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Готові списки джерел за темами / Energy resource scheduling / Статті в журналах

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Статті в журналах з теми "Energy resource scheduling"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Bai, Hongying, Xiaotong Zhang, Yingdong Xie, Haiyan Gong, Zhuang Li, and Shilong Liu. "Resource Scheduling Based on Unequal Clustering in Internet of Things." Mobile Information Systems 2022 (January 25, 2022): 1–14. http://dx.doi.org/10.1155/2022/1810704.

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Resource scheduling in a fair and efficient manner is a significant challenge in the Internet of Things. Although unequal clustering is an effective technique for alleviating the “energy holes” problem in multihop communication, resource scheduling based on unequal clustering is scarcely conducted. In the present study, a new resource scheduling based on unequal clustering in the Internet of Things (RSUC) is proposed. In RSUC, unequal clustering and multihop routing are considered, and the “energy holes” problem is alleviated effectively. RSUC includes resource scheduling of intracluster commu

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12

Xu, Yong Qiang, and Ming Yin. "A Mobility-Aware Task Scheduling Model in Mobile Grid." Applied Mechanics and Materials 336-338 (July 2013): 1786–91. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1786.

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The mobile grids bring some additional features into the grid, such as mobility, energy-constrained, etc. And the task scheduling becomes a more challenge thing. We propose a mobile grid task scheduling model considering the mobility of both user and resource, and the resource energy consumption. Through analyzing the architecture of mobile grid, a mathematical model is built to calculate the average distance between the resource and Base Station (BS). Then, it can decide which mobile grid the mobile devices are apt to stay in, which can deal with the mobility of mobile devices. On the other h

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13

Karim, Faten, Sara Ghorashi, Salem Alkhalaf, Ishak Ben, and Sameer Alshetewi. "Modelling of horse herd optimization based multi objective task scheduling approach in cloud computing environment." Thermal Science 29, no. 2 Part B (2025): 1583–95. https://doi.org/10.2298/tsci2502583k.

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Cloud computing, which offers scalable and flexible resources, faces a key challenge in task scheduling, directly impacting system performance and user satisfaction. Effective scheduling is crucial for optimizing resource use and reducing makespan. The NP-completeness of the task scheduling problem complicates achieving optimal outcomes. Scheduling applications is critical in cloud computing due to the need to map future tasks to resources in real time. Many existing methods focus on makespan and resource consumption but overlook factors like energy usage and migration time, which affect web s

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14

Karunakaran Ponon, Nidhin. "Energy-Efficient Big Data Processing Using Adaptive Resource Scheduling in Cloud Environment." International Journal of Science and Research (IJSR) 14, no. 5 (2025): 478–88. https://doi.org/10.21275/sr25503191344.

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15

Zhang, Yi, and Yi Min Su. "Research on Resource Scheduling Algorithm in Cloud Computing Data Center." Advanced Materials Research 926-930 (May 2014): 2050–53. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2050.

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In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects: 1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low. 2. Since not fully considering the sched

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16

Kumari, B. D. "Optimizing cloud resource scheduling for enhanced energy efficiency." i-manager’s Journal on Cloud Computing 10, no. 2 (2023): 12. http://dx.doi.org/10.26634/jcc.10.2.20420.

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Energy consumption in cloud computing plays a vital role in operating costs for both the service provider and the cloud user. The cloud is scalable and can provide access as per demand. Due to this, resource access requests are increasing and submitted to the server. To manage all requests, scheduling is the solution to assign requests with the quality of service. To avoid high operating costs, resource scheduling needs to be energy-aware. In this paper, energy-aware resource scheduling in the cloud is proposed. The total resource utilization of each resource has been calculated, and energy is

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17

Xu, Xiaolong, Yuan Xue, Mengmeng Cui, Yuan Yuan, and Lianyong Qi. "Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing." Complexity 2019 (December 4, 2019): 1–14. http://dx.doi.org/10.1155/2019/6180135.

