Low Delay Performance Research Articles

Deep learning personalized recommendation-based construction method of hybrid blockchain model

This study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.

PolarDB-IMCI: A Cloud-Native HTAP Database System at Alibaba

Cloud-native databases have become the de-facto choice for mission-critical applications on the cloud due to the need for high availability, resource elasticity, and cost efficiency. Meanwhile, driven by the increasing connectivity between data generation and analysis, users prefer a single database to efficiently process both OLTP and OLAP workloads, which enhances data freshness and reduces the complexity of data synchronization and the overall business cost. In this paper, we summarize five crucial design goals for a cloud-native HTAP database based on our experience and customers' feedback, i.e., transparency, competitive OLAP performance, minimal perturbation on OLTP workloads, high data freshness, and excellent resource elasticity. As our solution to realize these goals, we present PolarDB-IMCI, a cloud-native HTAP database system designed and deployed at Alibaba Cloud. Our evaluation results show that PolarDB-IMCI is able to handle HTAP efficiently on both experimental and production workloads; notably, it speeds up analytical queries up to ×149 on TPC-H (100GB). PolarDB-IMCI introduces low visibility delay and little performance perturbation on OLTP workloads (<5%), and resource elasticity can be achieved by scaling out in tens of seconds.

Analysis of Spatio-temporal Behavior of Research Travelers Based on Spark Big Data Computing Model

There are still many defects in the process of data processing, storage and analysis, which cannot adapt to the needs of big data processing and analysis. In order to more fully reflect the value of big data processing, it is necessary to realize efficient data processing. Therefore, people invented the Spark operating system and integrated the idea of SIMBA, built a big data analysis system, and searched the information through the database query method. In Spark operating system can import search mechanism, in order to improve the search effect of information, through the establishment of segment tree storage method can improve the retrieval effect, in big data analysis can use partition strategy method for big data partition, and adopted the global filtering and local search method, make in information retrieval can achieve high throughput and low delay performance.

Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network.

The precise assessment of cognitive load during a learning phase is an important pathway to improving students’ learning efficiency and performance. Physiological measures make it possible to continuously monitor learners’ cognitive load in remote learning during the COVID-19 outbreak. However, maintaining a good balance between performance and computational cost is still a major challenge in advancing cognitive load recognition technology to real-world applications. This paper introduced an adaptive feature recalibration (AFR) convolutional neural network to overcome this challenge by capturing the most discriminative physiological features (EEG and eye-tracking). The results revealed that the optimal average classification accuracy of the feature combination obtained by the AFR method reached 95.56% with only 60 feature dimensions. Additionally, compared with the best result of the conventional correlation-based feature selection (CFS) method, the introduced AFR algorithm achieved higher accuracy and cheaper computational cost, as well as a 2.06% improvement in accuracy and a 51.21% reduction in feature dimension, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.Graphical abstract

Layout Design of Row Decoder using Cadence

Abstract: Logic circuits, data transport circuits, and analogue to digital conversions all frequently employ decoders. For the line decoders, a mixed logic design approach incorporating transmission gate logic, pass transistor logic, and complementary metaloxide semiconductor (CMOS) technology is employed to achieve the desired performance and operation. A unique topology is proposed for the 2 to 4 decoders, which calls for a topology with fourteen transistors to reduce operating power and transistor count and a topology with fifteen transistors to achieve high power and low delay performance. For a total of four new designs, standard and inverting decoders are created for each situation. All of the suggested decoders have a low transistor count in comparison to their traditional CMOS architectures. Last but not least, a number of suggested solutions demonstrate a notable improvement in operating power and propagation latency, exceeding CMOS in virtually all instances

A lightweight and accurate double-branch neural network for four-class motor imagery classification

