The ABR algorithms are evaluated using a common Quality-of- Experience (QoE) model that accounts for playback bitrate, latency constraint, frame-skipping penalty, and rebuffering penalty.

While the bandwidth and latency improvement of both WiFi and cellular data networks in the past decades are plenty evident, the extent of signal strength fluctuation and network disruptions (unexpected switching or disconnections) experienced by mobile users in today's network deployment remains less clear. This paper makes three contributions. First, we conduct the first extensive measurement of network disruptions and significant signal strength fluctuations (together denoted as network instabilities) experienced by 2000 smartphones in the wild. Our results show that network disruptions and signal strength fluctuations remains prevalent as we moved into the 4G era. Second, we study how well popular mobile apps today handle such network instabilities. Our results show that even some of the most popular mobile apps do not implement any disruption-tolerant mechanisms. Third, we present Janus, an intelligent interface management framework that exploits the multiple interfaces on a handset to transparently handle network disruptions and satisfy apps' performance requirement. We have implemented a prototype of Janus and our evaluation using a set of popular apps shows that Janus can: 1) transparently and efficiently handle network disruptions; 2) reduce video stalls by 2.9 times and increase 31% of the time of good voice quality; 3) reduce traffic size by 26.4% and energy consumption by 16.3% compared to naive solutions.

Software Defined Network (SDN) enables flexible update of network functions with a well-defined abstraction between the control and the data plane. However, multiple active network functions with the same priority will potentially trigger conflicts among policies with overlapped flow space, causing the flow table explosion. In contrast to the local switch conflict resolution schemes proposed by previous works, this paper tackles the same problem from a different angle and resolves the policy conflict problem by coordinating all switches under a global centralized view. Specifically, we propose COnflict RAzor (CORA), which tremendously reduces the storage cost of conflicting policies leveraging the global network information obtained in the controller. The basic idea of CORA is migrating policies causing large explosions across the network if necessary, while keeping the semantics equivalence. We prove CORA's NP hardness and propose a heuristic to efficiently search a near-optimal policy migration strategy. Our experiments demonstrate that, CORA can effectively reduce the flow table storage occupation by at least 49% within less than 40 seconds.

Recent advances in high speed rails (HSRs) are propelling the need for acceptable network service in high speed mobility environments. However, previous studies show that the performance of traditional single-path transmission degrades significantly during high speed mobility due to frequent handoff. Multi-path transmission with multiple carriers is a promising way to enhance the performance, because at any time, there is possibly at least one path not suffering a handoff. In this paper, for the first time, we measure multi-path TCP (MPTCP) with two cellular carriers on HSRs with a peak speed of 310km/h. We find a significant difference in handoff time between the two carriers. Moreover, we observe that MPTCP can provide much better performance than TCP in the poorer of the two paths. This indicates that MPTCP's robustness to handoff is much higher than TCP's. However, the efficiency of MPTCP is far from satisfactory. MPTCP performs worse than TCP in the better path most of the time. We find that the low efficiency can be attributed to poor adaptability to frequent handoff by MPTCP's key operations in sub-flow establishment, congestion control and scheduling. Finally, we discuss possible directions for improving MPTCP for such scenarios.

Today's data center applications can generate a diverse mix of short and long flows. However, switches used in a typical data center network are usually shallow buffered in order to reduce queueing delay and deployment cost. As a result, the buildup of the queues by long flows can block short flows, leading to frequent packet losses and retransmissions, which translates to crucial performance degradation. While multiple end-to-end TCP-based solutions have been proposed, none of them have tackled the real challenge: reliable transmission in the network. In this paper, we fill this gap by presenting PABO — a novel link-layer design that can mitigate congestion by temporarily bouncing packets to upstream switches. PABO's design fulfills the following demands: i) providing per-flow based flow control on the link layer, ii) handling transient congestion without the intervention of end devices, and iii) gradually back propagating the congestion signal to the source when the network is not capable to handle the congestion. We complete a proof-of-concept implementation, and experiments under different severities of congestion show that PABO outperforms the standard unreliable link-layer protocol by guaranteeing zero packet loss while introducing only a reasonable stretch on packet delay.

