Optimal Throughput Research Articles

Improved 2D hydride detection for NMR-chemosensing via p‐H2 Hyperpolarization

2D NMR zero-quantum spectroscopy offers a robust and convenient way to resolve hydride resonances in non-hydrogenative ParaHydrogen Induced Polarization experiments. This approach has been recently applied to the detection and quantification of dilute components in biofluids and natural extracts. For certain classes of analytes, however, modulation of the zero-quantum coherence occurs at several kiloHertz frequency, which determines long measurement times for attaining the desired resolution in the indirect dimension. Here, we propose an alternative 2D approach to measure high-resolution NMR spectra that affords enhanced sensitivity and reduced experimental time for optimal sample throughput.

Design and experimental evaluation of algorithms for optimizing the throughput of dispersed computing

We introduce three optimized scheduling algorithms for dispersed computing and present JupiterTP, a real-world system built on k8s and the prior Jupiter system, enabling end-to-end computation on distributed clusters. Distinguishing itself from traditional throughput optimization approaches that focus on theory and simulations, our work is the first implementation of such an end-to-end system capable of handling arbitrary DAGs across diverse computing networks, including public clouds, IoT systems, and edge networks. Beyond mere scheduling, JupiterTP integrates profilers, execution, and orchestration engines, offering unified interfaces for additional scheduling algorithm integrations. The system's performance is tested on real clusters and real applications, compared to prior work that relied on simulations alone. We make JupiterTP available to the community as open-source software at https://github.com/ANRGUSC/JupiterTP.

TOLEDO: enhancing Maestro GUI for non-expert users to perform massive MD simulations

Classical Molecular Dynamics (MD) simulates the dynamical evolution of biological systems at the atomic level. Using MD in conjunction with high-performance computing (HPC) architectures, we can evaluate the possible interactions between a ligand library against one protein target to find a drug that can influence a protein target to cure a disease. Simultaneously, we can also obtain information about their dynamic evolution. One of the primary software packages for MD simulations is Desmond, which employs Maestro for the setup, execution, and analysis of MD through a graphical user interface (GUI), which is suitable even for non-expert users. However, using the GUI, users can typically run only one short (less than 1000 ns) MD each time. Our work aims to create a method/protocol to run several MD simulations simultaneously on a remote HPC cluster within Maestro-Desmond. In this work, we provide TOLEDO (Throughput Optimization of Ligand-Protein Systems Exploration through Dynamics simulation in Optimized HPC systems) to overcome such limitations and run several MD simulations simultaneously. The best feature of TOLEDO is its independence from the usual time constraints of many clusters, with storage space being the only limitation. To run TOLEDO, we prepare/set up the protein-ligand complex before running MD via Maestro GUI. Next, we run the main TOLEDO script for several MD simulations on a supercomputer. When TOLEDO finishes, users obtain reports and graphics. The obtained results are easily interpretable. In essence, TOLEDO significantly enhances MD throughput beyond the capabilities of the Maestro GUI.

FOS: A fully integrated open-source program for Fast Optical Spectrum calculations of nanoparticle media

FOS, which means light in Greek, is an open-source program for Fast Optical Spectrum calculations of nanoparticle media. This program takes the material properties and a description of the system as input, and outputs the spectral response including the reflectance, absorptance, and transmittance. Previous open-source codes often include only one portion of what is needed to calculate the spectral response of a nanoparticulate medium, such as Mie theory or a Monte Carlo method. FOS is designed to provide a convenient fully integrated format to remove the barrier as well as providing a significantly accelerated implementation with compiled Python code, parallel processing, and pre-trained machine learning predictions. This program can accelerate optimization and high throughput design of optical properties of nanoparticle or nanocomposite media, such as radiative cooling paint and solar heating liquids, allowing for the discovery of new materials and designs. FOS also enables convenient modeling of lunar dust coatings, combustion particulates, and many other particulate systems. In this paper we discuss the methodology used in FOS, features of the program, and provide four case studies.

TCP BBR-n interplay with modern AQM in Wireless-N/AC networks: Quest for the golden pair.

