Decrease In Execution Time Research Articles

Advanced Hybrid Optimization Algorithms for Energy Efficient Cloud Resource Allocation

AbstractCloud computing is highly sought after for its dynamic resource allocation capabilities and pay‐per‐use model. However, previous research has identified several challenges, such as lower coverage, high integration rates, longer computation times, and complex operators, all of which are associated with NP‐hard problems. These issues negatively impact the efficiency of resource allocation and scheduling, leading to slower processes, inefficiencies in multi‐objective optimization, lower throughput, and higher power consumption. To address these challenges, we propose a unique Hybridized Optimization Algorithm that integrates Crow Swarm Optimization (CSO), Cuckoo Search Optimization (CSO), and Cat Hunting Optimization (CHO). Initially, Particle Swarm Optimization (PSO) and the Crow Search Algorithm (CSA) handle the exploitation and exploration phases to balance task loads. Subsequently, Cuckoo Search Optimization (CSO) enhances resource utilization and addresses NP‐hard issues, while Cat Hunting Optimization (CHO) refines the search from global to local optimal spaces to achieve the best values. The results demonstrate that the proposed hybrid technique effectively reduces user request waiting times, lowers energy consumption, and decreases execution times on cloud servers compared to baseline approaches, thereby significantly improving overall system performance.

HAC-SAGIN: High-altitude computing enabled space-air-ground integrated networks for 6G

The space–air–ground integrated networks (SAGINs) provide a new paradigm for the evolution of the Internet of Things (IoT) networks by enhancing coverage and deploying computing resources near IoT devices, especially in emergency situations and disaster-hit regions. In the context of the IoT networks, aerial platforms such as unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) present in the air layer of SAGINs with access and aerial computing (AC) capabilities have the potential to significantly expand coverage, enhance performance, reduce delay and handle complex computation tasks for IoT devices. Seeking the stated prospect, we propose a high-altitude computing (HAC)-enabled SAGIN leveraging millimeter waves (mmWave) frequency range in which the IoT devices are provided access services by low-earth orbit satellites (LEO-SATs) and HAPs while the HAPs offer AC facility as well. Non-orthogonal multiple access (NOMA) is used as a multiple access scheme with different clustering mechanisms in uplink (UL) and downlink (DL) communication. We aim to establish high-rate data transmission in DL along with minimizing the execution time of IoT devices offloading their data to the HAPs in UL communication. The mmWaves range is targeted to have high-rate data transmissions and NOMA implementation further enhances the bandwidth available for an individual IoT device. For efficient offloading in UL communication, we formulate an optimization problem aiming to minimize the execution time by using the Lagrangian function-based approach. Execution time is minimized by reducing the transmission and computation time, which is attained by the optimization of allocated power and computation resources. Simulation results demonstrate that the proposed HAC-SAGIN is able to establish high-rate transmissions in DL and exhibits a significant decrease in execution time in UL in contrast to the no optimization case. Optimum power assignment improves the achievable rate, leading to reduced transmission time, while optimum core assignment efficiently reduces the computation time. In addition, the offloaded data size-driven NOMA implementation in UL prominently improves the system effective throughput.

LATA: learning automata-based task assignment on heterogeneous cloud computing platform

A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively.

Enhancing CFD solver with Machine Learning techniques

This study addresses the computational challenges in fluid flow simulations arising from demanding computational grids, required to capture the temporal and length scales involved. Our approach focuses on the pressure solver, as this is a resource-intensive component in Computational Fluid Dynamics (CFD) solvers. We achieve this by integrating a Machine Learning (ML) surrogate model with an incompressible fluid flow solver. We created two variants of an ML-enhanced CFD solver which were able to reduce the number of iterations required by the CFD pressure solver during unsteady flow simulations. Consequently, the simulations yielded comparable drag coefficients and Strouhal numbers, accompanied by an eightfold decrease in execution time. The performance enhancements are attributed to reduced computational effort per temporal iteration and early-stage forcing on the simulation dynamical behavior when using the ML-based surrogate models. This research introduces an approach to enhance the computational efficiency of fluid flow analyses by incorporating surrogate models to aid the pressure solver in CFD simulations. We propose a Hybrid CFD solver, ie. a physics-informed solver enhanced with data-driven surrogate models.

Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.