While the bandwidth and latency improvement of both WiFi and cellular data networks in the past decade are plenty evident, the extent of signal strength fluctuation and network disruptions (unexpected switching or disconnections) experienced by mobile users in today's network deployment remains less clear. This paper makes three contributions. First, we conduct the first extensive measurement of network disruptions and signal strength fluctuations (together denoted as instabilities) experienced by 2000 smartphones in the wild. Our results show that network disruptions and signal strength fluctuations remain prevalent as we moved into the 4G era. Second, we study how well popular mobile apps today handle such network instabilities. Our results show that even some of the most popular mobile apps do not implement any disruption-tolerant mechanisms. Third, we present JANUS, an intelligent interface management framework that exploits the multiple interfaces on a handset to transparently handle network disruptions and improve apps' QoE. We have implemented JANUS on Android and our evaluation using a set of popular apps shows that Janus can (1) transparently and efficiently handle network disruptions, (2) reduce video stalls by 2.9 times and increase 31% of the time of good voice quality compared to naive solutions.

Recent advances in high speed rails (HSRs), coupled with user demands for communication on the move, are propelling the need for acceptable quality of communication services in high speed mobility scenarios. This calls for an evaluation of how well popular voice/video call applications, such as Skype, can perform in such scenarios. This paper presents the first comprehensive measurement study on Skype voice/video calls in LTE networks on HSRs with a peak speed of 310 km/h in China. We collected 50 GB of performance data, covering a total HSR distance of 39,900 km. We study various objective performance metrics (such as RTT, sending rate, call drop rate, etc.), as well as subjective metrics such as quality of experience of the calls. We also evaluate the efficiency of Skype's algorithms regarding the level of utilization of network resources. We observed that the quality of Skype calls degrades significantly on HSRs. Moreover, it was discovered that Skype significantly under-utilizes the network resources, such as available bandwidth. We discovered that the root of these inefficiencies is the poor adaptability of Skype in many aspects, including overlay routing, rate control, state update and call termination. These findings highlight the need to develop more adaptive voice/video call services for high speed mobility scenarios.

While TCP has been extensively studied in static and low speed mobility situations, it has not yet been well explored in high speed mobility scenarios. Given the increasing deployment of high speed transport systems (such as high speed rails), there is an urgent need to understand the performance and behavior of TCP in such high speed mobility environments. In this paper, we conduct a comprehensive study to investigate the performance and behavior of TCP in a high speed environment with a peak speed of 310 km/h. Over a 16-month period spanning four years, we collect 500 GB of performance data on 3/4G networks in high speed trains in China, covering a distance of 108,490 km. We start by analyzing performance metrics, such as RTT, packet loss rate, and throughput. We then evaluate the challenges posed on the main TCP operations (establishment, transmission, congestion control, flow control, and termination) by such high speed mobility. This paper shows that RTT and packet loss rate increase significantly and throughput drops considerably in high speed situations. Moreover, TCP fails to adapt well to such extremely high speed leading to abnormal behavior, such as high spurious retransmission time out rate, aggressive congestion window reduction, long delays during connection establishment and closure, and transmission interruption. As we prepare to move into the era of 5G, and as the need for high speed travel continues to increase, our findings indicate a critical need for efforts to develop more adaptive transport protocols for such high speed environments.

The advent of software defined networking enables flexible, reliable and feature-rich control planes for data center networks. However, the tight coupling of centralized control and complete visibility leads to a wide range of issues among which scalability has risen to prominence due to the excessive workload on the central controller. By analyzing the traffic patterns from a couple of production data centers, we observe that data center traffic is usually highly skewed and thus edge switches can be clustered into a set of communication-intensive groups according to traffic locality. Motivated by this observation, we present LazyCtrl, a novel hybrid control plane design for data center networks where network control is carried out by distributed control mechanisms inside independent groups of switches while complemented with a global controller. LazyCtrl aims at bringing laziness to the global controller by dynamically devolving most of the control tasks to independent switch groups to process frequent intra-group events near the datapath while handling rare inter-group or other specified events by the controller. We implement LazyCtrl and build a prototype based on Open vSwitch and Floodlight. Trace-driven experiments on our prototype show that an effective switch grouping is easy to maintain in multi-tenant clouds and the central controller can be significantly shielded by staying “lazy”, with its workload reduced by up to 82 percent.

