This paper presents a novel composite phase unit (CPU) designed for an orbital angular momentum (OAM) metalens (ML), featuring a 3D-printed middle metal layer (MML) with non-uniform height. This design reduces slot length variations and mitigates bandwidth shrinkage resulting from passband overlap. It also aligns the frequency response of each CPU, ensuring that peak transmission occurs at the center frequency. The CPU consists of an MML sandwiched between two polarization grids (PGs), forming a quasi-Fabry-Pérot (FP) cavity. A high-resonance metal slot structure within the MML improves the transmission amplitude. To achieve a full 2π phase shift range, the CPU combines propagation, geometric, and resonant phase-control mechanisms. The non-uniform-height MML is fabricated via selective laser melting (SLM) using AlSi10Mg, which demonstrates the capability of 3D printing to fabricate complex structures. Experimental results show that the ML achieves a measured gain of 26.81 dBi at 36 GHz and a 3-dB gain bandwidth of 16.2%, representing significantly improved bandwidth retention relative to the CPU’s 3-dB transmission bandwidth of 12.8%. The proposed OAM metalens demonstrates high mode purity and stable transmission, which enables efficient multichannel wireless communications that are critical for Internet of Things (IoT) applications. This structure significantly improves link capacity, energy efficiency, and system-level simplicity in multichannel IoT systems.

Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC).MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge.To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling.Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema.Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations.Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.

The month’s regular papers issue covers machine learning at the wireless network edge, soft-informationbased localization techniques, and Antenna-in-Package technology.

Antenna-in-package (AiP) technology, in which there is an antenna (or antennas) with a transceiver die (or dies) in a standard surface-mounted device, represents an important antenna and packaging technology achievement in recent years. AiP technology has been widely adopted by chipmakers for 60-GHz radios and gesture radars. It has also found applications in 77-GHz automotive radars, 94-GHz phased arrays, 122-GHz imaging sensors, and 300-GHz wireless links. It is believed that AiP technology will also provide elegant antenna and packaging solutions to the fifth generation and beyond operating in the lower millimeter-wave (mmWave) bands. Thus, one can conclude that AiP technology has emerged as the mainstream antenna and packaging technology for various mmWave applications. This article will provide an overview of the development of AiP technology. It will consider antennas, packages, and interconnects for AiP technology. It will show that the antenna choice is usually based on those popular antennas that can be easily designed for the application, that the package choice is governed for automatic assembly, and that the materials and processes choices involve tradeoffs among constraints, such as electrical performance, thermal-mechanical reliability, compactness, manufacturability, and cost. This article also shows a probe-based setup to measure mmWave AiP impedance and radiation characteristics. It goes on to give AiP examples implemented, respectively, in a low-temperature co-fired ceramic, an embedded wafer level ball grid array process, and a high-density interconnect processes. Finally, this article will summarize and present some recommendations on research topics to further the state of the art of AiP technology.

A detailed examination of evolving traffic characteristics, operator requirements, and network technology trends suggests a move away from nonblocking interconnects in data center networks (DCNs). As a result, recent efforts have advocated oversubscribed networks with the capability to adapt to traffic requirements on-demand. In this paper, we present the design, implementation, and evaluation of OSA, a novel Optical Switching Architecture for DCNs. Leveraging runtime reconfigurable optical devices, OSA dynamically changes its topology and link capacities, thereby achieving unprecedented flexibility to adapt to dynamic traffic patterns. Extensive analytical simulations using both real and synthetic traffic patterns demonstrate that OSA can deliver high bisection bandwidth (60%-100% of the nonblocking architecture). Implementation and evaluation of a small-scale functional prototype further demonstrate the feasibility of OSA.

Modern cloud and data center platforms suffer failures and performance degradation from large traffic surges caused by both external (e.g., DDoS attacks) or internal (e.g., workload changes, operator errors, routing misconfigurations) factors. If not mitigated, traffic overload could have significant financial and availability implications for cloud providers. In this paper, we propose NetFuse, a mechanism to protect against traffic overload in OpenFlow-based data center networks. NetFuse is (1) scalable because it uses passively-collected OpenFlow control messages to detect active network flows; (2) accurate because it uses multi-dimensional flow aggregation to determine the right criteria to combine network flows that lead to overloading behavior; and (3) effective in limiting the damage of surges while not affecting the normal traffic because it uses a toxin-antitoxinlike mechanism to adaptively shape the rate of the flow based on application feedback. Experimental results on a real OpenFlow testbed show that NetFuse is effective in identifying and isolating misbehaving traffic with a small false positive rate (<; 9%).

