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检索条件"主题词=Scheduling algorithms"

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Optimal Divisible Load scheduling for Load Balancing in Cloud Computing

Optimal Divisible Load Scheduling for Load Balancing in Clou...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： R.S. Ramya R.S.Vishnu Durai R. Vijayakumar M. DhivyaShree Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Cloud computing is a significant technological advancement that finds application in different research domains, facilitating on-demand access to resources. Throughout this process, the cloud encounters many challenges related to fault tolerance, availability, scalability, and overhead reduction. across these challenges, load balancing across multiple nodes emerges as a significant concern. Ensuring equitable distribution of loads across several nodes is a crucial prerequisite for enhancing resource utilization and optimizing task response time. This research introduces a unique approach to address the aforementioned challenges by proposing a DLS methodology that use a divisible load scheduling algorithm. The algorithm exhibits dynamic behavior as it operates on data stored in two distinct centers and processes multiple requests. The experimental findings demonstrate that the suggested methodology exhibits strong performance, as shown by favourable values of several performance criteria such as throughput, reaction time, migration time, and resource utilization, when compared to conventional techniques.

关键词： Cloud computing Fault tolerance scheduling algorithms Heuristic algorithms Scalability Fault tolerant systems Load management Throughput Resource management Time factors

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Optimization-based Pump Operation scheduling for Optimal Energy Management and Pumping Costs

Optimization-based Pump Operation Scheduling for Optimal Ene...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Rithyam Pathak Shubham Kumar Vinayak Das Pravin P S Department of Electronics & Instrumentation Engineering National Institute of Technology Silchar Assam India

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Optimal pump operation scheduling primarily involves strategically planning the operation of pumps in households, industrial processes, waste water treatment plants, energy systems etc. This paper explores the significance of machine learning-based scheduling and optimization algorithms in achieving optimal pump operation scheduling. The proposed work aims in optimizing the water pumping cost by predicting the future usage of a specific consumer based on their past water usage pattern and scheduling the pump accordingly. This entails devising a new pump scheduling model and comparing the proposed model with the conventionally available methods with the objective to meet the consumer water demands together with minimising the total pumping costs. Various challenges such as dynamic demand fluctuations, time-of-use energy price, and system constraints are taken into account in this study. algorithms based on Mean-based prediction, Gaussian Curve Fitting-based prediction, time series forecasting techniques using Long Short-Term Memory (LSTM), and Auto-regressive Integrated Moving Average (ARIMA) were adopted and the best algorithms for different usage patterns were identified with the main objective of achieving (i) optimal resource allocation, (ii) cost savings, and (iii) overall system reliability for the various real world scenarios. Using the optimal pump scheduling algorithm, a total pumping cost reduction of 8.32% was observed in comparison to conventional pumping patterns.

关键词： Costs Job shop scheduling scheduling algorithms Heuristic algorithms Pumps Time series analysis Predictive models Prediction algorithms Optimization Long short term memory

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Sparrow Search Algorithm-Based Decomposition for Task scheduling in Fog-Cloud System Integrating Blockchain Technology

Sparrow Search Algorithm-Based Decomposition for Task Schedu...

引用

ACS/IEEE International Conference on Computer Systems and Applications

作者： Mohsen Aydi Houssem Eddine Nouri Olfa Belkahla Driss Ecole Supérieure de Commerce de Tunis LARIA UR22ES01 La Recherche en Intelligence Artificielle ENSI Université de la Manouba Tunisia Institut Suérieur d'Informatique et de Gestion de Kairouan Université de Kairouan LARIA UR22ES01 La Recherche en Intelligence Artificielle ENSI Université de la Manouba Tunisia Ecole Suérieure de Commerce de Tunis LARIA UR22ES01 La Recherche en Intelligence Artificielle ENSI Université de la Manouba Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

In the rapidly evolving landscape of distributed computing, the integration of fog computing, cloud paradigms, and blockchain technology has emerged as an innovative approach to enhance the efficiency and security of task scheduling. This article introduces a novel method using the Sparrow Search Algorithm (SSA), along with its decomposition variant SSA/D, to optimize task scheduling within this integrated system. This paper presents a comprehensive framework that outlines the unique characteristics of fog, cloud, and blockchain environments, addressing their individual constraints while leveraging their combined strengths. The adaptability and robustness of SSA/D are employed to address the multi-objective nature of the scheduling problem, focusing on minimizing latency, energy consumption, and financial cost while ensuring data integrity through the blockchain's immutable ledger. We have employed the Sparrow Search Algorithm (SSA), a recent optimization method proven effective in other applications. Our main contribution is the integration of a decomposition strategy to enhance the exploration of the search space and enable parallel execution of the SSA process. Experimental results show that the proposed approach outperforms existing algorithms in terms of both efficiency and effectiveness, providing significant improvements over popular task scheduling metaheuristics. This paper not only offers a new algorithmic solution but also sets a precedent for future research in integrated fog-cloud-blockchain systems, with a view towards integrating machine learning techniques to further optimize these problems.