The advent of software defined networking enables flexible, reliable and feature-rich control planes for data center networks. However, the tight coupling of centralized control and complete visibility leads to a wide range of issues among which scalability has risen to prominence. We observe that data center traffic is usually highly skewed and thus edge switches can be grouped according to traffic locality. As a result, the workload of the central controller could be highly reduced if we carry out distributed control inside those groups. Based on the above observation, we present LazyCtrl, a novel hybrid control plane design for data center networks. LazyCtrl aims at bringing laziness to the central controller by dynamically devolving most of the control tasks to independent switch groups to process frequent intra-group events using distributed control mechanisms, while handling rare inter-group or other specified events by the controller. We implement LazyCtrl and build a prototype based on Open vSwich and Floodlight. Trace-driven experiments on our prototype show that an effective switch grouping is easy to maintain in multi-tenant clouds and the central controller can be significantly shielded by staying lazy, with its workload reduced by up to 82%.

Today's giant data centers are power hungry. Data center energy saving not only helps control the operational cost, but also benefits the sustainable growth of cloud services. Due to the adoption of much more switches in modern data centers as well as the mature server-side power management techniques, energy saving for the data center network is becoming increasingly important. Most previous works on saving data center network energy focus on aggregating flows to as few switches as possible. However, in this paper we argue that this method may not work for network-limited flows, the throughputs of which are elastic based on the competing flows. To save the network energy consumed by this kind of elastic flows, we propose a flow scheduling approach called Willow, which takes both the number of switches involved and their active working durations into consideration. We formulate this problem by programming and design a greedy approximate algorithm to schedule flows in an online manner. Simulations based on MapReduce traces show that Willow can save up to 60 percent network energy compared with ECMP scheduling in typical settings, and outperforms other classical heuristic algorithms such as simulated annealing and particle swarm optimization. Testbed Experiments demonstrate that this kind of dynamic energy-efficient flow scheduling causes negligible impact on upper-layer applications.

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.

The network resource competition of today' data enters is extremely intense between long-lived elephant flows and latency-sensitive mice flows. Achieving both goals of high throughput and low latency respectively for the two types of flows requires compromise, which recent research has not successfully solved mainly due to the transfer of elephant and mice flows on shared links without any differentiation. However, current data enters usually adopt clos-based topology, e.g. Fat-tree/VL2, so there exist multiple shortest paths between any pair of source and destination. In this paper, we leverage on this observation to propose a flow scheduling scheme, Freeway, to adaptively partition the transmission paths into low latency paths and high throughput paths respectively for the two types of flows. An algorithm is proposed to dynamically adjust the number of the two types of paths according to the real-time traffic. And based on these separated transmission paths, we propose different flow type-specific scheduling and forwarding methods to make full utilization of the bandwidth. Our simulation results show that Freeway significantly reduces the delay of mice flow by 85.8% and achieves 9.2% higher throughput compared with Hedera.

Handling link failures is the fundamental task of routing schemes. Routing protocols based on link state (e.g., OSPF) require a global state advertisement and re-computation when link failure happens, and will cause inevitable delivery failures. To improve the routing resilience without introducing significant extra overhead, we propose a new routing approach, Keep Forwarding (KF) to achieve k-link failure resilience using inport-aware forwarding. KF is (i) flexible to handle multiple failures (or k-failure) with only small path stretch, (ii) efficient in recovery speed by instant and local lookup, (iii) bounded on memory requirement. Besides, the proposed approach is compatible with existing Internet protocols and routing infrastructures (e.g., requires no packet labeling or state recording), and the pre-computation has a linear temporal complexity. Experimental results on real ISP and datacenter networks reveal that KF guarantees near-optimal resilience (99.9%~100% for single failure and over 99.7% for multiple failures), with the average path stretch increment less than 5%.

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.

Longest Prefix Matching (LPM), Policy Filtering (PF), and Content Filtering (CF) are three important tasks for Internet nowadays. It is both technologically and economically important to develop integrated solutions to the effective execution of the three tasks. To this end, in this paper, we propose a distributed Ternary Content Addressable Memory (TCAM) coprocessor architecture. The integrated solution exploits the complementary lookup load and storage load requirements of the three tasks to balance the lookup load and storage load among the TCAMs. A prefix filtering-based CF algorithm is designed to reduce the lookup load and a novel cache system is developed to dynamically handle the lookups from overloaded TCAMs. Simulations based on real-world traffic traces show that the proposed solution can perform all three tasks given a 10 Gbps line rate using only the resources required to perform just the CF task given a 10 Gbps line rate.

