Assignment Approach Research Articles

SDN-enabled Resource Provisioning Framework for Geo-Distributed Streaming Analytics

Geographically distributed (geo-distributed) datacenters for stream data processing typically comprise multiple edges and core datacenters connected through Wide-Area Network (WAN) with a master node responsible for allocating tasks to worker nodes. Since WAN links significantly impact the performance of distributed task execution, the existing task assignment approach is unsuitable for distributed stream data processing with low latency and high throughput demand. In this paper, we propose SAFA, a resource provisioning framework using the Software-Defined Networking (SDN) concept with an SDN controller responsible for monitoring the WAN, selecting an appropriate subset of worker nodes, and assigning tasks to the designated worker nodes. We implemented the data plane of the framework in P4 and the control plane components in Python. We tested the performance of the proposed system on Apache Spark, Apache Storm, and Apache Flink using the Yahoo! streaming benchmark on a set of custom topologies. The results of the experiments validate that the proposed approach is viable for distributed stream processing and confirm that it can improve at least 1.64× the processing time of incoming events of the current stream processing systems.

Joint power allocation and channel assignment for device‐to‐device communication using the Hungarian model and enhanced hybrid Red Fox‐Harris Hawks Optimization

SummaryMultihop device‐to‐device (D2D) communication is a promising advancement since it allows wireless users in close proximity to communicate directly with one another without using base stations (BSs). With two nearby users, this technology intends to keep up with the accelerating development of mobile devices and the rising demands of local traffic loads. High‐data‐rate short‐range transmission is made possible by this paradigm, which also lowers network backhaul expenses while improving end‐user experience, spectral efficiency (SE) and network coverage. In order to maximize the cell's overall sum rates, this article examines the problem of selecting the optimal M‐D2D linkages and cellular users (CUs) to form spectrum‐sharing partners while taking quality of service (QoS) and energy efficiency (EE) needs into consideration. This study examines the use of a channel assignment system and a power allocation technique to manage interference in D2D communication scenarios involving many hops (more than one hop). The suggested channel assignment approach is based on the Hungarian method, but the power allocation system is based on the time‐efficient enhanced Harris hawks optimization (HHO) and red fox (RF) algorithms. A genetic algorithm (GA)‐based methodology and various baseline approaches are used to compare the effectiveness of the suggested system. Because it combines exploitation and exploration using a memory‐based local search methodology and a perturbation mechanism, the suggested approach outperforms the GA in simulations. The results clearly demonstrate that the suggested method increases EE by up to 13% by producing the appropriate channel and power assignment for CUs and M‐D2D users.

Joint load-balancing and power control strategy to maximize the data extraction rate of LoRaWAN networks

LPWAN enabled networks have a dizzying growth and continue to meet an essential need in the Internet of Things market due to their ability to provide low-cost wireless access to geographically spread-out devices. Consequently, an efficient allocation of wireless resources in order to support numerous devices becomes a major concern. In this paper, we propose an SF assignment approach in LoRa networks, paying attention on the traffic load both per Spreading Factor and over the channels. Indeed, our strategy consists in finding a better distribution of the end-devices on the SF by orchestrating an effective load balancing. Moreover, the performance of our solution is evaluated under diverse network configurations, taking into account the capture effect and the non-orthogonality of SFs. Furthermore, we extended the solution by a transmission power control strategy for overall system energy-efficiency. In addition, we validated some assumptions by full-scale experiments like for the 3GPP path loss model, which is used for the first time in LoRa simulations. Our results suggest that Load Shifting leads to better performance in terms of Date Extraction Rate (DER) while guaranteeing good scalability on the network size and density.

Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO

Consumption habits are changing due to the development of new technologies around renewable energy, environmental awareness, and new incentive policies. Smart grids are seen as an effective way to accommodate more renewable energy, achieve better control of demand, and improve the operating conditions of the electrical system. However, electric vehicles, which are an environmentally friendly alternative, have very high market penetration and require efficient electrical management at charging stations. Among the factors that have a significant impact on electrical energy consumption are traffic conditions, which can seriously impact the efficiency of electric vehicles. Therefore, the focus is on developing charging infrastructure and reducing vehicle waiting time by optimally allocating electric vehicles to charging stations. To this end, an optimization approach is presented, based on the traffic conditions collected by the SUMO simulator. This approach enables each vehicle to be assigned to the appropriate station while maintaining its battery state of charge at a higher level.