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By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and edge servers, massive resource requirements for implementing the increasing resource requests decrease the execution efficiency and affect the whole resource usage of edge servers. Therefore, it remains an issue to achieve dynamic scheduling of the com

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18

Jakwa, Ali Garba, Abdulsalam Yau Gital, Souley Boukari, and Fatima Umar Zambuk. "Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing." International Journal of Cloud Applications and Computing 13, no. 1 (2023): 1–16. http://dx.doi.org/10.4018/ijcac.324758.

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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 were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as requi

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19

Dewangan, Bhupesh Kumar, Amit Agarwal, Venkatadri M., and Ashutosh Pasricha. "Energy-Aware Autonomic Resource Scheduling Framework for Cloud." International Journal of Mathematical, Engineering and Management Sciences 4, no. 1 (2019): 41–55. http://dx.doi.org/10.33889/ijmems.2019.4.1-004.

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Cloud computing is a platform where services are provided through the internet either free of cost or rent basis. Many cloud service providers (CSP) offer cloud services on the rental basis. Due to increasing demand for cloud services, the existing infrastructure needs to be scale. However, the scaling comes at the cost of heavy energy consumption due to the inclusion of a number of data centers, and servers. The extraneous power consumption affects the operating costs, which in turn, affects its users. In addition, CO2 emissions affect the environment as well. Moreover, inadequate allocation

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20

Kaushik, Achal, and Deo Prakash Vidyarthi. "Green Energy Model for Grid Resource Allocation." International Journal of Grid and High Performance Computing 6, no. 2 (2014): 52–73. http://dx.doi.org/10.4018/ijghpc.2014040104.

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The computational grid helps in faster execution of compute intensive jobs. Many characteristic parameters are intended to be optimized while making resource allocation for job execution in computational grid. Most often, the green energy aspect, in which one tries for better energy utilization, is ignored while allocating the grid resources to the jobs. The conventional systems, which propose energy efficient scheduling strategies, ignore other Quality of Service parameters while scheduling the jobs. The proposed work tries to optimize the energy in resource allocation to make it a green ener

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21

Chana, Inderveer, and Tarandeep Kaur. "Resource Scheduling Techniques in Utility Computing." International Journal of Systems and Service-Oriented Engineering 4, no. 2 (2014): 44–65. http://dx.doi.org/10.4018/ijssoe.2014040104.

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Utility Computing offers on-demand services from a shared pool of resources and can be envisaged to be a benchmark in the IT development. The capability to provide on-demand services involves management of large number of resources that are geographically dispersed and thus poses a number of resource management and scheduling challenges in the domain of resource heterogeneity, dynamic resource locations and load balancing. Proficient resource allocations and efficient scheduling helps in achieving optimal resource utilization and hence enhances the performance of the system. This paper evaluat

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22

Fang, Juan, Yong Chen, and Shuaibing Lu. "Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing." Applied Sciences 10, no. 17 (2020): 6057. http://dx.doi.org/10.3390/app10176057.

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Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some real-time requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. In this paper, we focus on saving the energy of the system to provide an efficient scheduling strategy in edge computing. Our objective is to reduce the power consumption for the providers of the edge nodes while meeting the resources and delay constraints. We propose a two-stage scheduling strategy which includes the scheduling and resource provisioning. In

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23

Tan, Aiping, Yunuo Li, Yan Wang, and Yujie Yang. "Global Resource Scheduling for Distributed Edge Computing." Applied Sciences 13, no. 22 (2023): 12490. http://dx.doi.org/10.3390/app132212490.

