Strict Throughput Research Articles

Adaptive Repetition Strategies in IEEE 802.11bd V2X Networks

Vehicle-to-everything (V2X) communications have very strict throughput and latency requirements, even in scenarios with high mobility. IEEE 802.11bd is being developed as a WiFi amendment to improve V2X performance, allowing up to three repetitions per packet, along with other features. Message repetitions increase time diversity and enable maximum ratio combining at the receiver, thus improving the probability of correct decoding. This paper investigates the IEEE 802.11bd packet repetition feature. First, we analyze how the increased channel load due to repetitions may in some cases result in a higher collision rate leading to lower network performance. Then, we propose two strategies for exploiting the IEEE 802.11bd repetition feature. The proposed strategies use the channel busy ratio to adapt the number of transmissions to the channel load and are validated through network-level simulations, which account for both the acquisition and decoding processes. Results show that the proposed strategies improve network performance under variable traffic conditions and enable fair access to the channel.

Enabling Quasi-Static Reconfigurable Networks With Robust Topology Engineering

Many optical circuit switched data center networks (DCN) have been proposed in the last decade to attain higher capacity and topology reconfigurability, though commercial adoption of these architectures have been minimal. One major challenge these architectures face is the difficulty of handling uncertain traffic demands using commercial optical circuit switches (OCS) with high switching latency. Prior works have generally focused on developing fast-switching OCS prototypes to quickly react to traffic variations through frequent reconfigurations. This approach, however, adds tremendous complexity overhead to the control plane, and raises the barrier for commercial adoption of optical circuit switched data center networks. We propose, a robust topology and routing optimization framework for reconfigurable optical circuit switched data centers. co-optimizes topology and routing based on a convex set of traffic matrices, and offers strict throughput guarantees for any future traffic matrices bounded by the convex set. For the bursty traffic demands that are unbounded by the convex set, we employ a desensitization technique to reduce performance hit. This enables to generate topology and routing solutions capable of handling unexpected traffic changes without relying on frequent topology reconfigurations. Our extensive evaluations based on Facebook’s production DCN traces show that, even with daily reconfigurations which could be realized by current commercial MEMS-based OCSs from Calient Technologies, achieves about 20% lower max link utilization, and about 32% lower average hop count compared to cost-equivalent static topologies. Our work shows that adoption of reconfigurable topologies in commercial DCNs is feasible even without fast OCSs.

Performance Analysis of Multi-Band Microwave and Millimeter-Wave Operation in 5G NR Systems

Blockage of millimeter-wave (mmWave) radio propagation paths in dense mobile scenarios requires advanced techniques to preserve session continuity in 5G New Radio (NR) systems. In this work, we employ the tools of stochastic geometry and queuing theory as well as rely on 3GPP cluster-based propagation modeling to formulate a mathematical framework, which captures session-level service dynamics of user equipment (UE) that supports multi-band operation. Accordingly, the sub-6 GHz NR base station (BS) is used to temporarily serve the sessions with strict throughput requirements that experience an outage at the mmWave NR BSs. We derive user- and system-centric key performance indicators, including new and ongoing session drop probabilities as well as radio resource utilization. Our numerical results confirm that the use of microwave BSs to serve mmWave sessions is only feasible in light traffic conditions. Particularly, the presence of throughput-hungry mmWave traffic increases the session drop probability at the sub-6 GHz band as well as decreases its utilization. Further, the support of sub-6 GHz radio may not improve the mmWave BS resource utilization, as many mmWave sessions are dropped during their service as a consequence of frequent blockage-induced outage situations.