关键词： Energy consumption Machine learning algorithms Costs scheduling algorithms Optimization methods Search problems scheduling Robustness Blockchains Security

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QAQA-SS: An Improved Fuzzing Approach with Seed scheduling Based on the UCB Algorithm for QA Systems

QAQA-SS: An Improved Fuzzing Approach with Seed Scheduling B...

引用

IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Jinfu Chen Yucheng Wu Haibo Chen Jingyi Chen Ernest Akpaku School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Question Answering (QA) system is a type of artificial intelligence application designed to comprehend natural language questions and provide accurate, meaningful natural language answers. However, there are errors in the QA system, which can cause inconvenience for users and even lead to economic losses and security issues. In response to these challenges, QAQA showed up. QAQA designs several metamorphic relations (MRs) and achieves precise automatic testing through semantic-guided search and enhanced test oracles. However, QAQA fairly selects each seed (initial test input) in sequential order and assigns them a fixed number of mutations, which leaves many seeds that could produce quality test inputs neglected. This paper proposes an improved version named QAQA-SS (QAQA-Seed Schedule), where we enhance QAQA with a seed scheduling module and a fuzzing framework featuring iterative feedback. We combine the Upper Confidence Bound (UCB) algorithm with perplexity to devise a novel seed scheduling algorithm. QAQA-SS selects superior seeds and allocates appropriate mutation counts (energy) to generate more error-inducing test inputs within a limited energy budget. Additionally, we introduce a text paraphrasing operation to improve the performance of finding errors in the QA system. We conducted experiments following the experimental setup of QAQA on three datasets: BoolQ, SQuAD2, and NarrativeQA. Our experimental results reveal a noteworthy enhancement in the error discovery rate for QAQA-SS, achieving 81,777 error discoveries compared to 64,354 error discoveries with QAQA.

关键词： Schedules scheduling algorithms Natural languages Software quality Fuzzing scheduling Question answering (information retrieval) Software reliability Security Resource management

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Deep Reinforcement Learning-based Load Balancer using Kubernetes 17

Deep Reinforcement Learning-based Load Balancer using Kubern...

引用

17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023

作者： Soussia, Moenes Ben Bousselmi, Khadija Idoudi, Hanen University of Manouba Riadi Laboratory Manouba Tunisia University Savoie Mont Blanc Listic Laboratory Annecy France

ISBN: (纸本)9798350338904

The performance of irregular scientific applications can be easily affected by an uneven distribution of loads between computing resources. In this context, load balancing is one of the most important solutions to improve resource utilization. Yet many heuristic and metaheuristic algorithms have been proposed in the literature for dynamic load balancing, choosing the best performing load balancing algorithm for a given application is not a trivial task especially in situations where applications have a dynamic *** paper proposes a new approach of Load Balancing using Deep Reinforcement Learning. The proposed agent schedules tasks to the cluster's node using state of the art scheduling algorithms. Based on the user's tasks and the available nodes, the agent must choose an efficient scheduling algorithm as an action from the action space and use it to schedule the pending tasks to cluster's nodes. The agent's action space is composed of state of the art scheduling algorithms. This approach optimizes both the average execution time and the resource utilization while keeping a low Imbalance Degree in the cluster. © 2023 IEEE.

关键词： scheduling algorithms

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Research on Intelligent scheduling Algorithm for Electric Vehicle Charging Pile Based on Deep Learning

Research on Intelligent Scheduling Algorithm for Electric Ve...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Jin Cui Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

Along with the rapid popularity of EV, the intelligent dispatching of charging pile is becoming a key problem for improving the charging efficiency and optimizing the distribution of resources. This paper proposes an intelligent scheduling algorithm for electric vehicle charging piles based on RNN, aiming to solve the limitations of traditional scheduling methods in dynamic demand environments. By designing an RNN model containing LSTM units, this paper effectively captures the time dependency of charging demand and optimizes the scheduling strategy of charging piles. Experiments are carried out to compare the performance of RNN and conventional heuristics. Experiments indicate that RNN can significantly decrease the average waiting time (about 28%) and increase the utilization ratio of charging pile (about 17%). In addition, the calculation time of the RNN model meets the real-time scheduling requirements, and the training time is about 15 minutes, which is suitable for the actual needs of large-scale charging pile networks. Through in-depth analysis of the simulation results, this paper finds that the RNN scheduling algorithm shows higher flexibility and robustness in dealing with charging demand fluctuations.