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Анотація:

Recently, there has been a surge in interest surrounding the field of distributed edge computing resource scheduling. Notably, applications like intelligent traffic systems and Internet of Things (IoT) intelligent monitoring necessitate the effective scheduling and migration of distributed resources. In addressing this challenge, distributed resource scheduling must weigh the costs associated with resource scheduling, aiming to identify an optimal strategy amid various feasible solutions. Different application scenarios introduce diverse optimization objectives, including considerations such a

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24

He, Dan Dan, Hong Feng Hou, and Li Juan Wang. "Study on Energy-Saving Efficient Resource Scheduling Optimization Algorithm in Cloud Computing." Advanced Materials Research 915-916 (April 2014): 1285–91. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1285.

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It is a critical problem that schedules cloud resource in cloud environment. Based on the characteristics of cloud computing and analysis on cloud computing resource scheduling model framework, and the traditional resource scheduling of cloud computing is only concerned the maximum completion time of the task, without taking into account the energy consumption problem, this paper uses an improved particle swarm optimization, that is, when the optimal solution did not change for two generations, traversing through the chaotic particle method for local optimization to accelerate access to global

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25

George, Neema, and Anoop B. K. "Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (2023): 16–27. http://dx.doi.org/10.17762/ijritcc.v11i4s.6303.

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Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main

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26

Ranjit Kumar, A., S. Vemuri, and A. Halimah. "Fuel Resource Scheduling The Daily Scheduling Problem." IEEE Transactions on Power Apparatus and Systems PAS-104, no. 2 (1985): 313–20. http://dx.doi.org/10.1109/tpas.1985.319045.

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27

Joao, Soares, Morais Hugo, Sousa Tiago, Vale Zita, and Faria Pedro. "Day-ahead resource scheduling including demand response for electric vehicles." IEEE Transactions on Smart Grid 4, no. 1 (2013): 596–605. https://doi.org/10.1109/TSG.2012.2235865.

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The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested

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28

Wang, Jun, Xun Dou, Shizhen Wang, Zhen Wang, and Longzhang Zhao. "Integrated Energy Purchase-Sale Decision Making and Scheduling for Integrated Energy Service Provider Considering User Grading Dynamic Combination." E3S Web of Conferences 160 (2020): 02001. http://dx.doi.org/10.1051/e3sconf/202016002001.

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In an open energy market environment, energy retail competition is intensifying. integrated energy service provider (IEPS) with the right to operate regional integrated energy system. Under the requirement of distributed resource transaction access and scheduling security, how to integrate multi-level and multi-type user resources to participate in market operation, allocate resources within the region under its jurisdiction and improve the income of energy purchase and sale is the key for IESP to gain a favourable position in the market competition. Based on the operation framework of IESP in

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29

Qiao, Danxia, Lu Sun, Dianju Li, et al. "Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System." Sensors 24, no. 18 (2024): 6123. http://dx.doi.org/10.3390/s24186123.

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With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to “double near and far effect” in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the “double near and far effect” and make reason

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30

Dhindsa, Kanwalvir Singh. "Energy Efficient Resource Scheduling Framework for Cloud Computing." i-manager's Journal on Cloud Computing 2, no. 4 (2015): 1–15. http://dx.doi.org/10.26634/jcc.2.4.4904.

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31

Luo, Fengji, Zhao Yang Dong, Zhao Xu, Weicong Kong, and Fan Wang. "Distributed residential energy resource scheduling with renewable uncertainties." IET Generation, Transmission & Distribution 12, no. 11 (2018): 2770–77. http://dx.doi.org/10.1049/iet-gtd.2017.1136.

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32

Afzal, M., M. Naeem, M. Iqbal, M. Sharif, and Qi Huang. "Efficient energy resource scheduling for sustainable diversified farming." Journal of Renewable and Sustainable Energy 9, no. 4 (2017): 044902. http://dx.doi.org/10.1063/1.4997031.

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33

Chunlin, Li, Li FangYun, and Li Layuan. "Energy-aware grid resource scheduling: model and algorithm." International Journal of Computer Applications in Technology 37, no. 1 (2010): 39. http://dx.doi.org/10.1504/ijcat.2010.031523.