Address auto-configuration is a key problem in data center networks, where servers and switches encode topology information into their addresses for routing. A recent work DAC [2] has been introduced to address this problem. Without malfunctions, DAC can auto-configure all the devices quickly. But in case of malfunctions, DAC requires significant human efforts to correct malfunctions and it can cause substantial operation delay of the whole data center. In this paper, we further optimize address auto-configuration process even in the presence of malfunctions. Instead of waiting for all the malfunctions to be corrected, we could first configure the devices that are not involved in malfunctions and let them work first. This idea can be translated to considerable practical benefits because in most cases malfunctions in data centers only account for a very small portion. To realize the idea, we conceptually remove the malfunctions from the physical data center topology graph and mathematically convert the address configuration problem into induced sub graph isomorphism problem, which is NP-complete. We then introduce an algorithm that can solve the induced sub graph isomorphism quickly by taking advantage of data center topology characteristics and induced sub graph properties. We extensively evaluate our design on representative data center structures with various malfunction scenarios. The evaluation results demonstrate that the proposed framework and algorithm are efficient and labor-free to deal with the mapping task in the presence of error devices.

Pattern Matching (PM) is a key building block for many emerging network applications. Modern multicore platforms are becoming performance competitive with traditional hardware solutions, which are expensive and hard to adapt to the rapid diversification of Internet applications. However, due to uneven network flow sizes and the need to retain packet order within each flow, traditional parallel processing models using packet flows as the basic unit to partition the workload cannot fully take advantage of multicore platforms' power, exhibiting low CPU utilization and poor scalability with increasing numbers of CPUs or cores. In this paper, we propose a novel parallel inspection model called Dynamic Differentiated Distributed Detection (D4). D4deploys balanced parallel detection by adding one more dimension on PM workload partition. The pattern set is prepartitioned into several subsets so as to distribute the workload of the hot flows across multiple cores while still maintaining packet order within each flow. We also show theoretically that higher number of subsets leads to higher algorithmic overhead. To achieve optimal throughput for all flow size distributions, D4prepartitions the pattern set in several ways for use in different detection modes beforehand, and then, dynamically switches among these modes on-the-fly according to the flow and runtime information it senses. D4also allows multiple PM algorithms to work simultaneously on different pattern subsets. According to several heuristics and the algorithms' characteristics, the detection mode selection and subset partitioning algorithms are designed to maximize the CPU/core utilization while avoiding unnecessary overheads. Experiments show that D4features high core utilization and low overhead, thus achieving distinct performance gains against traditional load balancing schemes, as shown by experimental results using real-world pattern sets and traffic traces.

Policy Filtering (PF) and Content Filtering (CF) are two important tasks in packet forwarding of today's Internet. It is both technologically and economically important to develop integrated solutions for executing both tasks to reduce cost. In this paper, we propose a distributed Ternary Content Addressable Memory (TCAM) coprocessor architecture to allow fast and integrated PF and CF. Due to significant requirement diversities in both lookup load and storage load between PF and CF, the integrated solution exploits the complementary characteristics of the two tasks and well balances both the lookup load and storage load among TCAMs. A prefix filtering based CF algorithm is designed to reduce the lookup load and a novel cache mechanism is developed to dynamically handle the lookups from overloaded TCAMs. Simulations based on real-world traffic traces show that the proposed solution can match 10Gbps line rate for executing both PF and CF with the similar costs as CF task only.

In this paper, we identify the unique challenges in deploying parallelism on TCAM-based pattern matching for Network Intrusion Detection Systems (NIDSes). We resolve two critical issues when designing scalable parallelism specifically for pattern matching modules: 1) how to enable fine-grained parallelism in pursuit of effective load balancing and desirable speedup simultaneously; and 2) how to reconcile the tension between parallel processing speedup and prohibitive TCAM power consumption. To this end, we first propose the novel concept of Negative Pattern Matching to partition flows, by which the number of TCAM lookups can be significantly reduced, and the resulting (fine-grained) flow segments can be inspected in parallel without incurring false negatives. Then we propose the notion of Exclusive Pattern Matching to divide the entire pattern set into multiple subsets which can later be matched against selectively and independently without affecting the correctness. We show that Exclusive Pattern Matching enables the adoption of smaller and faster TCAM blocks and improves both the pattern matching speed and scalability. Finally, our theoretical and experimental results validate that the above two concepts are inherently complementary, enabling our integrated scheme to provide performance gain in any scenario (with either clean or dirty traffic).