We investigate the development of visuospatial and oculomotor reading skills in a cohort of elementary school children. Employing a longitudinal methodology, the study applies the Topological serial digit Rapid Automated Naming (Top-RAN) battery, which evaluates visuospatial reading skills leveraging metrics addressing crowding, distractors, and voluntary attention orientation. The participant pool comprises 142 students (66 males, 76 females), including 46 non-native speakers (21 males, 25 females), representing a diverse range of ethnic backgrounds. The Top-RAN dataset encompasses performance, error, and self-correction metrics for each subtest and student, underscoring the significance of these factors in the process of reading acquisition. Analytical methods include dimensionality reduction, clustering, and classification algorithms, consolidated into a Python package to facilitate reproducible results. Our results indicate that visuospatial reading abilities vary according to the task and demonstrate a marked evolution over time, as seen in the progressive decrease in execution times, errors, and self-corrections. This pattern supports the hypothesis that the growth of oculomotor, attentional, and executive skills is primarily fostered by educational experiences and maturation. This investigation provides valuable insights into the dynamic nature of these skills during pivotal educational stages.

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

Explainable AI Empowered Resource Management for Enhanced Communication Efficiency in Hierarchical Federated Learning

In the rapidly advancing landscape of machine learning, Federated Learning (FL) stands as a transformative paradigm, preserving data privacy and overcoming challenges in training models. Existing FL research has primarily concentrated on refining the learning process, often overlooking the critical aspect of minimizing communication costs, especially for clients with limited resources. This article introduces a groundbreaking approach, Explainable AI Empowered Resource Management for Enhanced Communication Efficiency in Hierarchical Federated Learning (XRM-HFL). XRM-HFL utilizes an explainable AI-driven resource management strategy, precisely predicting computational resource requirements. The hierarchical framework minimizes communication burdens by assigning communication with the cloud server to a designated Node Controller (NC) via edge aggregation. Empirical evaluations demonstrate that XRM-HFL outperforms prior methods, facilitating efficient communication, resource utilization, and faster convergence. The model exhibits notable decreases in intercommunication expenses, global costs, failure nodes, and execution time, along with an increase in resource utilization. XRM-HFL provides resource-constrained clients with an effective solution for federated learning, minimizing costs while preserving model accuracy.

An improved methodology for modeling short pulse buried layer x-ray emission spectra

Radiation-hydrodynamic and spectroscopic modeling are important aspects of high energy density experimental design. In this paper, we improve the performance and capabilities over those obtainable with a previous methodology used for simulating x-ray emission spectra from buried layer targets heated by short pulse lasers. The improvement incorporates post-processing HYDRA radiation-hydrodynamic output with a non-local thermodynamic equilibrium atomic-kinetics radiation transport code, Cretin. Each code uses an independent radiation field which allows decoupling HYDRA's radiation group structure from Cretin's spectral output to improve the speed and flexibility of the design methodology. The execution time decreases from 2–3 days to a few hours while the flexibility of the improved methodology allows for performing sensitivity studies including a comparison of steady-state and time-dependent atomic kinetics and differences in the radiation group structure.

Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI

The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is then converted into a control signal through a brain-computer interface (BCI) based on motor imagery. Whenever gathering features from EEG signals, making a BCI is difficult in part because of the enormous dimensionality of the data. Three stages make up the suggested methodology: pre-processing, extraction of features, selection, and categorization. To remove unwanted artifacts, the EEG signals are filtered by a fifth-order Butterworth multichannel band-pass filter. This decreases execution time and memory use, both of which improve system performance. Then a novel multichannel optimized CSP-ICA feature extraction technique is used to separate and eliminate non-discriminative information from discriminative information in the EEG channels. Furthermore, CSP uses the concept of an Artificial Bee Colony (ABC) algorithm to automatically identify the simultaneous global ideal frequency band and time interval combination for the extraction and classification of common spatial pattern characteristics. Finally, a Tunable optimized feed-forward neural network (FFNN) classifier is utilized to extract and categorize the temporal and frequency domain features, which employs an FFNN classifier with Tunable-Q wavelet transform. The proposed framework, therefore optimizes signal processing, enabling enhanced EEG signal classification for BCI applications. The result shows that the models that use Tunable optimized FFNN produce higher classification accuracy of more than 20% when compared to the existing models.