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34

Hao, Liang, Gang Cui, Ming Cheng Qu, and Wen De Ke. "Resource Scheduling Algorithm of Cloud Computing for Energy Optimization Base on Virtual Machine Migration." Advanced Materials Research 926-930 (May 2014): 3232–35. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3232.

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Анотація:

As the growing demand for cloud computing, the scale of cloud data centers increased gradually, so that the energy issues of cloud environments have become increasingly prominent. For the situation of energy consumption serious in cloud computing data center, the resource scheduling algorithm of cloud computing for energy optimization was designed base on the technology of virtual machine migration. The energy consumption of cloud data center was saved effectively through effective use of resources, rational allocation of resources scheduling. Simulation results showed that compared to sequenc

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35

Du, Guangyu, Hong He, and Qinggang Meng. "Energy-Efficient Scheduling for Tasks with Deadline in Virtualized Environments." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/496843.

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Анотація:

Data centers, as resource providers, take advantage of virtualization technology to achieve excellent resource utilization, scalability, and high availability. However, large numbers of computing servers containing virtual machines of data centers consume a tremendous amount of energy. Thus, it is necessary to significantly improve resource utilization. Among the many issues associated with energy, scheduling plays a very important role in successful task execution and energy consumption in virtualized environments. This paper seeks to implement an energy-efficient task scheduling algorithm fo

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36

V, Sindhu, Prakash M, and Mohan Kumar P. "Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment." Symmetry 14, no. 11 (2022): 2340. http://dx.doi.org/10.3390/sym14112340.

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Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays whi

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37

Morillo Torres, Daniel, Federico Barber, and Miguel A. Salido. "A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 1 (2017): 293–305. http://dx.doi.org/10.1177/0954405417711734.

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This article focuses on obtaining sustainable and energy-efficient solutions for limited resource programming problems. To this end, a model for integrating [Formula: see text] and energy consumption objectives in multi-mode resource-constrained project scheduling problems (MRCPSP-ENERGY) is proposed. In addition, a metaheuristic approach for the efficient resolution of these problems is developed. In order to assess the appropriateness of theses proposals, the well-known Project Scheduling Problem Library is extended (called PSPLIB-ENERGY) to include energy consumption to each Resource-Constr

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Kak, Sanna Mehraj, Parul Agarwal, and M. Afshar Alam. "Task Scheduling Techniques for Energy Efficiency in the Cloud." EAI Endorsed Transactions on Energy Web 9, no. 39 (2022): e6. http://dx.doi.org/10.4108/ew.v9i39.1509.

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Energy efficiency is a key goal in cloud datacentre since it saves money and complies with green computing standards. When energy efficiency is taken into account, task scheduling becomes much more complicated and crucial. Execution overhead and scalability are major concerns in current research on energy-efficient task scheduling. Machine learning has been widely utilized to solve the problem of energy-efficient task scheduling, however, it is usually used to anticipate resource usage rather than selecting the schedule. The bulk of machine learning approaches are used to anticipate resource c

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Gu, Xinyang, Zhansheng Duan, Guangyuan Ye, and Zhenjun Chang. "Virtual Node-Driven Cloud–Edge Collaborative Resource Scheduling for Surveillance with Visual Sensors." Sensors 25, no. 2 (2025): 535. https://doi.org/10.3390/s25020535.

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For public security purposes, distributed surveillance systems are widely deployed in key areas. These systems comprise visual sensors, edge computing boxes, and cloud servers. Resource scheduling algorithms are critical to ensure such systems’ robustness and efficiency. They balance workloads and need to meet real-time monitoring and emergency response requirements. Existing works have primarily focused on optimizing Quality of Service (QoS), latency, and energy consumption in edge computing under resource constraints. However, the issue of task congestion due to insufficient physical resourc

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S. P. Balakannan, S. Alangaram,. "Optimizing Task Scheduling in Cloud Data Centres with Dynamic Resource Allocation Using Genetic Algorithm (TSOGA)." Journal of Electrical Systems 20, no. 3s (2024): 62–72. http://dx.doi.org/10.52783/jes.1127.