Frameworks for Energy Efficiency Maximization in HetNets With Millimeter Wave Backhaul Links

Heterogeneous networks (HetNets) and millimeter wave (mmWave) communications have been recognized as two of the most promising techniques for future cellular networks. HetNets possess the ability to significantly increase network capacity and coverage, while the mmWave bands have an abundant spectrum to support gigabit-per-second data transmission for backhauling. Due to the extreme pathloss and the unreliable transmission of mmWave signals over longer distances, multi-hop mmWave transmissions have been identified as a backhaul (BH) solution in HetNets. On the other hand, energy efficiency (EE) has been identified as a prime design factor for cellular networks because of their rising energy costs. In this paper, two optimization frameworks for maximizing the EE of HetNets with multi-hop mmWave BH links are explored. The first framework, referred to as joint EE, power, and flow control (JEEPF), considers enforcing a strict throughput requirement on all user equipment (UEs) and maximizing the network EE via the joint optimization of power and BH flows. The second framework, referred to as joint EE, power, flow, and throughput (JEEPFT), allows an acceptable range of throughput requirements for each UE and maximizes the network EE via the joint optimization of power, BH flows, and UEs' achievable throughputs. It is observed that this little change (i.e., strict vs. an acceptable range of throughput requirements) causes a drastic difference in the formulations of both problems. The JEEPF simplifies to power minimization problem (which is convex), while the JEEPFT is a ratio of throughput to power (which is fractional and non-convex). Two solution techniques that obtain the optimal solution are proposed for the JEEPFT optimization framework. Simulation results are used to demonstrate the superiority of the JEEPFT framework over the JEEPF and other simple benchmark schemes. The computational complexity of the JEEPFT solution techniques is discussed.

Energy-Efficient High-Throughput VLSI Architectures for Product-Like Codes

Implementing forward error correction (FEC) for modern long-haul fiber-optic communication systems is a challenge, since these high-throughput systems require FEC circuits that can combine high coding gains and energy-efficient operation. We present very large scale integration (VLSI) decoder architectures for product-like codes for systems with strict throughput and power dissipation requirements. To reduce energy dissipation, our architectures are designed to minimize data transfers in and out of memory blocks, and to use parallel noniterative component decoders. Using a mature 28-nm VLSI process technology node, we showcase different product and staircase decoder implementations that have the capacity to exceed 1-Tb/s information throughputs with energy efficiencies of around 2 pJ/b.

Modeling and Analysis of FPGA Accelerators for Real-Time Streaming Video Processing in the Healthcare Domain

Complex real-time video processing applications with strict throughput constraints are commonly found in a typical healthcare application. The video processing chain is implemented as Field-Programmable Gate Array (FPGA) accelerators (processing blocks) communicating through a number of First-In First-Out (FIFO) buffers. The FIFO buffers are made out of Block RAM (BRAM) and limited in availability. Therefore, a key design question is the optimal sizes of the FIFO buffers with respect to the throughput constraint. In this paper, we use model-driven analysis and detailed hardware level simulation to address the question of buffer dimensioning in an efficient way. Using a Cyclo-Static Dataflow (CSDF) model and an optimization method, we identify and optimize the FIFO buffers. The results are confirmed using a detailed hardware level simulation and validated by comparison with VHDL simulations. The technique is illustrated on a use case from Philips Healthcare Image Guided Therapy (IGT) on the imaging pipeline of an Interventional X-Ray (i XR) system.

Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale

A strict throughput requirement has placed a cap on the degree to which we can depend on the execution of single, long, fine spatial grid simulations to explore global atmospheric climate behavior. Alternatively, running an ensemble of short simulations is computationally more efficient. We test the null hypothesis that the climate statistics of a full-complexity atmospheric model derived from an ensemble of independent short simulation is equivalent to that from an equilibrated long simulation. The climate of short simulation ensembles is statistically distinguishable from that of a long simulation in terms of the distribution of global annual means, largely due to the presence of low-frequency atmospheric intrinsic variability in the long simulation. We also find that model climate statistics of the simulation ensemble are sensitive to the choice of compiler optimizations. While some answer-changing optimization choices do not effect the climate state in terms of mean, variability and extremes, aggressive optimizations can result in significantly different climate states.