COALA: Concurrently Assigning Wire Segments to Layers for 2-D Global Routing

Two-dimensional (2-D) global routing followed by layer assignment is a common and popular strategy to obtain a good tradeoff between runtime and routing performance. Yet, the huge gap between 2-D routing patterns and the final 3-D routing paths often results in inevitable overflow after layer assignment. State-of-the-art (SOTA) studies on layer assignment usually adopt dynamic programming-based approaches to sequentially find an optimal solution for each net in terms of overflow or/and the number of vias. However, a fixed assignment ordering severely restricts the solution space, and the distributed overflows can hardly be resolved with any existing refinement approach. This article proposes a novel layer assignment framework that concurrently considers all the wire segments of nets and iteratively assigns them from the lowest available layer to the highest one. The concurrent scheme facilitates the maximal utilization of routing resource on each layer, contributing to an effective rerouting procedure that greatly reduces inevitable overflows. Based on the proposed framework, we further propose an obstacle-aware strategy that can mitigate obstacle-induced inevitable overflows in the original framework. Experimental results show that compared to an implemented sequential layer assignment approach based on SOTA techniques and refined by the well-known overflow/congestion reduction rip up and rerouting procedure, the proposed concurrent layer assignment framework (COALA) brings great improvements in the overflow reduction and runtime efficiency, which shows the significant advantage of the concurrent layer assignment scheme over sequential methods. The improvement is also verified in detailed routing, where the proposed COALA framework contributes to sparser routing results with fewer vias and design rule violations (DRVs).

An Integrated Trajectory Planning and Motion Control Strategy of a Variable Rotational Speed Pump-Controlled Electro-Hydraulic Actuator

This article proposes an integrated two-loop motion control strategy of an electro-hydraulic actuator, in which the cylinder actuator is driven by a variable speed pump to track a desired trajectory. The control strategy combines constrained trajectory planning (outer loop) with nonlinear motion control (inner loop) to achieve accurate trajectory tracking of electro-hydraulic actuators under constraints. A nonlinear filter-type trajectory planner is utilized as the outer loop to synthesize the optimal trajectory, reaching the desired reference with the minimum time while fulfilling the constraints. A full-state constraints assignment approach is proposed, including motion and pressure state constraints. Furthermore, to accurately track the synthesized trajectory of the outer loop, a model-based adaptive robust backstepping motion controller is designed as the inner loop, which can realize guaranteed robustness and high accuracy of the trajectory tracking in the presence of dynamic nonlinearities and parametric uncertainties of the electro-hydraulic actuator. Experiments with contrast control strategies and practical trajectories are conducted, which comprehensively verify the advantages of the proposed integrated two-loop control strategy.

Assessing Bioeconomy Development Opportunities in the Latvian Policy Planning Framework

The broad spectrum of bioresource use makes it challenging to interconnect strategic objectives and policy planning documents without compromising a coherent development vision. Bioeconomy development directions have been defined at the EU and Latvian levels. Nevertheless, to facilitate their implementation, the goals must be consistent with those specified in relevant national policy planning documents and vice versa. To determine whether internationally defined bioeconomy objectives are implemented in Latvian policy planning documents and what priority is given to them, a mixed methods approach was used—a systematic literature review combined with a keyphrase assignment approach. The results are summarized in an illustrative screening matrix and aggregated using the TOPSIS method to identify in which policy planning documents bioeconomy objectives are prioritized and to what extent. The results have shown a high prioritization of bioeconomy objectives in Latvian policy planning documents, especially in hierarchically higher documents.

