Готові списки джерел за темами / Energy resource scheduling

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Добірка наукової літератури з теми "Energy resource scheduling"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Energy resource scheduling"

Zhang, Ruichao, and Lin Zhou. "Dispatching Method of Distributed Energy Storage Resources in Substation Based on Peak-shaving Compensation Response Model." Journal of Physics: Conference Series 2662, no. 1 (2023): 012022. http://dx.doi.org/10.1088/1742-6596/2662/1/012022.

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Abstract The conventional distributed energy storage resource scheduling method is mainly based on automatic load demand response scheduling, and the load response gap between peak hours and low hours is large, which affects the economic benefits of energy storage resource scheduling. Therefore, a distributed energy storage resource scheduling method based on a peak shaving compensation response model is designed. The cluster scheduling characteristics of distributed energy storage resources are extracted, and the distributed energy storage resources are charged and discharged synchronously at

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Datta, Jayanta, Subhamita Mukherjee, and Indrajit Pan. "Prioritized Energy Efficient Resource Scheduling in Cloud Computing." ITM Web of Conferences 54 (2023): 01005. http://dx.doi.org/10.1051/itmconf/20235401005.

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Resource scheduling in cloud computing is one of the impactful area of research. Cloud service providers maintain its efficacy through proper resource management schemes. Users experience seamless cloud services when cloud service provider manages its resources efficiently. Another aspect in resource management is energy efficient schemes. Energy efficiency largely depends on employment of minimum number of resource servers. This article discusses energy efficient resource scheduling mechanism on multi-layer prioritized resource requests. Resource requests are segregated into three categories

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Narendrababu Reddy, G., and S. Phani Kumar. "Regressive Whale Optimization for Workflow Scheduling in Cloud Computing." International Journal of Computational Intelligence and Applications 18, no. 04 (2019): 1950024. http://dx.doi.org/10.1142/s146902681950024x.

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Cloud computing is the advancing technology that aims at providing services to the customers with the available resources in the cloud environment. When the multiple users request service from the cloud server, there is a need of the proper scheduling of the resources to attain good customer satisfaction. Therefore, this paper proposes the Regressive Whale Optimization (RWO) algorithm for workflow scheduling in the cloud computing environment. RWO is the meta-heuristic algorithm, which schedules the task depending on a fitness function. Here, the fitness function is defined based on three majo

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Shuja, Junaid, Kashif Bilal, Sajjad Ahmad Madani, and Samee U. Khan. "Data center energy efficient resource scheduling." Cluster Computing 17, no. 4 (2014): 1265–77. http://dx.doi.org/10.1007/s10586-014-0365-0.

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Sreenivasulu Gogula, Et al. "A Study Resource Optimization Techniques Based Job Scheduling in Cloud Computing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1714–22. http://dx.doi.org/10.17762/ijritcc.v11i10.8746.

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Cloud computing has revolutionized the way businesses and individuals utilize computing resources. It offers on-demand access to a vast pool of virtualized resources, such as processing power, storage, and networking, through the Internet. One of the key challenges in cloud computing is efficiently scheduling jobs to maximize resource utilization and minimize costs. Job scheduling in cloud computing involves allocating tasks or jobs to available resources in an optimal manner. The objective is to minimize job completion time, maximize resource utilization, and meet various performance metrics

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Chang, Daofang, Ting Fang, Junliang He, and Danping Lin. "Defining Scheduling Problems for Key Resources in Energy-Efficient Port Service Systems." Scientific Programming 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7053962.

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This paper addresses the problem of key resource scheduling of container terminals for energy-efficient operation. A combination of key resource scheduling and energy-efficient operation in container terminals is firstly described. An energy-efficient evaluation model of the key resource scheduling is then proposed. The objective set, decision variable set, and constraint set of key resource scheduling of a container terminal for energy-efficient operation are established in this paper. At the same time, their mapping relationship is carefully analyzed and the system structure of the key resou

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Xiang, Xinyu, Chengyao Gong, Xingchen Zong, Qing Luo, Tian Xia, and Tao Lu. "Optimal Dispatch of Multi-energy Microgrid Considering Flexible Resources Aggregation." Journal of Physics: Conference Series 2491, no. 1 (2023): 012024. http://dx.doi.org/10.1088/1742-6596/2491/1/012024.