Multi-core platforms are becoming prevailing in telecom infrastructures, and many SIP(Session Initiation Protocol) enabled applications are using Java as the development language and runtime environment. It is important to understand the workload characteristics and performance issues of Java based SIP stack over multi-core platforms. In this paper, two RFC 3261 compliant Java based SIP stacks, the JAIN-SIP and a proprietary SIP stack are studied in depth. Especially we focused on the typical performance issues caused by either Java language features or the workload of SIP protocol, including scalability of SIP stack, resource contention issue and GC impact with SIP memory usage pattern. Some optimization techniques and their drawbacks are also discussed along with the performance evaluation. It turns out that due to the complex combination of SIP protocol semantics and Java language features, the performance of Java based SIP stack tends to be far from competitive on multicore platforms. Extensive optimization and certain improvements of program structure and object management policy may help, however, may, on the other hand, sacrifice some key values of the java language, e.g. easy development.

We propose a novel Voice over IP (VoIP) traffic detection approach which can penetrate some important barriers in front of common traffic identification mechanisms: encryption, complexity and camouflage. First we use the information source, channel and destination model in the communication theory to reexamine our understanding of Internet data flows. The packet, the Inter Arrival Time (IAT) between 2 successive packets and the payload length which are only of statistical meanings previously, are now assigned physical properties as pulse, pulse arrival time and the amplitude. The packet-based Internet traffic is therefore transformed to a discrete-time pulse sequence.We then use spectral analysis to exhibit frequency domain behaviors of the general Internet background traffic and some typical VoIP traffics. The result reveals that all kinds of VoIP traffics have their own characteristics (i.e., peaks in certain frequencies) on the power spectrum. Experimental data on simulation as well as real life data set indicates that the approach is effective on recognizing VoIP traffics. We argue that the novel approach complements existing traffic identification mechanisms. The rationale behind it can also be applied to other fields.

Multi-core processors are widely used in computer systems. As the performance of microprocessors greatly exceeds that of memory, the memory wall becomes a limiting factor. It is important to understand how the large disparity of speed between processor and memory influences the performance and scalability of Java applications on emerging multi-core platforms.

Pattern Matching (PM) over network packet flows for Network Intrusion Detection/Prevention System is becoming more and more performance sensitive due to the rapid progress of Internet applications in terms of data volumes. Meanwhile, modern multicore platforms are becoming performance competitive with traditional hardware solutions for PM. But due to the unbalance of network flow sizes, traditional flow- based data parallel processing/programming model can not fully exert multicore platforms' computing power and results in poor performance scalability. In this paper, a novel parallel inspection model, Dynamic Differentiated Distributed Detection (D4) is proposed. D4deploys distributed parallel operations by adding one more dimension on workload partition/allocation. It proposes an effective and efficient scheme to pre-partition the pattern set in several candidate ways, called "Detection Modes", and let multiple candidate PM methods to handle the subsets, respectively; the most suitable Detection Mode would be selected specifically for each incoming flows at the run-time, and the workload would be dynamically allocated among multiple CPU cores. Experimental results on real-world pattern set and traffic traces show that D4scales much better than traditional schemes by better balancing the load among the processors while avoiding unnecessary overheads.