FIKA: A Conformal Geometric Algebra Approach to a Fast Inverse Kinematics Algorithm for an Anthropomorphic Robotic Arm

This paper presents a geometric approach to solve the inverse kinematics for an anthropomorphic robotic arm with seven degrees of freedom (DoF). The proposal is based on conformal geometric algebra (CGA), by which many geometric primitives can be operated naturally and directly. CGA allows for the intersection of geometric entities such as two or more spheres or a plane’s projection over a sphere. Rigid transformations of such geometric entities are performed using only one operation through another geometric entity called a motor. CGA imposes geometric restrictions on the inverse kinematics solution, which avoids computation of the forward kinematics or other numerical solutions, unlike traditional approaches. Comparisons with state-of-the-art algorithms are included to prove our algorithm’s superior performance: such as decreased execution time and errors of the end-effector for a series of desired poses.

Symbolic Regression on FPGAs for Fast Machine Learning Inference

The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR). It searches the equation space to discover algebraic relations approximating a dataset. We use PySR (a software to uncover these expressions based on an evolutionary algorithm) and extend the functionality of hls4ml (a package for machine learning inference in FPGAs) to support PySR-generated expressions for resource-constrained production environments. Deep learning models often optimize the top metric by pinning the network size because the vast hyperparameter space prevents an extensive search for neural architecture. Conversely, SR selects a set of models on the Pareto front, which allows for optimizing the performance-resource trade-off directly. By embedding symbolic forms, our implementation can dramatically reduce the computational resources needed to perform critical tasks. We validate our method on a physics benchmark: the multiclass classification of jets produced in simulated proton-proton collisions at the CERN Large Hadron Collider. We show that our approach can approximate a 3-layer neural network using an inference model that achieves up to a 13-fold decrease in execution time, down to 5 ns, while still preserving more than 90% approximation accuracy.

A Safe Heuristic Path-Planning Method Based on a Search Strategy.

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the search node, then calculates the node state based on the search strategy, including whether it is a normal or dangerous state, and calculates the danger coefficient of the corresponding point to select the path with a lower danger coefficient. At the same time, the optimal boundary is obtained by incorporating the environmental facilities, and the optimal path between the starting point, the optimal boundary point and the end point is obtained. Compared to the traditional A-star algorithm, this method achieved significant improvements in various aspects such as path length, execution time, and path smoothness. Specifically, it reduced path length by 2.89%, decreased execution time by 13.98%, and enhanced path smoothness by 93.17%. The resulting paths are more secure and reliable, enabling robots to complete their respective tasks with reduced power consumption, thereby mitigating the drain on robot batteries.

Optimizing Cloud Computing Applications with a Data Center Load Balancing Algorithm

Delivering scalable and on-demand computing resources to users through the usage of the cloud has become a common paradigm. The issues of effective resource utilisation and application performance optimisation, however, become more pressing as the demand for cloud services rises. In order to ensure efficient resource allocation and improve application performance, load balancing techniques are essential in dispersing incoming network traffic over several servers. The workload balancing in the context of cloud computing, particularly in the Infrastructure as a Service (IaaS) model, continues to be difficult. Due to available virtual machines and the limited resources, efficient job allocation is essential. To prevent prolonged execution delays or machine breakdowns, cloud service providers must maintain excellent performance and avoid overloading or underloading hosts. The importance of task scheduling in load balancing necessitates compliance with Service Level Agreement (SLA) standards established by cloud developers for consumers. The suggested technique takes into account Quality of Service (QoS) job parameters, VM priorities, and resource allocation in order to maximise resource utilisation and improve load balancing. The proposed load balancing method is in line with the results in the body of existing literature by resolving these problems and the current research gap. According to experimental findings, the Dynamic LBA algorithm currently in use is outperformed by an average resource utilisation of 78%. The suggested algorithm also exhibits excellent performance in terms of accelerated Makespan and decreased execution time.

Fast calculation for approximations in Dominance-based Rough Set Approach using Dual Information Granule

The Dominance-based Rough Set Approach (DRSA) is an extension of RST, which utilizes the dominance relation in attributes. However, traditional DRSA-based methods do not exploit the properties and logical structure in depth, which causes a high computational cost in lower and upper approximations. Besides, these methods are object-based, leading to repeated calculations from identical instances, and they involve numerous redundant computations to approximate different decisions. Therefore, we propose a novel approach, called DIGAC (Dual Information Granule-based Approximation Calculation), to improve DRSA by replacing the object-based calculation with the granule-based calculation. It effectively reduces the time complexity by constructing a granule-based ordered decision system. Additionally, this novel approach uses three types of Dual Information Granules (DIGs) to avoid repeated calculations from identical samples. In the process of lower and upper approximation, we leverage their transitivity to put forward a Distributed Storage-based Lower Approximation Calculation (DSLAC) strategy and a Query-based Upper Approximation Calculation (QUAC) strategy to eliminate redundant computations. Importantly, we theoretically prove that the DIG-based approach extensively reduces the time complexity of the approximations and obtains the same approximations as the original counterpart. Our approach is investigated on 23 datasets, and the experimental results show that it outperforms existing algorithms in terms of efficiency and stability, especially for large-scale and high-dimensional datasets, where the average decrease in execution time is up to 99%.