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Abstract Distributing energy has facilitated the interaction between electricity, heating, and gas systems. Aggregating flexible resources in the optimal scheduling process of multi-energy microgrids can promote the complementary coordination of multi-energy systems and reduce useless energy waste. In this paper, flexible resource aggregation analysis is added to the optimal scheduling problem of multi-energy microgrids. Firstly, a resource aggregation model based on Mincowski addition is proposed to fully characterize the schedulable potential of flexible resources. Then, a multi-energy micro

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Jakwa, Ali Garba, Dr Gital, Professor Souley, and Dr Fatima Umar Zambuk Umar Zambuk. "Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing." International Journal of Advances in Scientific Research and Engineering 09, no. 02 (2023): 20–28. http://dx.doi.org/10.31695/ijasre.2023.9.2.3.

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Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of Internet of Things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques are reassessed based on deterministic and meta-heuristics to find out solution to scheduling problem in fog computing. This paper proposes Hybrid Meta-Heuristics Optimiz

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Liu, Shuaishuai, Xinyu Ma, Yuanfei Jia, and Yue Liu. "An Energy-Saving Task Scheduling Model via Greedy Strategy under Cloud Environment." Wireless Communications and Mobile Computing 2022 (April 15, 2022): 1–13. http://dx.doi.org/10.1155/2022/8769674.

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Cloud computing, an emerging computing paradigm, has been widely concerned due to its high scalability and availability. An essential stage of cloud computing is cloud resource management. Currently, the existing research about cloud computing technology has two prevalent disadvantages: high energy consumption and low resource utilization. Considering greedy scheduling is an effective strategy for cloud resource management technology in cloud computing, particularly in improving resource utilization and reducing energy consumption, we consider the heterogeneous characteristics of resources to

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Xu, Hui, Zhong Fu Tan, Huan Huan Li, and Zhi Hong Chen. "Profits Distribution Optimizing Model of Multi-Type Generation Resources Joint Scheduling." Applied Mechanics and Materials 441 (December 2013): 1081–84. http://dx.doi.org/10.4028/www.scientific.net/amm.441.1081.

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Power generation resource joint scheduling optimizing is of great significance for electric power system run economically and to achieve energy saving targets. To compare multi-types power generation resource scheduling models economic and environment benefits under different scheduling objectives and constraints, this paper took the unit output, generation resources, pollution and other aspects of constraints, respectively, took the lowest coal consumption and the smallest pollutant emissions as the target established the contract power, the energy-saving generation dispatching optimization m

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Дисертації з теми "Energy resource scheduling"

Fayyaz, Ahmad. "Energy Efficient Resource Scheduling Methodologies for Cluster and Cloud Computing." Diss., North Dakota State University, 2015. https://hdl.handle.net/10365/27936.

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One of the major challenges in the High Performance Computing (HPC) clusters, Data Centers, and Cloud Computing paradigms is intelligent power management to improve energy efficiency. The key contribution of the presented work is the modeling of a Power Aware Job Scheduler (PAJS) for HPC clusters, such that the: (a) threshold voltage is adjusted judiciously to achieve energy efficiency and (b) response time is minimized by scaling the supply voltage. The key novelty in our work is utilization of the Dynamic Threshold-Voltage Scaling (DTVS) for the reduction of cumulative power utilized by each

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Alnowiser, Abdulaziz Mohammed. "TOWARD ENERGY-EFFICIENT SCHEDULING USING WEIGHTED ROUND-ROBIN AND VM REUSE." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1303.