关键词： Deep learning scheduling algorithms Heuristic algorithms Prediction algorithms Electric vehicle charging Real-time systems Resource management Vehicle dynamics Optimization Load modeling

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Opposition Strategy with Harris Hawk Algorithm for Task scheduling in Edge Computing

Opposition Strategy with Harris Hawk Algorithm for Task Sche...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： S. Padmakala Smita Khond Mohammed Al-Farouni S. Punitha S. Kaliappan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of CSE Malla Reddy Engineering College for Women Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mathematics Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation Deemed to be University Salem India Department of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Edge computing (EC) nodes undertake an increasing number of tasks with rise of business density. However, unreasonable edge computing resource allocation suffers from high energy consumption and resource waste. This paper, Opposition-Based Learning with Harris Hawk Algorithm (OHHO) using task scheduling in EC, distributed involves complex and dynamic environment with changing tasks and resources availability. The HHO has mechanisms to maintain exploration and exploitation, reduce high dimensionality in search space where algorithm avoids to fall in local optima. OBL helps in this dynamic context by ensuring suboptimal solutions are quickly discarded and replaced with their opposites, which allows algorithm to adapt better to recent situation. This is adaptability is crucial in edge computing, where tasks and resources are constantly fluctuating. The resource allocation involved Dynamic Priority Task scheduling Algorithm (DPTSA) helps in balancing system load by distributing tasks based on their priority and availability of resources. The proposed OHHO algorithm achieves high performance such energy 0.1mJ, Makespan of 1s and Delay of 45ms respectively when compared to existing techniques Deep Q Network (DQN) and Particle Swarm Optimization (PSO) - Grey Wolf Optimizer (GWO) approach.

关键词： Energy consumption scheduling algorithms Heuristic algorithms Dynamic scheduling Delays Resource management Data communication Particle swarm optimization Optimization Edge computing

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Developing a Mobile-Based Application System to Accelerate the Efficiency of the Course Rescheduling Process 3

Developing a Mobile-Based Application System to Accelerate t...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2023

作者： Kolandaisamy, Raenu Al Subaramaniam, Kasthuri Ap Wong, Steven Ucsi University Kuala Lumpur Malaysia

ISBN: (纸本)9798350319897

Clashing on the timetable is one of the common issues at the university level. With multiple faculties inside the university and several majors, it is not impossible for at least two lecturers to clash venue/time clashing. In this paper, the rescheduling can be done through Course Rescheduling Application System (CRAS). Our algorithm is designed to overcome inefficient procedures by providing a tool that helps lecturers directly update their subject time through a mobile application. Also, the CRAS system is created to avoid another clashing after the update is conducted. We provide a friendly and clear UI/UX design to the application as well as optimize a database through the implemented checking algorithm. The feedback shows our algorithm meets the requirement to produce a solution for the current UCSI University timetabling issue. Hence, our algorithm is most effective to be implemented with an optimal solution. © 2023 IEEE.

关键词： scheduling algorithms

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Brief Industry Paper: A DAG Generator with Full Topology Coverage 44

Brief Industry Paper: A DAG Generator with Full Topology Cov...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Fang, Yinjie Zhao, Shuai Guo, Yili Chang, Wanli Sun Yat-Sen University China Hunan University China Huawei Technologies China

ISBN: (纸本)9798350328578

The increasing computational demand promotes the application of parallel tasks with complex execution dependencies in industrial applications. The Directed Acyclic Graph (DAG) task model is widely applied with dedicated scheduling algorithms to understand and manage the execution of such systems. In order to validate the effectiveness of different DAG scheduling algorithms, DAG generators are often applied to produce synthesized DAGs for performance evaluation. However, existing DAG generators either fail to provide sufficient topology coverage or suffer from severe scalability issues, leading to biased and incomplete evaluation results. This paper proposes a novel DAG generator that provides full topology coverage under the given DAG structural parameters while eliminating isomorphic DAGs as well as redundant edges in each DAG. In addition, a verification method is constructed that enables topology coverage, isomorphic DAG identification, and constraint satisfaction of the generated DAGs. The experimental results show that compared to existing generators, the proposed DAG generator achieves full topology coverage and significantly reduces the number of DAGs being produced. The DAG generator proposed in this work provides a complete solution for synthesised DAG generation, enabling fair and comprehensive evaluation of DAG systems. © 2023 IEEE.

关键词： scheduling algorithms

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Runtime Composition of Iterations for Fusing Loop-carried Sparse Dependence 23

Runtime Composition of Iterations for Fusing Loop-carried Sp...

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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Cheshmi, Kazem Strout, Michelle Mehri Dehnavi, Maryam Electrical and Computer Engineering McMaster University Hamilton Canada Computer Science University of Arizona Tucson United States Computer Science University of Toronto Toronto Canada

ISBN: (纸本)9798400701092

Dependence between iterations in sparse computations causes inefficient use of memory and computation resources. This paper proposes sparse fusion, a technique that generates efficient parallel code for the combination of two sparse matrix kernels, where at least one of the kernels has loop-carried dependencies. Existing implementations optimize individual sparse kernels separately. However, this approach leads to synchronization overheads and load imbalance due to the irregular dependence patterns of sparse kernels, as well as inefficient cache usage due to their irregular memory access patterns. Sparse fusion uses a novel inspection strategy and code transformation to generate parallel fused code optimized for data locality and load balance. Sparse fusion outperforms the best of unfused implementations using ParSy and MKL by an average of 4.2× and is faster than the best of fused implementations using existing scheduling algorithms, such as LBC, DAGP, and wavefront by an average of 4× for various kernel combinations. © 2023 ACM.

关键词： scheduling algorithms

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