Deep learning is an important pathway for investigation of motor imagery signal classification. Nevertheless, maintaining a good compromise between performance and computational cost has been a major challenge in developing deep models for decoding motor imagery EEG. In this paper, a novel shallow double-branch convolutional neural network (DSCNN) is proposed for four-class motor imagery classification. The proposed CNN adopts parallel extraction of two branches to improve classification accuracy. Meanwhile, in order to constrain the depth of the whole network, the left branch only contained two single temporal and spatial convolutional layers to extract common EEG features. Similarly, the right branch first introduced 1D convolution to exploit the channel dependency and temporal features across multiple time-scales, secondly a depth-wise separable convolutional layer was applied for optimizing EEG signal series. Then the feature representation for final classification was obtained by merging intermediate features extracted from the two branches. Also, the DSCNN is an end-to-end decoder, as it employs the raw EEG data as inputs and does not require additional complex preprocessing. The proposed model was evaluated on public benchmark BCI competition IV dataset 2a and achieved in terms of accuracy is 85% and kappa value is 0.79. Compared with other state-of-the-art algorithms, the experiment results reveal that the DSCNN has higher decoding accuracy and robustness, as well as a 10% improvement in accuracy than the single general shallow model. Furthermore, as a lightweight architecture, the DSCNN relies on a lower computational power than similar mainstream models, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.

Performance Analysis of NOMA Downlink for Next- Generation 5G Network with Statistical Channel State Information

Non-orthogonal multiple access (NOMA) is a compelling strategy that helps wireless networks of the fifth-generation (5G) to fulfill the diverse demands of increased fairness, high reliability, extensive connectivity, low delay and superior performance. Traditionally, NOMA network presumes perfect transmitter-side channel state information (CSI), which is almost impossible for a number of communication scenarios. Furthermore, this paper contemplates the realistic NOMA downlink method in the Rayleigh fading networks, where the statistical CSI linked with each user is known to the transmitter. We evaluate the outage probability of the proposed model and obtain a closed-form framework of the probability of outage for each user. Besides, the source can optimize the network’s achievable sum-rate for different users based on statistical CSI. The precision of our outage probability analysis and the optimum power allocation algorithm proposed are both verified by the analytical and simulation results.

Receiver Designs for Low-Latency HF Communications

High frequency (HF) radio communications offer latency advantages over fiber-optics in certain long-distance communication applications. As a result, there is interest in using HF for high-frequency trading (HFT), to make trans-continental market decisions that leverage the lower latency path of HF channels. The HF skywave channel is a dynamic medium and is one of the most challenging communication channels. Latencies in existing HF modems from coding and interleaving over several packets are prohibitive for HFT, which motivates the development of receivers that provide good performance given a delay constraint. In this paper, we present receiver designs that can be employed for low-latency reliable communication. We introduce a multitrellis adaptive Viterbi algorithm (MAVA) for sparse and time-varying intersymbol interference (ISI) channels, where the conventional Viterbi algorithm would lead to a large trellis. While multitrellis receiver designs have previously been considered, MAVA also incorporates real-time channel tracking, which is necessary for a practical HF receiver. We combine the MAVA algorithm with maximum a posteriori (MAP) detection to obtain a hybrid receiver with high-fidelity, low-delay performance. Finally, we conduct an asymptotic efficiency analysis of the hybrid receiver to gain insight into how deferring decisions improves performance in the high signal-to-noise ratio (SNR) regime.

A LOW-DELAY AND ROBUST SOLUTION FOR RAPID MOVEMENT DETECTION USING UWB

Abstract. The increasing applications for location-based services (LBS) and real-time location system (RTLS) promote the development of indoor positioning and navigation technology. However, the positioning methods based on traditional Radio Frequency (RF) signals are difficult to improve the accuracy and sensitivity for rapid movement detection with a low latency parameter. In order to solve those problems, this paper adopted ultra-wideband (UWB) chips as the wireless communication technology and designed a method of rapid movement detection with low delay. The method consists of a topology of combined two way ranging (C-TWR), a schedule of time division multiple access (TDMA), an non-light-of-sight (NLOS) handling mechanism and an adaptive extended Kalman filter (A-EKF). To examine the low delay performance, accuracy and robustness of the system, a specific electro-displacement actuator with programmable speed is applied to provide precise ground truth for real speed and distance, and a system test platform consisting of four anchors and four tags provides information of specific situation. The results show that the system can locate the rapid moving pedestrian with a maximal speed of approximately 5 m s−1 in real-time with a positioning accuracy of less than 0.5 m. There are profound implications for further studies in the range of human wearable tags.