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AN ABSTRACT OF THE THESIS OF Abdulaziz M. AlNowiser, for the Master of Science degree in Computer Science, presented on November 1, 2013, at Southern Illinois University Carbondale. TITLE: TOWARD ENERGY-EFFICIENT SCHEDULING USING WEIGHTED ROUND- ROBIN AND VM REUSE MAJOR PROFESSOR: Dr. Michelle M. Zhu In recent years, the rapid evolving Cloud Computing technologies multiply challenges such as minimizing power consumption and meeting Quality-of-Services (QoS) requirements in the presence of heavy workloads from a large number of users using shared computing resources. Powering a middle-sized dat

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Renaud-Goud, Paul. "Energy-aware scheduling : complexity and algorithms." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2012. http://tel.archives-ouvertes.fr/tel-00744247.

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In this thesis we have tackled a few scheduling problems under energy constraint, since the energy issue is becoming crucial, for both economical and environmental reasons. In the first chapter, we exhibit tight bounds on the energy metric of a classical algorithm that minimizes the makespan of independent tasks. In the second chapter, we schedule several independent but concurrent pipelined applications and address problems combining multiple criteria, which are period, latency and energy. We perform an exhaustive complexity study and describe the performance of new heuristics. In the third c

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Celik, Berk. "Coordination mechanisms for smart homes electric energy management through distributed resource scheduling with demand response programs." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCA013/document.

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La modernisation des réseaux électriques via ce que l'appelle aujourd'hui les réseaux intelligents (ou smart grids) promet des avancées pour permettre de faire face à une augmentation de la demande mondiale ainsi que pour faciliter l'intégration des ressources décentralisées. Grâce à des moyens de communication et de calcul avancés, les smart grids offrent de nouvelles possibilités pour la gestion des ressources des consommateurs finaux, y compris pour de petits éléments comme de l'électroménager. Cependant, ce type de gestion basée sur des décisions prises indépendamment peuvent causer des pe

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SEVERINI, Marco. "Energy and resources management in Micro Grid environments." Doctoral thesis, Università Politecnica delle Marche, 2017. http://hdl.handle.net/11566/245444.

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Nonostante le tecnologie Micro Grid siano ancora in fase sperimentale, il potenziale miglioramento di efficienza robustezza e flessibilità è significativo. Lo spreco di energia e le fluttuazioni del carico possono essere notevolmente ridotte, ciononostante un sistema automatico che gestisca correttamente le risorse risulta necessario per sviluppare completamente il potenziale delle risorse disponibili. Al riguardo, un approccio alla gestione dell’energia, basato su tecniche Mixed Integer Linear Programming è stato esaminato, implementato e proposto. La dissertazione copre gli aspetti teoric

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Pietri, Ilia. "Cost-efficient resource management for scientific workflows on the cloud." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/costefficient-resource-management-for-scientific-workflows-on-the-cloud(4cfe73ce-1de9-411b-8288-f463d6b52680).html.

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Scientific workflows are used in many scientific fields to abstract complex computations (tasks) and data or flow dependencies between them. High performance computing (HPC) systems have been widely used for the execution of scientific workflows. Cloud computing has gained popularity by offering users on-demand provisioning of resources and providing the ability to choose from a wide range of possible configurations. To do so, resources are made available in the form of virtual machines (VMs), described as a set of resource characteristics, e.g. amount of CPU and memory. The notion of VMs enab

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Al-Saedy, Murtadha. "Performance analysis of multicell coordination in cellular wireless networks." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12606.

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In this thesis, multicell coordination for wireless cellular networks is studied, whereby various approaches have been conducted to tackle this issue. Firstly, the coverage probability and e ective capacity in downlink multiple-input multiple-output (MIMO) cellular system are considered. Two scenarios are investigated; in the rst scenario, it is assumed that the system employs distance-based fractional power control with no multicell coordination. For the second scenario, it is assumed that the system implements multicell coordinated beamforming so as to cancel inter-cell interference. The bas

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Zeng, Gang, Tetsuo Yokoyama, Hiroyuki Tomiyama, and Hiroaki Takada. "A Generalized Framework for Energy Savings in Real-Time Multiprocessor Systems." IEEE, 2008. http://hdl.handle.net/2237/12101.

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Djemai, Ibrahim. "Joint offloading-scheduling policies for future generation wireless networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS007.