Psychometric properties of the readiness for return to work scale in occupational rehabilitation in South Korea

BackgroundThis study validated the Korean version of the Readiness to Return to Work (RRTW) scale, as an assessment measure, following a musculoskeletal, work-related injury and as a measure of following return to work.MethodsThe participants of this study were workers with experience in rehabilitation programs at the Workers’ Compensation and Welfare Service (KCOMWEL) Hospital in Korea. Factor analyses were employed to ensure the validity and reliability of the RRTW scale in claimants who were in treatment without working (the not-working group) or who had already returned to work (the working group). To test structural validity, we analyzed exploratory factor analysis (EFA) respectively for the not working group (exploratory factor analysis (EFA) (n = 200), confirmatory factor analysis (CFA) (n = 109), and the working group (n = 123). To verify concurrent validity (multidimensional and assignment approach), the variables that were identified as relevant variables in previous studies were analyzed.ResultsThe not working group EFA, as shown in the original scale, had four dimensions, and one item was deleted: (1) Precontemplation (PC), (2) Contemplation (C), (3) Prepared for Action-Self-evaluative (PAS), and (4) Prepared for Action-Behavioral (PAB). The CFA revealed that a good model fit and reliability were suitable. Regarding the working group of EFA, it appeared in two dimensions as in the original scale, one item was modified from the UM scale to the PM scale, and the reliability was appropriate. Concurrent validity was satisfied based on the correlation between the RRTW factor and related variables.ConclusionsRRTW in the Korean version of the instrument was similar to those reported for the original scale, indicating that it may be used in research and clinical settings.

A Practical Guide to 16S rRNA Microbiome Analysis in Musculoskeletal Disorders.

Microbial taxonomic assignment based on 16S marker gene amplification requires multiple data transformations, often encompassing the use of a variety of computational platforms. Bioinformatics analysis may represent a bottleneck for researchers as many tools require programmatic access in order to implement the software. Here we describe a step-by-step approach for taxonomic assignment using QIIME2 and highlight the utility of graphical-based microbiome tools for further analysis and identification of biological relevant taxa with reference to an outcome of interest.

A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain

A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain

A novel repositioning approach and analysis for dynamic ride-hailing problems

Mobility-on-demand services continue to grow in popularity and could provide a cheap and resource-saving alternative to private vehicles. However, to be truly attractive to the general public, these services must be thoroughly optimized. In this paper, we consider a ride-hailing problem where available vehicles have to be assigned to dynamically arising customer requests and, furthermore, vacant vehicles have to be repositioned to other parts of the service area to balance supply and demand. We propose a novel repositioning strategy based on dynamically created, overlapping zones that addresses identified weaknesses of previous repositioning approaches. While most other ride-hailing studies only consider one specific setting for which a suitable ride-hailing strategy is developed, we further analyze which design decisions in the context of assignment and repositioning work best under different given problem characteristics. Our results show that the proposed repositioning approach outperforms the benchmark approaches in most of the relevant settings, independent of the underlying objective function. Additionally, we show that, especially for low-utilized fleets, the simple nearest-vehicle assignment strategy outperforms matching-based assignment approaches in many settings. The insights gained are analyzed and thoroughly discussed.

Molecular and morphological survey of Lamiaceae species in converted landscapes in Sumatra.

Molecular biodiversity surveys have been increasingly applied in hyperdiverse tropical regions as an efficient tool for rapid species assessment of partially undiscovered fauna and flora. This is done by overcoming shortfalls in knowledge or availability of reproductive structures during the sampling period, which often represents a bottleneck for accurate specimens' identification. DNA sequencing technology is intensifying species discovery, and in combination with morphological identification, has been filling gaps in taxonomic knowledge and facilitating species inventories of tropical ecosystems. This study aimed to apply morphological taxonomy and DNA barcoding to assess the occurrence of Lamiaceae species in converted land-use systems (old-growth forest, jungle rubber, rubber, and oil palm) in Sumatra, Indonesia. In this species inventory, we detected 89 specimens of Lamiaceae from 18 species distributed in seven subfamilies from the Lamiaceae group. One third of the species identified in this study lacked sequences in the reference database for at least one of the markers used (matK, rbcL, and ITS). The three loci species-tree recovered a total of 12 out of the 18 species as monophyletic lineages and can be employed as a suitable approach for molecular species assignment in Lamiaceae. However, for taxa with a low level of interspecific genetic distance in the barcode regions used in this study, such as Vitex gamosepala Griff. and V. vestita Wall. ex Walp., or Callicarpa pentandra Roxb. and C. candidans (Burm.f.) Hochr., the use of traditional taxonomy remains indispensable. A change in species composition and decline in abundance is associated with an increase in land-use intensification at the family level (i.e., Lamiaceae), and this tendency might be constant across other plant families. For this reason, the maintenance of forest genetic resources needs to be considered for sustainable agricultural production, especially in hyperdiverse tropical regions. Additionally, with this change in species composition, accurate species identification throughout molecular assignments will become more important for conservation planning.