Effective congestion control on the internet has been a problem since its inception. Transmission Control Protocol (TCP), being the most widely used transport layer protocol tries to mitigate it using a variety of congestion control algorithms. Cubic, Reno, and Bottleneck Bandwidth and Round-trip propagation time (BBR) are the most deployed congestion controls. BBR v2 is leading the congestion control race with its superior performance in terms of better throughput and lower latency. Furthermore, Active Queue Management (AQM) algorithms try to mitigate the congestion control at the network layer through active buffer control to avoid bufferbloat. The most efficient congestion control occurs when TCP and AQM work together. Indeed, it is the TCP-AQM algorithm "Golden pair" that can result in the most efficient performance. This paper proposes such a novel pair based on our previously tested and published BBR-n (BBR new) with the most effective of the modern AQMs, that completely gels together to provide lower latency in wireless networks based on Wireless N/AC. Real-time experiments were performed using Flent on our physical testbed with BBR-n and modern AQMs such as Fair Queuing (FQ), Constrained Delay (CoDel), Proportional Integral controller Enhanced (PIE), Common Applications Kept Enhanced (Cake) and Flow Queuing Controlled Delay (FQ_CoDel). Various tests done on our physical testbed helped us identify CAKE as the most optimum AQM that fits with our proposed BBR-n while providing optimum throughput and lower latency in 802.11N/AC-based wireless networks.

VNF placement in NFV-enabled networks: considering time-varying workloads and multi-tenancy with a throughput optimization heuristic

VNF placement in NFV-enabled networks: considering time-varying workloads and multi-tenancy with a throughput optimization heuristic

IEEE 802.11be Network Throughput Optimization With Multilink Operation and AP Controller

IEEE 802.11be Network Throughput Optimization With Multilink Operation and AP Controller

Congestion and throughput optimization protocol for providing better quality of service and experience

<p>Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.</p>

Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption

Nowadays, cybersecurity has grown into a more significant and difficult sci-entific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guar-antees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The as-sessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

Synergies and Challenges in the Integration of Cloud Computing and Deep Learning: Current Status, Interconnectedness, and Future Directions

This article reviews the status and recent developments in the integration of cloud computing and deep learning, as well as the interrelationship between these two technologies. The paper explores the intersection of cloud computing and deep learning in addressing cybersecurity challenges. Amidst the rapid expansion of the worldwide public cloud services market, the vulnerability to cyber-attacks and breaches in data management is on the rise. Different intrusion detection systems use different deep learning techniques to improve the effectiveness of intrusion detection in cloud computing environments. Additionally, the use of encryption technology and the corresponding deep learning retrieval technology further improves the security of cloud data. Moreover, the paper deeply studies how the scheduling mechanism of deep reinforcement learning can optimize the performance of cloud services by efficiently allocating resources and solving the problem of slow cloud service speed. It also derives the optimal energy strategy through deep neural networks to address the energy consumption challenges in cloud computing data centers. This article also reviews the five emerging architectures of cloud computing and explores the role of deep learning within these frameworks. Finally, it analyzes some of the challenges facing the future of cloud computing and deep learning, including the security and confidentiality of cloud computing, as well as low latency and high throughput optimization in the field of deep learning. In summary, this article provides insight into current trends, challenges, and future prospects for the evolving integration between cloud computing and deep learning.

Meta‐heuristic optimization techniques for scheduling in multiple‐input multiple‐output multiple‐access channel system