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Анотація:

Les défis posés par le nombre croissant d'appareils connectés, la forte consommation d'énergie et l'impact environnemental dans les réseaux sans fil d'aujourd'hui et de demain retiennent de plus en plus l'attention. De nouvelles technologies telles que le cloud mobile de périphérie (Mobile Edge Computing) ont vu le jour pour rapprocher les services en nuage des appareils et remédier à leurs limitations en matière de calcul. Le fait de doter ces appareils et les nœuds du réseau de capacités de récolte d'énergie (Energy Harvesting) est également prometteur pour permettre de consommer de l'énergi

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Vasudevan, Meera. "Profile-based application management for green data centres." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/98294/1/Meera_Vasudevan_Thesis.pdf.

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This thesis presents a profile-based application management framework for energy-efficient data centres. The framework is based on a concept of using Profiles that provide prior knowledge of the run-time workload characteristics to assign applications to virtual machines. The thesis explores the building of profiles for applications, virtual machines and servers from real data centre workload logs. This is then used to inform static and dynamic application assignment, and consolidation of applications.

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Книги з теми "Energy resource scheduling"

Thompson, John S., Jing Yang, Himal A. Suraweera, and Alessio Zappone. Green Communications for Energy-Efficient Wireless Systems and Networks. Institution of Engineering & Technology, 2020.

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Shally. Energy Efficient Scheduling of Resources in Cloud Datacenter. Victorian Government - Department of Environment & Primary Industries, 2023.

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Частини книг з теми "Energy resource scheduling"

Agrawal, Pragati, Damien Borgetto, Carmela Comito, et al. "Scheduling and Resource Allocation." In Large-Scale Distributed Systems and Energy Efficiency. John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118981122.ch8.

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Jin, Shunfu, and Wuyi Yue. "Energy-Efficient Task Scheduling Strategy." In Resource Management and Performance Analysis of Wireless Communication Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7756-7_20.

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Venkataswamy, Vanamala, Jake Grigsby, Andrew Grimshaw, and Yanjun Qi. "RARE: Renewable Energy Aware Resource Management in Datacenters." In Job Scheduling Strategies for Parallel Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22698-4_6.

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Cuijpers, Pieter J. L., Jonas Hansen, and Kim G. Larsen. "Safe and Infinite Resource Scheduling Using Energy Timed Automata." In Theoretical Aspects of Software Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64626-3_5.

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González, Rubén A., Jerónimo Ramos-Teodoro, Francisco Rodríguez, and Manuel Berenguel. "Multi-resource Scheduling of the Water–Energy–Food Nexus in Agro-Industrial Environments." In Green Energy and Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69015-0_8.

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Xue, Yanfen, Guisheng Fan, Huiqun Yu, and Huaiying Sun. "Energy-Aware Resource Scheduling with Fault-Tolerance in Edge Computing." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30709-7_28.

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Ahmad, Mahmoud Al, Sudhansu Shekhar Patra, and Rabindra K. Barik. "Energy-Efficient Resource Scheduling in Fog Computing Using SDN Framework." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_57.

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Almeida, Jos, Joo Soares, Bruno Canizes, Ivn Razo-Zapata, and Zita Vale. "Intraday Energy Resource Scheduling for Load Aggregators Considering Local Market." In 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87869-6_22.

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Mavromoustakis, Constandinos X., Christos Dimitriou, George Mastorakis, Athina Bourdena, and Evangelos Pallis. "Using Traffic Diversities for Scheduling Wireless Interfaces for Energy Harvesting in Wireless Devices." In Resource Management in Mobile Computing Environments. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06704-9_22.

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Tran, Xuan T. "Resource-Aware Scheduling in Heterogeneous, Multi-core Clusters for Energy Efficiency." In Advances in Information and Communication Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49073-1_56.

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Тези доповідей конференцій з теми "Energy resource scheduling"

Song, Minggang, Nan Zou, Pengfei Pan, Junhan Qin, and Yuening Shi. "Flexible Resource Aggregation Model and Scheduling for Virtual Power Plant." In 2024 Second International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE). IEEE, 2024. http://dx.doi.org/10.1109/iccsie61360.2024.10698564.