FastInformer-HEMS: A Lightweight Optimization Algorithm for Home Energy Management Systems

In a smart home with distributed energy resources, the home energy management system (HEMS) controls the photovoltaic (PV) storage system by executing the optimization algorithm to achieve the lowest power cost. The existing mixed integer linear programming (MILP) algorithm is not suitable for execution on the end-user side due to its high computational complexity. The HEMS algorithm based on a long short-term memory neural network (LSTM-HEMS) can effectively solve the problem of the high computational complexity of MILP, but its optimization outcome is not high due to the accumulation of prediction errors. In order to achieve a better balance between computational complexity and optimization outcome, this paper proposes a lightweight optimization algorithm called the FastInformer-HEMS, which introduces the E-Attn attention mechanism based on Informer and uses global average pooling to extract the attention characteristics. Meanwhile, the proposed method introduces the maximum self-consumption algorithm as a backup strategy to ensure the safe operation of the system. The simulated results show that the computational complexity of the proposed FastInformer-HEMS is the lowest among the existing algorithms. Compared with the existing LSTM-HEMS, the proposed algorithm reduces the power consumption cost by 12.3% and 6.6% in the two typical scenarios, while the execution time decreases by 13.6 times.

Sieve Search Centroiding Algorithm for Star Sensors.

The localization of the center of the star image formed on a sensor array directly affects attitude estimation accuracy. This paper proposes an intuitive self-evolving centroiding algorithm, termed the sieve search algorithm (SSA), which employs the structural properties of the point spread function. This method maps the gray-scale distribution of the star image spot into a matrix. This matrix is further segmented into contiguous sub-matrices, referred to as sieves. Sieves comprise a finite number of pixels. These sieves are evaluated and ranked based on their degree of symmetry and magnitude. Every pixel in the image spot carries the accumulated score of the sieves associated with it, and the centroid is its weighted average. The performance evaluation of this algorithm is carried out using star images of varied brightness, spread radius, noise level, and centroid location. In addition, test cases are designed around particular scenarios, like non-uniform point spread function, stuck-pixel noise, and optical double stars. The proposed algorithm is compared with various long-standing and state-of-the-art centroiding algorithms. The numerical simulation results validated the effectiveness of SSA, which is suitable for small satellites with limited computational resources. The proposed algorithm is found to have precision comparable with that of fitting algorithms. As for computational overhead, the algorithm requires only basic math and simple matrix operations, resulting in a visible decrease in execution time. These attributes make SSA a fair compromise between prevailing gray-scale and fitting algorithms concerning precision, robustness, and processing time.

An Implementation of Lean Approach to Achieve Working Time Efficiency in Precast Factory

Question: Can the 5S lean approach using Seiri (Sorting), Seiton (Arranging), Seiso (Cleaning), Seiketsu (Standardizing), and Shitsuke (Sustaining) improve the workflow to achieve efficiency in the working time needed to produce Corrugated Concrete Sheet Pile (CCSP)? Purpose: The aim of this study is to offer modification of work processes by implementing a lean approach using 5S to eliminate waste and improve working time. Research Method: Data were collected by practitioner interview and in-depth observation to obtain a completed understanding work process of the precast production cycle. Then, Bizagi Modeler 2.0 was used to determine the mapping of the production cycle and the total time duration. Process Activity Mapping is used to determine the problems contained in the cycle, especially activities that are NVA. Then, a modification of work processes used a lean approach with 5S concept to eliminate non-value added (NVA) activities and achieve working time efficiency. Findings: Based on the research results, it was found that there were two problems in the CCSP concrete production cycle. The first was the strand cutting activity, delayed since it was performed with a lack of workers. The second was the delivery ready mix was categorized as a delay activity that needs to be eliminated. Both of these problems were addressed with 5S. Seiri is applied by doing a standard operating procedure completed with the number of workers for each work item. At the same time, Seiton concepts are conducted by moving the batching plant. The improvement has resulted in: a decreased execution time of strand cutting from 129.23 minutes down to 64.8 minutes (50%), an elimination of delivery ready-mix as a delay activity (NVA activity) which can save 15.13 minutes of production time, and a shortened working time of concrete bucket loading from 10 minutes to 5 minutes (50%). All the working time savings are obtained in the production cycle. Before the improvement it took 33 days to complete 25 production cycle of CCSP. After the improvements the 25 CCSP production cycle took 31.5 days (4.5% efficiency). Limitations: This study mainly focused on a 5S lean approach with Bizagi Modeler software and Process Activity Mapping method at a precast factory producing CCSP. Implications: This research will provide guidance for applying the 5S lean approach relative to time efficiency in a construction company and can also inspire further research to develop alternative lean approaches. Value for Practitioners: This paper presents the impact of applying the 5S lean approach to minimize wastage in the production cycle in order to achieve time efficiency, with an emphasis on construction supply factories wishing to adopt lean. Keywords: 5S, Bizagi Modeler, Lean Approach, Process Activity mapping, Time Efficiency Paper type: Case Study