Multi-tenant data centers are complex environments, running thousands of applications that compete for the same infrastructure resources and whose behavior is guided by (sometimes) divergent configurations. Small workload changes or simple operator tasks may yield unpredictable results and lead to expensive failures and performance degradation. In this paper, we propose a holistic approach for detecting operational problems in data centers. Our framework, FlowDiff, collects information from all entities involved in the operation of a data center -- applications, operators, and infrastructure -- and continually builds behavioral models for the operation. By comparing current models with pre-computed, known-to-be-stable models, FlowDiff is able to detect many operational problems, ranging from host and network failures to unauthorized access. FlowDiff also identifies common system operations (e.g., VM migration, software upgrades) to validate the behavior changes against planned operator tasks. We show that using passive measurements on control traffic from programmable switches to a centralized controller is sufficient to build strong behavior models; FlowDiff does not require active measurements or expensive server instrumentation. Our experimental results using NEC data center testbed, Amazon EC2, and simulations demonstrate that FlowDiff is effective and robust in detecting anomalous behavior. FlowDiff scales well with the number of applications running in the data center and their traffic volume.

With the ever-increasing number and complexity of applications deployed in data centers, the underlying network infrastructure can no longer sustain such a trend and exhibits several problems, such as resource fragmentation and low bisection bandwidth. In pursuit of a real-world applicable cloud network (CN) optimization approach that continuously maintains balanced network performance with high cost effectiveness, we design a topology independent resource allocation and optimization approach, NetDEO. Based on a swarm intelligence optimization model, NetDEO improves the scalability of the CN by relocating virtual machines (VMs) and matching resource demand and availability. NetDEO is capable of (1) incrementally optimizing an existing VM placement in a data center; (2) deriving optimal deployment plans for newly added VMs; and (3) providing hardware upgrade suggestions, and allowing the CN to evolve as the workload changes over time. We evaluate the performance of NetDEO using realistic workload traces and simulated large-scale CN under various topologies.

Driven by the large-scale growth of applications deployment in data centers and complicated interactions between service components, automated application dependency discovery becomes essential to daily system management and operation. In this paper, we present ADD, which extracts dependency paths for each application by decomposing the application-layer connectivity graph inferred from passive network monitoring data. ADD utilizes a series of statistical techniques and is based on the combination of global observation of application traffic matrix in the data center and local observation of traffic volumes at small time scales on each server. Compared to existing approaches, ADD is especially effective in the presence of overlapping and multi-hop applications and resilient to data loss and estimation errors.

With the ever-increasing number and complexity of applications deployed in data centers, the underlying network infrastructure can no longer sustain such a trend and exhibits several problems, such as resource fragmentation and low bisection bandwidth. In pursuit of a real-world applicable data center network (DCN) optimization approach that continuously maintains balanced network performance with high cost effectiveness, we design a topology independent resource allocation and optimization approach, NetDEO. Based on a swarm intelligence optimization model, NetDEO improves the scalability of the DCN by relocating virtual machines (VMs) and matching resource demand and availability. NetDEO is capable of (1) incrementally optimizing an existing VM placement in a data center; (2) deriving optimal deployment plans for newly added VMs; and (3) providing hardware upgrade suggestions and allowing the DCN to evolve as the workload changes over time. We evaluate the performance of NetDEO using realistic workload traces and simulated large-scale DCN under various topologies.

Full-bandwidth connectivity between all servers of a data center may be necessary for all-to-all traffic patterns, but such interconnects suffer from high cost, complexity, and energy consumption. Recent work has argued that if all-to-all traffic is uncommon, oversubscribed network architectures that can adapt the topology to meet traffic demands, are sufficient. In line with this work, we propose Proteus, an all-optical architecture targeting unprecedented topology-flexibility, lower complexity and higher energy efficiency.

Network tomography has been proposed to ascertain internal network performances from end-to-end measurements. In this work, we present priority probing, an optimal probing scheme for unicast network delay tomography that is proven to provide the most accurate estimation. We first demonstrate that the Fisher information matrix in unicast network delay tomography can be decomposed into an additive form where each term can be obtained numerically. This establishes the space over which we can design the optimal probing scheme. Then, we formulate the optimal probing problem into a semi-definite programming (SDP) problem. High computation complexity constrains the SDP solution to only small scale scenarios. In response, we propose a greedy algorithm that approximates the optimal solution. Evaluations through simulation demonstrate that priority probing effectively increases estimation accuracy with a fixed number of probes.

In recent years, data center network (DCN) architectures (e.g., DCell, FiConn, BCube, FatTree, and VL2) received a surge of interest from both the industry and academia. However, none of existing efforts provide an in-depth understanding of the impact of these architectures on application performance in practical multi-tier systems under realistic workload. Moreover, it is also unclear how these architectures are affected in virtualized environments. In this paper, we fill this void by conducting an experimental evaluation of FiConn and FatTree, each respectively as a representative of hierarchical and flat architectures, in a three-tier transaction system using virtual machine (VM) based implementation. We observe several fundamental characteristics that are embedded in both classes of network topologies and cast a new light on the implication of virtualization in DCN architectures. Issues observed in this paper are generic and should be properly addressed by any DCN architectures before being considered for actual deployment, especially in mission-critical real-time transaction systems.