One of the most critical resource management issues in the use of ternary content addressable memory (TCAM) for packet classification/filtering is how to effectively support filtering rules with ranges, known as range matching. In this paper, a Dynamic Range Encoding Scheme (DRES) is proposed to significantly improve TCAM storage efficiency for range matching. Unlike the existing range encoding schemes requiring additional hardware support, DRES uses the TCAM coprocessor itself to assist range encoding. Hence, DRES can be readily programmed in a network processor using a TCAM coprocessor for packet classification. A salient feature of DRES is its ability to allow a subset of ranges to be encoded and hence to have full control over the range code size. This feature allows DRES to exploit the TCAM structure to maximize TCAM storage efficiency. DRES is a comprehensive solution, including a dynamic range selection algorithm, a search key encoding scheme, a range encoding scheme, and a dynamic encoded range update algorithm. While the dynamic range selection algorithm running in software allows optimal selection of ranges to be encoded to maximize the TCAM storage efficiency, the dynamic encoded range update algorithm allows the TCAM database to be updated lock-free without interrupting the TCAM database lookup process. DRES is evaluated based on real-world databases and the results show that DRES can reduce the TCAM storage expansion ratio from 6.20 to 1.23. The performance analysis of DRES based on a probabilistic model demonstrates that DRES significantly improves TCAM storage efficiency for a wide spectrum of range distributions.

Most conventional IPv4-based route lookup algorithms are no more suitable for IPv6 packet forwarding due to the significantly increased 128-bit-long address. However, as a result of lacking of standard IPv6 route databases, it is hard to make benchmarks for the new generation IPv6-based algorithms developing/evaluation. In this paper, based on the studies of initial IPv6 prefix distributions and the associated RFC documents, we originally develop a scalable IPv6 prefix generator, called V6Gene, for IPv6-based route lookup algorithms benchmarking. According to the RFCs and other associated standards, V6Gene generates IPv6 route prefixes from the initially assigned LIR (local Internet registries) prefixes collected from the real world, simulating the process of future IPv6 address block allocation from the LIRs to their subscribers. V6Gene is totally flexible for generation of all kinds of route databases with different characteristics. It is simple for implementation and can be easily integrated within other IPv6 benchmark tools/systems.

Caching, multithreading and the combination of them are the major latency hiding techniques adopted in network processors (NPs). Although they achieve great success in general purpose processors (GPPs), none of them have been well studied under the new context of packet processing. In this paper, we simulate the processing procedure of a four-PE (processing element) network processor and thoroughly evaluate different configurations of these techniques with real-life packet traces. Our major findings include: (1) In general, all of these latency hiding techniques effectively increase the traffic throughput and robustness of NP; but thread allocation policy has great impact on their performance. (2) If assigning packets of the same flow to different threads is allowed, multithreading keeps the PE in a working state as long as possible and less jitter in packet sending rate is resulted than caching schemes; otherwise, a cache with a reasonable size outperforms multithreading in almost all metrics such as traffic throughput, packet loss rate, queuing and total delay. (3) When access latency is comparable to the working time of execution unit, the performance of multithreading is more sensitive to packet arrival process and memory reference pattern than caching. In short, caching and multithreading have their respective advantages under different environment. In some cases, combined caching and multithreading tend to bring more performance gain than simply adding more threads or cache entries.

As one of the critical data path functions for many emerging networking applications, packet classification is gaining more and more concerns nowadays. It is commonly believed that conventional software-based classification algorithms are much more time-consuming than hardware-based solutions, i.e., the costly and power consuming TCAM-based mechanism, and incompetent for future high-end applications. In this paper, we propose an efficient optimization framework which can be applied to "gear up" most exiting software-based packet classification algorithms. Under this framework, the large rule set is pre-partitioned into several small subsets, according to some heuristics and dedicated methods. Then the conventional classification process can be significantly simplified and results in a distinct performance improvement by converging the classification power on only a small portion of the rule set. According to the results of our experiment, in which the framework is applied to one of the best algorithms EGT-PC [2], the memory accesses can even be reduced by up to 70%. This provides a much lower cost and more power-efficient alternative to TCAM-based solutions. Another advantage is that the framework requires no change to the hardware environment and little system cost overhead, making it especially suitable for the modern network processor based network solutions.

The ability to inspect both packet headers and payloads to identify attack signatures makes network intrusion detection system (NIDS) a promising approach to protect Internet systems. Since most of the known attacks can be represented with strings or combinations of multiple substrings, string matching is a key component, as well as the bottleneck in NIDS to address the requirement of constantly increasing capacity. We propose a memory-efficient multiple-character-approaching architecture consisting of multiple parallel deterministic finite automata (DFAs), called TDP-DFA. By employing efficient representations for the transition rules in each DFA, TDP-DFA significantly reduces the complexity. We also present a novel scheme to share the storage of transition rules among multiple DFAs, substantially decreasing the total storage cost, and avoiding the cost increase being proportional to the number of DFAs. We evaluate this design through theoretical analysis and comprehensive experiments. Results show that TDP-DFA is able to meet the critical requirement of OC-768 wirespeed processing, as well as constituting a promising way for scaling up to cope with throughput over 100 Gb/s in the future.