Equilibrium Allocation Approaches of Quantum Key Resources With Security Levels in QKD-Enabled Optical Data Center Networks

In this article, the network performance of equilibrium allocation of quantum key resources was investigated in quantum key distribution (QKD)-enabled optical data center networks. To effectively use quantum key resources, we propose three novel efficient load balancing routing, wavelength, and time-slot assignment (LB-RWTA) approaches, including LB-RWTA with flexible security level (LB-RWTA-FSL), LB-RWTA with specific security level (LB-RWTA-SSL), and LB-RWTA without security level (LB-RWTA-NSL). Particularly, the proposed LB-RWTA-FSL approach is uniquely featured by adaptive security level classification (ASLC) and load balancing (LB), aiming to demarcate the security level (SL) and reduce the overall network congestion. We introduce an existing routing, wavelength, and time-slot assignment (E-RWTA) approach without SL, called E-RWTA, for comparison. Simulation results show the effectiveness of our proposed approaches in terms of much higher quantum key resource efficiency and thus much higher network security performance than the state-of-the-art quantum key resource allocation approaches compared with the E-RWTA approach in QKD-enabled optical data center networks.

Software architecture for pervasive critical health monitoring system using fog computing

Because of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture.

Communication-Aware Energy Consumption Model in Heterogeneous Computing Systems

Abstract Large heterogeneous computing systems are composed of conventional central processing units and graphics processing units (GPUs) where communication plays a crucial role for system performance. This paper presents an energy consumption analytical model in terms of communication perception for the communication–computing pipeline characterization of discrete GPUs systems. We propose a dynamically adaptive energy-efficient task assignment approach, which harnesses particle swarm optimization. Static energy optimization is addressed by optimal task partition granularity. The experimental results demonstrate that the communication-based energy optimization algorithms can be more energy-saving than those without communication consideration. For some application benchmarks, the energy consumption can be saved by up to 31%. This implies the potential that the energy-saving optimization methods can be incorporated in system engineering processes.

Linear Quadratic Regulator Weighting Matrices for Output Covariance Assignment in Nonlinear Systems

A time-varying output covariance assignment problem in the presence of a stochastic disturbance is solved using finite-horizon optimal control formulation. It is shown that an assignment of time-varying output error covariance is possible in the presence of model error by utilizing a time-varying linear quadratic regulator controller with a class of output and control weighting sequences. This paper develops a systematic algorithm to calculate the sequence of such time-varying output weights that are further shown to be the Lagrange multipliers associated with the covariance constraints. A short horizon attitude control problem with stringent covariance constraints and a more nonlinear example concerning a low-thrust interplanetary maneuver are solved to demonstrate the utility of the proposed approach. Numerical results offer a degree of optimism about the broad applicability of the time-varying covariance assignment approach to solve guidance and control problems associated with nonlinear dynamic systems.

Optimal forecast combination based on PSO-CS approach for daily agricultural future prices forecasting

Forecasting agricultural commodity prices accurately is a challenging task due to the complexity of the trading market and the variability of influencing factors. Many studies have proven that forecast combination is an effective strategy for improving forecast performance relative to individual forecasting. In the field of forecast combination, how to determine the reasonable weights for combination is still an open question. This study proposed an optimal forecast combination framework for agricultural commodity prices forecasting, which integrates the decomposition–reconstruction–ensemble methodology with an improved nature-inspired global optimization algorithm. The update mechanism of particle swarm optimization (PSO) is introduced to improve cuckoo search (CS), in order to reduce the searching blindness in the huge exploration space. The framework consists of four steps. First, data decomposition using empirical wavelet transform (EWT), singular spectral analysis (SSA), and variational mode decomposition (VMD); Second, component reconstruction via a modified reconstruction approach based on the largest comprehensive grey correlation degree clustering (CGCD); Third, individual forecasting using autoregressive integrated moving average regression (ARIMA), exponential smoothing (ETS), back propagation neural network (BPNN) and extreme learning machine (ELM); Fourth, forecast combination via PSO-CS weight assignment method. Using corn and wheat future prices as research samples, the experimental results demonstrated that: (a) the PSO-CS weight assignment approach is superior to other combination approaches in most cases; (b) the CGCD approach can effectively reduce the computational cost of forecasting and improve the prediction performance; (c) the Full-PSO-CS model provides the most accurate forecast due to the diversity of individual forecasts, it reduces MAPE by 43.66% and improves directional accuracy by 30.80% on average compared with the best single model.