Design and Implementation of High Performance and Low Power Mixed Logic Line Decoders

The decoders are widely used in the logical circuits, data transfer circuits and analog to digital conversions. A mixed logic design methods for the line decoders are used to combining the transmission gate logic, pass transistor logic, and complementary metal-oxide semiconductor (CMOS) technology provides desired operation and performance. A novel topology is presented for the 2 to 4 decoder requires a fourteen transistor topology aiming on reducing the transistor count and operating power and a fifteen transistor topology aiming on high power and low delay performance. The standard and inverting decoders are designed in each of the case, gives a total of four new designs circuits. All the proposed decoders have compact transistor count compared to their conservative CMOS technologies. Finally, a variety of proposed designs present a noteworthy improvement in operating power and propagation delay, outperforming CMOS in almost all the cases.

Delay-Optimal Scheduling of VMs in a Queueing Cloud Computing System with Heterogeneous Workloads

This paper studies virtual machine (VM) scheduling in a queueing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs’ delay requirements. The delay-optimal VM scheduling in such a cloud computing system is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queueing model that buffers the same type of VM jobs in one virtual queue. The queueing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. A min-min best fit (MM-BF) policy is used to schedule the jobs in different queues to minimize the remaining system resources, while a shortest-job-first (SJF) policy is used to buffer the job requests in each queue based on their job lengths in an ascending order. To avoid job starvation for the long-duration jobs in SJF-MMBF, we further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. Simulation results show that, SJF-MMBF and SJF-QMW achieve low delay performance in terms of average job completion time and high throughput performance in terms of job hosting ratio.

Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

To overcome devices' limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users' power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are re-associated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers' computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale while a dynamic task offloading and resource allocation policy is executed in the short timescale. Simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly-reliable task computation and lower delay performance, compared to several baselines.

The priority assignment of messages effects on delay performance in VANET

Low delay performance between neighbor vehicles in VANT need to an indepth research. In this paper, we conceived a novel priority assignment plan to reduce transmission delay. A priority was assigned to each message based on static and dynamic factors and size of the message. Dynamic scheduling sequence was carried out based on the messages priority assigned. The performance of proposed method was analysed in highway scenario on the average delay and waiting delay in queue. Simulation results are consistent with theoretical deriving. Therefore, low latency environments can be provided in Vehicular Ad-hoc Network using the novel plan.

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

The vehicular edge computing (VEC) system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states and computing workloads. These uncertainties bring extra challenges to task offloading. In this work, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning-based task offloading (ALTO) algorithm based on the multi-armed bandit (MAB) theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.

Improved Delay Performance in VANET by the Priority Assignment

Low delay performance in VANT need to an intensive study. In this article, we proposed a novel priority method to reduce transmission delay of one-hop in VANET. A priority was marked to each message based on static factors, dynamic factors and size of the message. Messages scheduling was implemented based on the priority of messages. The performance of proposed method was analysed in highway scenario on the average delay and waiting delay in queue. Simulation results are consistent with theoretical deriving. Therefore, by using the novel method, low delay and more reliable communication scenarios can be provided in Vehicular Ad-hoc Network.

Online and Scalable Unsupervised Network Anomaly Detection Method

Nowadays, network intrusion detectors mainly rely on knowledge databases to detect suspicious traffic. These databases have to be continuously updated which requires important human resources and time. Unsupervised network anomaly detectors overcome this issue by using “intelligent” techniques to identify anomalies without any prior knowledge. However, these systems are often very complex as they need to explore the network traffic to identify flows patterns. Therefore, they are often unable to meet real-time requirements. In this paper, we present a new online and real-time unsupervised network anomaly detection algorithm (ORUNADA). Our solution relies on a discrete time-sliding window to update continuously the feature space and an incremental grid clustering to detect rapidly the anomalies. The evaluations showed that ORUNADA can process online large network traffic while ensuring a low detection delay and good detection performance. The experiments performed on the traffic of a core network of a Spanish intermediate Internet service provider demonstrated that ORUNADA detects in less than half a second an anomaly after its occurrence. Furthermore, the results highlight that our solution outperforms in terms of true positive rate and false positive rate existing techniques reported in the literature.