Packet classification has been a critical data path function for many emerging networking applications. An interesting approach is the use of ternary content addressable memory (TCAM) to achieve deterministic, high-speed packet classification performance. However, apart from high cost and power consumption, due to slow growing clock rate for memory technology, in general, the traditional single TCAM-based solution has difficulty to keep up with fast growing line rates. Moreover, the TCAM storage efficiency is largely affected by the need to support rules with ranges or range matching. In this paper, a distributed TCAM scheme that exploits chip-level-parallelism is proposed to greatly improve the throughput performance. This scheme seamlessly integrates with a range encoding scheme which not only solves the range matching problem, but also ensures a balanced high throughput performance. A thorough theoretical worst-case analysis of throughput, processing delay, and power consumption, as well as the experimental results show that the proposed solution can achieve scalable throughput performance matching up to OC768 line rate or higher. The added TCAM storage overhead is found to be reasonably small for the five real-world classifiers studied.

Most existing packet classification algorithms are decision-tree-based, which are easy-implemented and have reasonably good performance. However, due to rule duplication caused by rule overlapping, these solutions always occupy large and indeterministic-sized memory space, making them only be implemented with mass-but-slow SDRAMs and incompetent for future high-end applications. In this paper, by proposing a novel pre-partitioning scheme for the rule set, we eliminate the overlap among the rules and, therefore, avoid rule duplications. We then further develop a parallel packet classification scheme and a quarter-cut decision tree algorithm. The large rule set is distributedly stored in multiple search engines and the classification can be performed in parallel, independently. Much smaller and more deterministic memory requirement is achieved, as well as O(Log(N)) processing delays. Experimental results show that for a policy table with over 1500 rules, only 36 Kbytes memory is required, and averagely 12 memory accesses are needed for each classification.

Packet classification (PC) has been a critical data path function for many emerging networking applications. An interesting approach is the use of TCAM to achieve deterministic, high speed PC However, apart from high cost and power consumption, due to slow growing clock rate for memory technology in general, PC based on the traditional single TCAM solution has difficulty to keep up with fast growing line rates. Moreover, the TCAM storage efficiency is largely affected by the need to support rules with ranges, or range matching. In this paper, a distributed TCAM scheme that exploits chip-level-parallelism is proposed to greatly improve the PC throughput. This scheme seamlessly integrates with a range encoding scheme, which not only solves the range matching problem but also ensures a balanced high throughput performance. Using commercially available TCAM chips, the proposed scheme achieves PC performance of more than 100 million packets per second (Mpps), matching OC768 (40 Gbps) line rate.

One of the key design issues for network processors (NPs) is hiding long latency of random off-chip memory accesses. We present a novel memory subsystem especially for access and edge routers to implement feature-rich network applications with wire-speed processing guarantees. Because of the hierarchical organizations specially designed for network circumstances, access latency of DRAM is totally hidden and the number of off-chip memory accesses can also be reduced. We implement this architecture based on a simplified OpenRISC processor core in an Altera Stratix EP1S20B672 FPGA. Time analysis shows that this memory subsystem achieves an operating frequency of over 200MHz, with approximately 2% LEs and 1% memory resources.

Ternary content-addressable memory (TCAM) is widely used in high-speed route lookup engines. However, restricted by the memory access speed, the route lookup engines for next-generation terabit routers demand exploiting parallelism among multiple TCAMs. Traditional parallel methods always incur excessive redundancy and high power consumption. We propose An original TCAM-based IP lookup scheme that achieves an ultra high lookup throughput and a high utilization of the memory while being power efficient. In our multichip scheme, we devise a load-balanced TCAM table construction algorithm together with an adaptive load balancing mechanism. The power efficiency is well controlled by decreasing the number of TCAM entries triggered in each lookup operation. Using 133 MHz TCAM chips and given 25% more TCAM entries than the original route table, the proposed scheme achieves a lookup throughput of up to 533 Mpps and is simple for ASIC implementation.