In the recent years, a progressively growing number of computing and communication services have undertaken the migration from their conventional media to the new unified platform, IP networks. As a consequence, business success of service providers becomes largely determined by the effectiveness of their service management schemes, which require rapid identification of problems and resolution of network-related anomalies. However, this is a non-trivial task in heterogeneous enterprise networks due to service providers' invisibility of the health and performance of the underlying carrier network. In addition, the gap between quality of service (QoS) measurements reflecting network performance and quality of experience (QoE) metrics indicating user-perceived service quality further makes effective service management more challenging. In this paper, we present a unified service management system called QoEScope, which combines scalable end-to-end probing, accurate topology inference in the presence of implicit routers, adaptive bridging between QoS measurement and QoE metrics, and intelligent root cause analysis. Extensive testbed emulations and Internet experiments demonstrate that QoEScope is a highly practical and effective IP service management solution for heterogeneous enterprise networks.

Characterizing user churn has become an important topic in studying P2P networks, both in theoretical analysis and system design. Recent work has shown that direct sampling of user lifetimes may lead to certain bias (arising from missed peers and round-off inconsistencies) and proposed a technique that estimates lifetimes based on sampled residuals. In this paper, however, we show that under non-stationary arrivals, which are often present in real systems, residual-based sampling does not correctly reconstruct user lifetimes and suffers a varying degree of bias, which in some cases makes estimation completely impossible. We overcome this problem using two contributions: a novel non-stationary ON/OFF churn model and an unbiased randomized residual sampling technique for measuring user lifetimes. The former allows correlation between ON/OFF periods of the same user and exhibits different join rates during the day. The latter spreads sampling points uniformly during the day and uses a novel estimator to reconstruct the underlying lifetime distribution. We finish the paper with experimental measurements of Gnutella and discussing reduction in overhead compared to direct sampling methods.

This paper presents a frame-level hybrid framework for modeling MPEG-4 and H.264 multi-layer variable bit rate (VBR) video traffic. To accurately capture long-range-dependent and short-range-dependent properties of VBR sequences, we use wavelets to model the distribution of I-frame sizes and a simple time-domain model for P/B frame sizes. However, unlike previous studies, we analyze and successfully model both inter-GOP (group of pictures)andintra-GOP correlation in VBR video and build an enhancement-layer model using cross-layer correlation. Simulation results demonstrate that our model effectively preserves the temporal burstiness and captures important statistical features (e.g., the autocorrelation function and the frame-size distribution) of original traffic. We also show that our model possesses lower complexity and has better performance than the previous methods in both single- and multi-layer sequences.

Innovative efforts to provide a clean-slate design of congestion control for future high-speed heterogeneous networks have recently led to the development of explicit congestion control. These methods (N. Dukkipati, et al., Jun. 2005), (S. Jain, et al., June 2007), (D. Katabi, et al., Aug. 2002), (Y. Zhang, et al., April 2006) rely on multi-byte router feedback and aim to contribute to the design of a more scalable Internet of tomorrow. However, experimental evaluation and deployment experience with these approaches, especially in high- capacity networks and multi-link settings, is still missing from the literature. This paper aims to fill this void and investigate the behavior of these methods in single and multi-link topologies involving real systems and gigabit networks. We implement four recent protocols XCP (D. Katabi, et al., Aug. 2002), JetMax (Y. Zhang, et al., April 2006), RCP (N. Dukkipati, et al., Jun. 2005) and PIQI-RCP (S. Jain, et al., June 2007) in the existing Linux TCP/IP stack (Linux) in a manner that is transparent to applications and conduct experiments in Emulab using a variety of network configurations. Our experiments not only confirm the known behavior of these methods, but also demonstrate their previously undocumented properties.

Most existing criteria [3], [5], [8] for sizing router buffers rely on explicit formulation of the relationship between buffer size and characteristics of Internet traffic. However, this is a non-trivial, if not impossible, task given that the number of flows, their individual RTTs, and congestion control methods, as well as flow responsiveness, are unknown. In this paper, we undertake a completely different approach that uses control- theoretic buffer-size tuning in response to traffic dynamics. Motivated by the monotonic relationship between buffer size and loss rate and utilization, we design a mechanism called Adaptive Buffer Sizing (ABS), which is composed of two Integral controllers for dynamic buffer adjustment and two gradient-based components for intelligent parameter training. We demonstrate via ns2 simulations that ABS successfully stabilizes the buffer size at its minimum value under given constraints, scales to a wide spectrum of flow populations and link capacities, exhibits fast convergence rate and stable dynamics in various network settings, and is robust to load changes and generic Internet traffic (including FTP, HTTP, and non-TCP flows). All of these demonstrate that ABS is a promising mechanism for tomorrow's router infrastructure and may be of significant interest for the ongoing collaborative research and development efforts (e.g., GENI and FIND) in reinventing the Internet.