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Nowadays, Massive business applications are increasingly giving attention to cloud computing data centres because of its high potential, adaptability, and efficiency in supplying several sources of both software and hardware to support networked consumers. The criteria for autonomy of virtual machines necessitate a flexible resource allocation strategy for Virtual Machines (VMs) .The majority of resource utilization models were inaccurate, making it impossible to determine the virtual machine's energy usage directly from the hardware. Due to the size of modern data centres and the constantly c

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Wei, Zhe, Xuebin Yu, and Lei Zou. "Multi-Resource Computing Offload Strategy for Energy Consumption Optimization in Mobile Edge Computing." Processes 10, no. 9 (2022): 1762. http://dx.doi.org/10.3390/pr10091762.

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The energy consumption optimization of edge devices in the mobile edge computing environment is mainly based on computational offload strategy. Most of the current common computing offload strategies only consider a single computing resource and do not comprehensively consider different kinds of computing resources in mobile edge computing environments, which cannot fully reduce the energy consumption of edge devices under the condition of ensuring response time constraints. To solve this problem, a multi-resource computing unloading energy consumption model is proposed in the mobile edge comp

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J. Radha, Et al. "Bivariate Correlative Modest Adaptive Boost Resource Aware Task Scheduling in Cloud Environment." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 538–50. http://dx.doi.org/10.17762/ijritcc.v11i10.8521.

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Cloud computing is a rapidly evolving paradigm that provides accessible and virtualized resources through Internet technology. In this model, Cloud Service Providers (CSPs) offer online access to computing resources for users to execute their application tasks. Task scheduling in cloud computing involves the allocation of computational tasks to available resources within the cloud environment. The primary objectives of task scheduling are to optimize resource utilization, minimize task completion time, and enhance overall system performance. Task scheduling plays a vital role in cloud resource

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Medishetti, Santhosh Kumar, Rameshwaraiah Kurupati, Rakesh Kumar Donthi, and Ganesh Reddy Karri. "Energy and Deadline Aware Scheduling in Multi Cloud Environment Using Water Wave Optimization Algorithm." International Journal of Intelligent Systems and Applications 17, no. 3 (2025): 48–64. https://doi.org/10.5815/ijisa.2025.03.04.

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Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient task scheduling in Cloud Computing (CC) remains a critical challenge due to the need to balance energy consumption and deadline adherence. Existing scheduling approaches often suffer from high energy consumption and inefficient resource utilization, failing to meet stringent deadline constraints, especially under dynamic workload variations. To address these limitations, this study proposes an Energy-Deadline Aware Task Scheduling using the Water Wave Opti

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Liu, Weimin, Chen Li, Aiyun Zheng, Zhi Zheng, Zhen Zhang, and Yao Xiao. "Fog Computing Resource-Scheduling Strategy in IoT Based on Artificial Bee Colony Algorithm." Electronics 12, no. 7 (2023): 1511. http://dx.doi.org/10.3390/electronics12071511.

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As the amount of data input increases, fog devices on IoT edge networks become increasingly inefficient. However, a well-designed fog computing resource-scheduling strategy can help to reduce excessive time delays and energy consumption. Therefore, in this paper, we propose an efficient fog computing resource-scheduling strategy. First, we used particle swarm optimization (PSO) to determine the optimal load balance among fog nodes and to obtain the optimal computation time and energy consumption in a single fog cluster. Second, we designed a particle swarm genetic joint optimization artificial

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Mao, Li, De Yu Qi, Wei Wei Lin, Bo Liu, and Ye Da Li. "An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization." International Journal of Grid and High Performance Computing 8, no. 2 (2016): 43–57. http://dx.doi.org/10.4018/ijghpc.2016040103.