Execution Time Decrease for Controllers Based on Adaptive Particle Swarm Optimization

Execution time is an important topic when using metaheuristic-based optimization algorithms within control structures. This is the case with Receding Horizon Control, whose controller makes predictions based on a metaheuristic algorithm. Because the closed loop’s main time constraint is that the controller’s run time must be smaller than the sampling period, this paper joins the authors’ previous work in investigating decreasing execution time. In this context, good results have been obtained by introducing the “reference control profile” concept that leads to the idea of adapting the control variables’ domains for each sampling period. This paper continues to address this concept, which is adjusted to harmonize with the Particle Swarm Optimization algorithm. Moreover, besides adapting the control variables’ domains, the proposed controller’s algorithm tunes these domains to avoid losing convergence. A simulation study validates the new techniques using a nontrivial process model and considering three modes in which the controller works. The results showed that the proposed techniques have practical relevance and significantly decrease execution time.

Arbitrarily Parallelizable Code: A Model of Computation Evaluated on a Message-Passing Many-Core System

The number of processing elements per solution is growing. From embedded devices now employing (often heterogeneous) multi-core processors, across many-core scientific computing platforms, to distributed systems comprising thousands of interconnected processors, parallel programming of one form or another is now the norm. Understanding how to efficiently parallelize code, however, is still an open problem, and the difficulties are exacerbated across heterogeneous processing, and especially at run time, when it is sometimes desirable to change the parallelization strategy to meet non-functional requirements (e.g., load balancing and power consumption). In this article, we investigate the use of a programming model based on series-parallel partial orders: computations are expressed as directed graphs that expose parallelization opportunities and necessary sequencing by construction. This programming model is suitable as an intermediate representation for higher-level languages. We then describe a model of computation for such a programming model that maps such graphs into a stack-based structure more amenable to hardware processing. We describe the formal small-step semantics for this model of computation and use this formal description to show that the model can be arbitrarily parallelized, at compile and runtime, with correct execution guaranteed by design. We empirically support this claim and evaluate parallelization benefits using a prototype open-source compiler, targeting a message-passing many-core simulation. We empirically verify the correctness of arbitrary parallelization, supporting the validity of our formal semantics, analyze the distribution of operations within cores to understand the implementation impact of the paradigm, and assess execution time improvements when five micro-benchmarks are automatically and randomly parallelized across 2 × 2 and 4 × 4 multi-core configurations, resulting in execution time decrease by up to 95% in the best case.

A fast serial computation approach for computing viewshed of a region by detecting key spots on the terrain

Computing a viewshed for a space (a polygon), rather than a single location (a point), requires analysis of the maximum terrain extent observable from any location within the space. For a digital elevation model (DEM), it is the sum of viewshed analyses for all the pixels inside the confined space. As the raster spatial resolution increases, the process becomes computationally expensive. In this study, we propose a new serial computation approach, named Regional Viewshed by Aspect Peaks (RVAP), in which key pixels that define the cumulative visibility map on a digital elevation model (DEM) are found, making the process computationally affordable. We compare our proposed method next to 1) a 5-cell grid interval method 2) and an all-encompassing but slow complete approach where the viewshed is computed for all the pixels and then overlaid. Six regions-of-interests (ROIs) on two Shuttle radar (∼555 × 555 km dimensions, 90-meter resolution) DEMs are selected and categorized by their terrain ruggedness index (TRI) (i.e. slightly rugged, mildly rugged, highly rugged). The proposed approach is much faster, while its coverage is identical to the all-encompassing approach. Moreover, as the TRI increases, the accuracy of the proposed algorithm is not diminished, while its execution time decreases.