A novel user clustering and a low-complexity power allocation in multi-user and multi-cluster NOMA system via Stackelberg game competition

Non-orthogonal multiple access (NOMA) has drawn wide attentions in multi-user, resource-constrained mobile systems to improve the system spectral efficiency. In the conventional resource assignment approaches, the optimal power allocation solutions are usually performed among multiple clusters, which, however, incur a high computational complexity due to the diversity of multiple users. In this work, we aim at the practical scenarios of multi-user and multi-cluster NOMA networks with the proposed a low-complexity power allocation and a suboptimal user clustering approach. By using Stackelberg game competition, a closed-form solution with no iterative operations is given first to the power allocation problem. The main idea of the authors’ proposed scheme is to have the base station (BS) allocating resources to all the users freely to guarantee their required minimum transmission-rates, and obtaining the maximal revenue through selling the remaining resources. Then, a suboptimal user clustering approach is further proposed to obtain more revenue for selling the resource. Numerical results show that the proposed power allocation scheme approximates the optimal sum-rates performance, but with lower computational floating-point operations (FLOPs) for the multi-user scene. And, the given user clustering scheme can pursue more revenue of the BS.

Numerical Simulation of Bearing Characteristics of Bored Piles in Mudstone Based on Zoning Assignment of Soil around Piles

This study conducts a field indoor simulation test, SEM observation, and penetration test to determine the bearing capacity of the dynamic driving pile in the mudstone foundation. It comprehensively analyzes the variation laws of structure and strength of mudstone around piles after piling. Indeed, the strength of mudstone structure is significantly reduced from outside to inside. Therefore, the numerical simulation of piles in mudstone should consider the actual characteristics of soil damage around piles. The strength of mudstone after pile driving damage is measured, and the scatter diagram depicting the relationship between mudstone strength and pile side distance is produced. Then, the best-fitting curve of the relationship between the strength ratio and the distance ratio of the simulated pile driving test is established by the nonlinear fitting of multiple curves. A numerical simulation method is proposed to consider the damaged area and parameters surrounding the pile. The range of soil damage caused by pile driving in the mudstone foundation is determined to be two times that of the pile diameter. The disturbance area is divided into four parts on average, and the width of each part is 0.5d. The simulation results are compared to the conventional approach of uniform parameter assignment to prove the rationality of the method.

Improved spectral allocation in optical wireless communication based on quadrature amplitude modulation using metaheuristic wavelength division multiplexing

With the current surge in interest in high-speed wireless access to wireless communication data networks, appealing, and OFDM (Orthogonal frequency-division multiplexing) for achieving high data speeds. This research ends the real-time exception by proposing a larger number of subcarriers at 35 km/h data rates based on wireless communication as well as spectrum assignment approach is proposed. Simultaneous transmission has also been achieved using OFDM-QAM (Quadrature Amplitude Modulation). As a direct result of comparing, it to the signal delivered, a bit-error-rate performance calculation comparison has been done. The experimental analysis has been carried out for National Science Foundation Network (NSFNET) and EON (European Optical Network)− 28 network topology in terms of SNR, energy efficiency, Throughput and spectral efficiency. For National Science Foundation Network (NSFNet) topology the obtained SNR of 72 %, energy efficiency of 95 %, throughput of 96 % and spectral efficiency of 92 % and EON-28 topology of the network, the proposed technique obtained 70 % SNR, 92 % energy efficiency, 97 % throughput and 95 % spectral efficiency.