SummaryIn a highly dense multiple‐input multiple‐output (MIMO) communication system, proper resource allocation is exceedingly challenging. The simultaneous selection of antennas at the base‐station (BS) and scheduling of users in an uplink multiple access channel (MAC) system add up to the signal processing load in the MIMO wireless system. The method chosen for combining the user scheduling and antenna selection must have a very low computational complexity. However, the exhaustive search algorithm (ESA), which achieves the best sum‐rate capacity in a MIMO‐MAC system, has a very high level of complexity. In this paper, we use a metaheuristic‐based optimization algorithms to optimize the sum‐rate capacity in a MAC system with significantly less computational complexity. First, we use the beluga whale optimization (BWO) metaheuristic method to solve the optimization problem. The BWO is a swarm‐based algorithm that draws inspiration from beluga whale behavior. An improved BWO (iBWO) is also presented and is utilized for the same optimization challenge. The proposed iBWO scheme improves the search capability of the algorithm and thereby ensures better exploration and exploitation capabilities of the beluga whales. We compare the performance of these algorithms with a standard differential evolution (DE) algorithm. The simulation results demonstrate that the proposed iBWO is a very competitive meta‐heuristic algorithm for solving optimization problems. The proposed iBWO algorithm achieves near optimal throughput in a MIMO‐MAC system. Also, the computational complexity related to the suggested optimization technique in a MIMO‐MAC system is found to be very less compared with the complexity related to ESA.

Joint altitude and beamwidth optimization for LEO satellite‐based IoT constellation

SummaryLow Earth Orbit (LEO) satellite‐based Internet of Things (IoT) has become a hot topic in IoT networks due to the ability of global coverage, especially in remote areas. How to design a commercial LEO satellite‐based constellation to meet the IoT traffic requirement remains an open problem. In this paper, we propose a throughput optimization algorithm for LEO satellite‐based IoT networks meanwhile reducing the number of LEO satellite. Based on stochastic geometry theory, a closed‐form expression is derived for the throughput of a dynamic LEO satellite‐based IoT networks when LEO satellite equips with capture effect (CE) receiver and successive interference cancelation (SIC) receiver, respectively. Furthermore, a joint altitude and beamwidth optimization problem is formulated under the constraint of Walker constellation to optimize the throughput and the number of LEO satellites. To solve this multi‐objective optimization problem, we design an iterative non‐dominated sorting genetic algorithm II (NSGA‐II) for the rapid development of IoT traffic. Simulation results show that our proposed algorithm can effectively improve the throughput performance of random access (RA) protocol in LEO satellite‐based IoT networks compared to benchmark problems.

PuffChain: A Dynamic Scaling Blockchain System With Optimal Effective Throughput

PuffChain: A Dynamic Scaling Blockchain System With Optimal Effective Throughput

Biphase routing scheme for optimal throughput in large-scale optical satellite networks

In the large-scale optical satellite network (LS-OSN), hundreds to thousands of low Earth orbit (LEO) satellites will be interconnected via laser links, offering global coverage characterized by high throughput and low latency. LS-OSNs present an attractive strategy to cultivate a comprehensively connected, intelligent world. However, the dynamic nature of the satellites, as they orbit the Earth, results in frequent changes in the LS-OSN topology. Thus, there is a pressing need for efficient routing algorithms that not only cater to massive traffic demands but also swiftly adapt to these constant topological changes. Traditional routing algorithms for services with specific bandwidth requirements often compromise on either computational speed or throughput efficiency. In response, this study introduces a routing scheme based on flow optimization and decomposition (FOND). This seeks to shorten the computation time while preserving optimal network throughput. Expanding upon the FOND scheme, we further devised two heuristic algorithms: the flow-based greedy path (FGP) and the flow-based greedy width (FGW). Simulation results from a 288-satellite constellation network indicate that both the FGP and FGW outpace contemporary methods in terms of the routing computation time while maintaining a consistent throughput equal to 100% of the network capacity. Notably, the FGP has exhibited an impressive capability, reducing the routing computation time to 0.23% compared to the baseline incremental-widest-path (IWP) algorithm, which operates on Dijkstra’s algorithm principles.