Response modeling runtime schedulers for timing analysis of self-timed dataflow graphs

Constituent tasks of modern day Embedded Streaming Applications (ESAs), such as engine control systems, multimedia and software defined radios often exhibit execution behaviors that do not conform to conventional task models. ESAs consist of iterative, pipelined sequences of tasks that are conditioned by intra- and inter-iteration dependencies, and often have strict throughput and latency requirements. We model ESAs as dataflow graphs, where actors represent computational units, and directed edges represent communication channels between actors. Due to practical constraints like cost-effectiveness, power consumption and chip-area, multiple ESAs are run on a shared (multi-processor) platform. Thus rigorous timing analysis is required to verify whether individual ESAs meet their respective timing requirements.We look at response modeling, a compositional timing analysis approach wherein the local worst-case influence of runtime scheduling is represented within the constructs provided in dataflow. These local representations (called response models) can be composed together to construct a global understanding of an ESA's worst-case execution which is then used to verify whether its real-time requirements are met. This paper proposes a generic response modeling technique for runtime scheduling of ESAs. We focus on preemptive Fixed Priority Scheduling (FPS) but also demonstrate that we can apply our technique to a wide range of runtime schedulers. In our experiments, we present academic and industrial case-studies that highlight the effectiveness of our approach in the timing analysis of ESAs with unconventional execution behavior.

High speed low power optical detection of sub-wavelength scatterer.

Optical detection of scatterers on a flat substrate, generally done using dark field microscopy technique, is challenging since it requires high power illumination to obtain sufficient SNR (Signal to Noise Ratio) to be able to detect sub-wavelength particles. We developed a bright field technique, based on Fourier scatterometry, with special illumination and detection control to achieve this goal with a power level that can be sustained by most substrates including polymers. The performance of the system in a roll-to-roll line in production environment and strict throughput requirement is shown.

Optimizing latency and throughput of application workflows on clusters

Scheduling, in many application domains, involves optimization of multiple performance metrics. For example, application workflows with real-time constraints have strict throughput requirements and also desire a low latency or response time. In this paper, we present a novel algorithm for the scheduling of workflows that act on a stream of input data. Our algorithm focuses on the two performance metrics, latency and throughput, and minimizes the latency of workflows while satisfying strict throughput requirements. We also describe steps to use the above approach to solve the problem of meeting latency requirements while maximizing throughput. We leverage pipelined, task and data parallelism in a coordinated manner to meet these objectives and investigate the benefit of task duplication in alleviating communication overheads in the pipelined schedule for different workflow characteristics. The proposed algorithm is designed for a realistic bounded multi-port communication model, where each processor can simultaneously communicate with at most k distinct processors. Experimental evaluation using synthetic benchmarks as well as those derived from real applications shows that our algorithm consistently produces lower latency schedules that meet throughput requirements, even when previously proposed schemes fail.

A high-quality digital audio delivery bluetooth platform

The recent growth of home networking applications has raised the requirement for cable-free audio streaming. In this work, the "BlueDAT" digital audio platform is presented, which employs the Bluetooth wireless technology for real-time, CD-like quality, stereo reproduction. It is shown that, despite the strict throughput limitations imposed by the Bluetooth protocol, the BlueDAT application setup can efficiently accommodate cable-free, high-quality audio streaming and playback over one or two receivers for home environments, through the employment of a set of novel mechanisms for the automatic adaptation of both the transmission rate and the audio coding bitrate

Bluebox: a cable-free digital jukebox for compressed-quality audio delivery

The recent growth of home networking applications has raised the requirement for cable-free distribution of an increasing amount of media to portable playback devices, which lack the resources for local storage. In this work, the "BlueBox" wireless digital jukebox application is presented, which employs the Bluetooih wireless technology for distributing on demand, high-quality compressed audio, originally stored in a Bluetooth-enabled digital jukebox to remote portable client devices. It is shown that despite the strict throughput limitations imposed by the early-defined Bluetooth protocols, the BlueBox application setup can efficiently accommodate cable-free, high-quality audio distribution over an adequate number of receivers for home environments.

Precedence-constrained task allocation onto point-to-point networks for pipelined execution

The problem of scheduling directed acyclic task flow graphs to multiprocessor systems using point-to-point networks is examined. An environment where the application has a strict throughput requirement is assumed. Pipelined parallelism is used to meet the throughput requirement. Communication and computation are completely overlapped. Each task and message has a periodic rate and deadline equal to the throughput requirement. A heuristic procedure based on preclustering, recursive mincut bipartitioning, and iterative improvement is proposed to reduce the maximum contention due to communication in the network, increasing the likelihood that messages meet their deadlines. The task assignment procedure takes into account the topology of the multiprocessor system and the distance between communicating tasks.