In this paper, we show that end-host based congestion prediction is more accurate than previously characterized. However, it may not be possible to entirely eliminate the uncertainties in congestion prediction. To address these uncertainties, we propose Probabilistic Early Response TCP (PERT). PERT emulates the behavior of AQM/ECN, in the congestion response function of end-hosts. We present fluid-flow analysis of PERT/RED and PERT/PI, versions of PERT that emulate router-based RED and PI controllers. Our analysis shows that PERT/RED has better stability behavior than router-based RED. We also present results from ns-2 simulations to show the practical feasibility of PERT. The scheme presented here is general and can be used for emulating other AQM algorithms.

Abstract — Recent surge of interest towards congestion control that relies on single-router feedback (e.g., XCP [12], RCP [1], [5], MaxNet [24], EMKC [28], VCP [26]) suggests that such systems may offer certain benefits over traditional models of additive packet loss [13]. Besides topology-independent stability and faster convergence to efficiency/fairness [24], it was recently shown [28] that any stable single-router system with a symmetric Jacobian tolerates arbitrary fixed, as well as time-varying, feedback delays. Although delay-independence is an appealing characteristic, the EMKC system developed in [28] exhibits undesirable equilibrium properties and slow convergence behavior. To overcome these drawbacks, we propose a new method called JetMax and show that it admits a low-overhead implementation inside routers (three additions per packet), overshoot-free transient and steady state, tunable link utilization, and delay-insensitive flow dynamics. The proposed framework also provides capacity-independent convergence time, where fairness and utilization are reached in the same number of RTT steps for a link of any bandwidth. Given a 1 mb/s, 10 gb/s, or googol (10 100) bps link, the method converges to within 1 % of the stationary state in 6 control intervals. We finish the paper by comparing JetMax’s performance to that of existing methods in ns2 simulations and discussing its Linux implementation.

Video streaming is becoming an increasingly important part of the present Internet. To guarantee a high-quality streaming environment to end users, many video applications require a strict form of network QoS that is not available in the present Internet. Thus, to supplement the best-effort model of existing networks, we study a new video streaming framework that allows applications to mark their own packets with different priority and use multiqueue congestion control inside routers to effectively drop the less-important packets during buffer overflows. We describe priority AQM algorithms that provide "optimal" performance to video applications under arbitrary network loss and study a variation of Kelly's congestion control in combination with our framework. We call the combined architecture PELS - partitioned enhancement layer streaming.

In this paper, we analyze how feedback delays a#ect stability, oscillations, and packet loss of several classes of congestion controllers used or proposed for video streaming in the current/future Internet (including window-based AIMD, rate-based AIMD, Scalable TCP, and TFRC). Our results indicate that window-based protocols in this list incur significantly less packet loss under delayed feedback than their rate-based counterparts, which explains their better overall performance observed in practice [12]. At the same time, we show that even TCP's congestion control is far from ideal from the control-theoretic point of view and leads to amplified oscillations when queuing delays increase. We conclude with an observation that multimedia in the future Internet is not likely to enjoy oscillation-free congestion control unless the network deploys some form of AQM.

Recent research efforts to design better Internet transport protocols combined with scalable Active Queue Management (AQM) have led to significant advances in congestion control. One of the hottest topics in this area is the design of discrete congestion control algorithms that are asymptotically stable under heterogeneous feedback delay and whose control equations do not explicitly depend on the RTTs of end-flows. In this paper, we show that max-min fair congestion control methods with a stable symmetric Jacobian remain stable under arbitrary feedback delay (including heterogeneous directional delays) and that the stability condition of such methods does not involve any of the delays. To demonstrate the practicality of the obtained result, we change the original controller in Kelly's work [14] to become robust under random feedback delay and fixed constants of the control equation. We call the resulting framework Max-min Kelly Control (MKC) and show that it offers smooth sending rate, exponential convergence to efficiency, and fast convergence to fairness, all of which make it appealing for future high-speed networks.

Balancing peer-to-peer graphs, including zone-size distributions, has recently become an important topic of peer-to-peer (P2P) research [1], [2], [6], [19], [31], [36]. To bring analytical understanding into the various peer-join mechanisms, we study how zone-balancing decisions made during the initial sampling of the peer space affect the resulting zone sizes and derive several asymptotic results for the maximum and minimum zone sizes that hold with high probability.