Instantaneous and average throughput optimization using reconfigurable intelligent surfaces with solar energy harvesting

Instantaneous and average throughput optimization using reconfigurable intelligent surfaces with solar energy harvesting

Power allocation scheme for sum rate and fairness trade-off in downlink NOMA networks

Non-orthogonal multiple access (NOMA) is an essential enabler technology that is expected to help satisfy the key requirements of increased system throughput in future wireless networks. However, another equally important aspect that should go hand-in-hand with system throughput is user fairness for any network. But, to the best of our knowledge, even though there have been works that look at system throughput or user fairness maximization for NOMA-based networks, they looked at these as a single objective optimization problem, where one is the objective and the other is one of the constraints. However, quite often, joint optimization of both system throughput and user fairness is required to make optimized decisions in the face of trade-offs between these two equally important but conflicting objectives. In this regard, this paper formulates a multi-objective optimization problem to jointly maximize the sum rate and user fairness in a downlink transmission NOMA system, through optimize power allocation (PA), under system-imposed constraints. A weighted sum approach is used to turn the multi-objective optimization problem into a single-objective optimization problem to make it analytically tractable. The optimized PA is then obtained using Lagrange dual decomposition method and Karush–Kuhn–Tucker (KKT) conditions. Using our derived expressions, we propose an iterative PA algorithm that converges fast enough to be employed in practical NOMA networks. We also present simulation results to highlight the effectiveness of the proposed solution. Further, the performance of the proposed method is compared with that of the benchmark methods.

Throughput and coverage based Base Station–Relay Station deployment for 5G cellular network

SummaryFifth generation cellular networks have high data rates and connectivity demands. The relaying approaches are widely used in cellular networks to improve coverage, user throughput, and capacity at low cost. The increasing data requirements of the mobile network increase energy consumption. Energy efficiency‐based approaches also impact network coverage and throughput. Therefore, developing an energy‐efficient network with optimal coverage and throughput is considered a challenging issue. It can be resolved with optimal deployment of Base Station (BS), Relay Station (RS), and minimizing power consumption. In this research, a joint clustering‐based deployment technique is proposed for BS–RS deployment by considering the network throughput and coverage ratio. After enabling optimal BS–RS deployment, the network traffic is estimated with an On‐demand real‐time traffic estimation framework (ODTE). It estimates user association values using Beacon frames. In addition, the Relay Assisted Base station Power Scheduling (RABPS) approach to minimize power consumption is included. Based on Erlang's probability, the RABPS algorithm is enhanced with sleep mode activation during off‐peak hours. The proposed 5G network ensures uninterrupted connectivity and energy efficiency with varying time slots of power scheduling. The throughput performance of the clustering‐based approach is increased with a coverage ratio of 88%. The simulation results show the superiority of the proposed 5G BS–RS deployment and power scheduling in terms of throughput, coverage ratio, and power consumption.

Multi-Armed-Bandit-Based Framed Slotted Aloha for Throughput Optimization

Multi-Armed-Bandit-Based Framed Slotted Aloha for Throughput Optimization

Development and Optimization of Cassava (Manihot Esculenta) Peeling Machine

The conventional cassava peeling methods are inefficient, time-consuming, and labor-intensive This study aimed to create an enhanced cassava peeling machine with minimal flesh loss. The machine design includes components like a hopper, shafts, bearings, an electric motor, and a v belt connected to a pulley that drives the brush-equipped shaft. This locally sourced and fabricated machine, designed for 50kg of cassava tubers, was tested at operational speeds of 380, 420, and 460 rpm and retention times of 4, 6, and 8 minutes, optimizing the effects of machine speeds and peeling times on peeling efficiency using I-optimal Response Surface Experimental Design with a mean separation at P<0.05. Results pertaining to tuber properties, such as angle of repose, peel thickness, moisture content, peel penetration force, bulk density, and coefficient of friction, were utilized in the machine's design. The machine achieved maximum peeling efficiency of 74% at a speed of 380m/s when operated for 6 minutes. At this optimal efficiency, the machine reached an optimal throughput capacity of 171.4kg/h, with a 21% flesh loss, a peeling weight proportion of 25.8%, and minimal tuber damage (3.3%). The desirability, which signifies the extent to which these optimal values align with the optimization goal, was 0.83 (83%). In essence, this machine significantly advances cassava peeling mechanization.

Throughput optimization in multi-numerology 5G NR non-terrestrial networks

Throughput optimization in multi-numerology 5G NR non-terrestrial networks