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With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the s

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A, Ganesh, K. Sree Divya, Chinthakunta Sasikala, et al. "Optimizing Task Scheduling: Exploring Advanced Machine Learning in Dew-Powered Cloud Environments." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3701–14. http://dx.doi.org/10.12694/scpe.v25i5.3111.

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Research into Dew computing environments has recently emerged as a result of the increasing prevalence and processing power of mobile and IoT devices. In these settings, even low-powered devices can share some of their computational resources with their neighbors. This paper proposes a novel approach to workflow scheduling in dew enabled cloud computing environment, called Deep Q-learning (DQN) + Chronological Geese Migration Optimization (CGMO). DQN is a deep learningbased method for scheduling workflows, while CGMO is a hybrid optimization algorithm that combines the chronological idea and t

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Yassine, Farah, Melhem El Helou, Samer Lahoud, and Oussama Bazzi. "Energy-Efficient Uplink Scheduling in Narrowband IoT." Sensors 22, no. 20 (2022): 7744. http://dx.doi.org/10.3390/s22207744.

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This paper presents a detailed study of uplink scheduling in narrowband internet of things (NB-IoT) networks. As NB-IoT devices need a long battery lifetime, we aim to maximize energy efficiency while satisfying the main requirements for NB-IoT devices. Also, as the NB-IoT scheduling problem is divided into link adaptation problem and resource allocation problem, this paper investigates the correlation between these two problems. Accordingly, we propose two scheduling schemes: the joint scheduling scheme, where the two problems are combined as one optimization problem, and the successive sched

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Li, Chunlin, Jing Zhang, and Yi Chen. "Media Cloud Service Scheduling Optimization for Resource-Intensive Mobile Application." International Journal of Cooperative Information Systems 27, no. 04 (2018): 1850008. http://dx.doi.org/10.1142/s0218843018500089.

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How to reduce energy consumption, improve resource utilization and put forward efficient resource management model so as to improve the media cloud performance and mobile users’ quality of service (QoS) is the problem needed to be addressed. Our proposed media cloud distributed scheduling model aims to maximize the utility of media cloud. The media cloud distributed scheduling policy for resource-intensive mobile application includes media service provisioning and cloud resource scheduling among media cloud datacenter. The media cloud service scheduling optimization algorithms include two sub-

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Zhang, Haifeng, Xiu Ji, Wei Wang, Mingge Li, and Jiajun Zhang. "Study on enhanced new energy source-load interaction response strategy based on multiple time scales." Journal of Physics: Conference Series 2781, no. 1 (2024): 012020. http://dx.doi.org/10.1088/1742-6596/2781/1/012020.

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Abstract Rapid socio-economic development has also brought about problems such as resource depletion and ecological and environmental pollution. Electricity is the most important form of energy supply for human beings, and its production and use are undergoing fundamental changes. This thesis conducts an in-depth study on the source-load interaction scheduling method in new energy power grids., which realizes a safe, economical, low-carbon and environmentally friendly intelligent scheduling mode through two-way interaction between the demand side and the grid. Firstly, the virtual power plant

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Mohamed, Sathik *. Ayman Alhalaybeh. "MUTUAL-BENEFIT APPROACH FOR ENERGY-EFFICIENT SEAMLESS MOBILE APPLICATION EXECUTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 3 (2019): 70–78. https://doi.org/10.5281/zenodo.2595805.

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The MUTUAL-BENEFIT approach exploits optimal task offloading, task scheduling, resource allocation, and provider selection process to execute the mobile cloud applications.  The proposed approach enabled mobile cloud environment ensures the seamless application execution resulting in extending the battery lifetime and the optimal profit. The mobile cloud task scheduling and resource allocation process schedules the offloaded tasks and allocates the resources merely based on the availability and the resource requirements. The additional consideration of the proposed algorithm in mobile clo

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