Ionic Liquid and Biofuel Blend: A Low–cost and High Performance Hypergolic Fuel for Propulsion Application

AbstractHypergolic fuel has unique importance in missiles and satellite launch vehicles. Hydrazine and its derivatives (Monomethyl hydrazine, MMH and Unsymmetrical Dimethyl Hydrazine, UDMH) are the continuous choices of fuel in bipropellant though it is carcinogenic and toxic. Development of new, green and cost‐effective fuel blends based on trust‐worthy, ionic liquid and biofuel in different volume proportion have been reported. The blends exhibit reasonably good stability (chemical and thermal), low viscosity (η < 15 mPa⋅s), high density (ρ > 1 g/cm3), low ignition delay and high performance in comparison with UDMH ‐ a proven liquid fuel presently being used as promising fuels for liquid rocket application. The reported blends appear to be a technologically promising, eco‐friendly and affordable fuel blend to replace currently used liquid toxic fuels.

Performance evaluation of a large-scale optical switch based on an arrayed waveguide grating router and wavelength converter

A scheduling scheme for a large-scale optical switch is presented and its performance is evaluated. The optical switch is built on an arrayed waveguide grating router (AWGR) and space switching enabled tunable wavelength converter (SS-TWC) and in a Clos switch structure. The SS-TWC has more than one output and in the switch structure enables a multiple fold expansion of the switch port count while using the same dimension AWGR. The switch reconfiguration is triggered by an event, e.g., packet arriving or input port transmitter available for its waiting packets, and the scheduling scheme operates asynchronously. Simulation results demonstrate that the traffic scheduling scheme enables the delivery of high-transmission bandwidth and low packet delay performance, and verify the nonblocking property of the switch.

Modeling Dynamic Adaptive Streaming Over Information-Centric Networking

The dynamic adaptive streaming technique flexibly adapts the video bit-rate to link fluctuations, which can improve the quality of experience (QoE). In this paper, we present a systematic framework of video streaming in the context of information-centric networking, in order to facilitate the large-scale deployment of the dynamic adaptive streaming technique. Specifically, we design the network as a two-layer coordinating structure, namely, the control layer and the transmission layer. The control layer employs the statistical data recorders to record the variations of the video popularity, link states, and user demands. On the other side, the network forwards user requests and caches data packets in the transmission layer, based on the statistical data which is obtained in the control layer. In addition, the network executes the real-time monitoring of link conditions in the transmission layer, and adjusts the video bit-rate accordingly. Under the above feedback circumstance, we first develop a distributed algorithm of joint dynamic forwarding and caching to theoretically maximize the total user demand rates within the network stability region. Then, we modify the distributed algorithm with a practical caching strategy to make the system applicable to real scenarios. Simulation results show the superior performance of the modified distributed algorithm in terms of low user delay and high QoE performance.

A high performance long-reach passive optical network with a novel excess bandwidth distribution scheme

Long-reach passive optical networks (LR-PONs) have been considered to be promising solutions for future access networks. In this paper, we propose a distributed medium access control (MAC) scheme over an advantageous LR-PON network architecture that reroutes the control information from and back to all ONUs through an (N+1)×(N+1) star coupler (SC) deployed near the ONUs, thereby overwhelming the extremely long propagation delay problem in LR-PONs. In the network, the control slot is designed to contain all bandwidth requirements of all ONUs and is in-band time-division-multiplexed with a number of data slots within a cycle. In the proposed MAC scheme, a novel profit-weight-based dynamic bandwidth allocation (P-DBA) scheme is presented. The algorithm is designed to efficiently and fairly distribute the amount of excess bandwidth based on a profit value derived from the excess bandwidth usage of each ONU, which resolves the problems of previously reported DBA schemes that are either unfair or inefficient. The simulation results show that the proposed decentralized algorithms exhibit a nearly three-order-of-magnitude improvement in delay performance compared to the centralized algorithms over LR-PONs. Moreover, the newly proposed P-DBA scheme guarantees low delay performance and fairness even when under attack by the malevolent ONU irrespective of traffic loads and burstiness.