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Aindala, Prashanthi, K. Shivakumar, Paladugula Haripriya, Ramagowni Sivakumar, Rakesh Kumar Donthi, and Santhosh Kumar Medishetti. "AVOA: Energy and Resource Aware Scheduling Through Cloud Environment." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11035432.

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Li, Zhifeng, Yurun Ye, Wenxin Liu, and Ang Lu. "Integrated Energy System Flexible Energy Resource Optimal Scheduling Based on Stackelberg Game." In 2024 The 9th International Conference on Power and Renewable Energy (ICPRE). IEEE, 2024. https://doi.org/10.1109/icpre62586.2024.10768682.

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Kaushik, Harshal D., Roshni Anna Jacob, Souma Chowdhury, and Jie Zhang. "Distribution Network Restoration: Resource Scheduling Considering Coupled Transportation-Power Networks." In 2024 IEEE Kansas Power and Energy Conference (KPEC). IEEE, 2024. http://dx.doi.org/10.1109/kpec61529.2024.10676189.

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Pan, Pengfei, Meng Zhao, Yongjun Gao, Xiru Wang, Ying Zhao, and Hongyuan Ma. "Flexible Resource Virtual Power Plant Aggregation Scheduling Method Considering Uncertainty and Demand Response." In 2024 Second International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE). IEEE, 2024. http://dx.doi.org/10.1109/iccsie61360.2024.10698203.

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Zhu, Yujie, Haoyun Yuan, Xingyao Xiong, Jiaqi Ruan, and Shi Chen. "Flexible Resource Scheduling of Distribution Network Based on Deep Reinforcement Learning." In 2025 IEEE International Conference on Power and Integrated Energy Systems (ICPIES). IEEE, 2025. https://doi.org/10.1109/icpies65420.2025.11070188.

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Xia, Qiu, Jian-Hu Lv, Jian Geng, Xiao-Rui Guo, and Jing Zhou. "Research on Modeling and Optimization Scheduling of Electrochemical Energy Storage System under Multi Resource Coordination." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10991747.

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Wang, Zezhou, Nengwen Shu, Jianqin Lu, Jianfei Bao, Yuanchao Lu, and Jianzhou Cheng. "Optimization Scheduling Method of Medium and Low Voltage AC / DC Distribution System for Distributed Resource Access." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10991729.

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Shi, Yuening, Guangdi Li, Zicheng Wang, and Shuang Wang. "Research on Resource Allocation and Scheduling Optimization of Virtual Power Plants Considering Uncertainty." In 2025 7th Asia Energy and Electrical Engineering Symposium (AEEES). IEEE, 2025. https://doi.org/10.1109/aeees64634.2025.11020041.

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Morais, H., Z. Vale, and T. Sousa. "Energy resource scheduling considering reactive power management." In CIRED 2012 Workshop: Integration of Renewables into the Distribution Grid. IET, 2012. http://dx.doi.org/10.1049/cp.2012.0802.

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Звіти організацій з теми "Energy resource scheduling"

Jengo, Eunice, Salome Maro, and Björn Haßler. Leveraging IOT Systems to Assess and Reduce the Impact of Climate Change in Learning Environments. Open Development & Education, 2025. https://doi.org/10.53832/opendeved.1185.

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Learning environments in low- and middle-income countries are increasingly impacted by climate change, facing challenges such as extreme heat, poor air quality, and noise pollution. However, there is limited research that comprehensively assesses these conditions across diverse climatic zones. This study addresses that gap by designing and piloting a low-cost, energy-efficient, solar-powered IoT system to monitor environmental conditions in schools. Using a mixed-methods, multi-phase research design, the study deploys sensor networks in Tanzanian classrooms—including retrofitted and control sp

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Fontes, Dalila, S. Mahdi Homayouni, and Joao Fernandes. Energy-efficient job shop scheduling problem with transport resources considering speed adjustable resources. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2307p3639977.

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