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Journal articles on the topic 'Adaptive resource allocation'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Pourebrahimi, Behnaz, and Koen Bertels. "Self-Adaptive Economic-Based Resource Allocation in Ad-Hoc Grids." International Journal of Embedded and Real-Time Communication Systems 3, no. 2 (2012): 111–30. http://dx.doi.org/10.4018/jertcs.2012040106.

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Resource allocation is the process of discovering and allocating resources to requested tasks in a way that satisfy both user jobs and resource administrators. In ad-hoc Grids, resource allocation is a challenging undertaking as tasks and resources are distributed, heterogeneous in nature, owned by different individuals or organizations and they may arise spontaneously at any time with various requirements and availabilities. In this paper, the authors address an economic-based framework for resource allocation in ad-hoc Grids to deal with the dynamic nature of such networks. Within the econom

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2

Busemeyer, Jerome R., Kenneth N. Swenson, and Alejandro Lazarte. "An adaptive approach to resource allocation." Organizational Behavior and Human Decision Processes 38, no. 3 (1986): 318–41. http://dx.doi.org/10.1016/0749-5978(86)90004-x.

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3

Stephens, Kenneth R., William R. Hutchison, Sharon S. Hormby, and Thomas M. Bell. "Dynamic resource allocation using adaptive networks." Neurocomputing 2, no. 1 (1990): 9–16. http://dx.doi.org/10.1016/0925-2312(90)90012-g.

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4

Avgeris, Marios, Dimitrios Dechouniotis, Nikolaos Athanasopoulos, and Symeon Papavassiliou. "Adaptive Resource Allocation for Computation Offloading." ACM Transactions on Internet Technology 19, no. 2 (2019): 1–20. http://dx.doi.org/10.1145/3284553.

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5

Huang, Binbin, Zhongjin Li, Yunqiu Xu, et al. "Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing." Wireless Communications and Mobile Computing 2020 (July 2, 2020): 1–17. http://dx.doi.org/10.1155/2020/2765491.

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Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of th

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6

Petrovska, Inna, and Heorhii Kuchuk. "ADAPTIVE RESOURCE ALLOCATION METHOD FOR DATA PROCESSING AND SECURITY IN CLOUD ENVIRONMENT." Advanced Information Systems 7, no. 3 (2023): 67–73. http://dx.doi.org/10.20998/2522-9052.2023.3.10.

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Subject of research: methods of resource allocation of the cloud environment. The purpose of the research: to develop a method of resource allocation that will improve the security of the cloud environment. At the same time, effective data processing should be achieved. Method characteristics. The article discusses the method of adaptive resource allocation in cloud environments, focusing on its significance for data processing and enhanced security. A notable feature of the method is the consideration of external influences when calculating the characteristics of cloud resource requests and p

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ZHANG, Wei, Li RUAN, Mingfa ZHU, et al. "SLA_Driven Adaptive Resource Allocation for Virtualized Servers." IEICE Transactions on Information and Systems E95.D, no. 12 (2012): 2833–43. http://dx.doi.org/10.1587/transinf.e95.d.2833.

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8

Edmondson, James, and Douglas Schmidt. "Multi-agent distributed adaptive resource allocation (MADARA)." International Journal of Communication Networks and Distributed Systems 5, no. 3 (2010): 229. http://dx.doi.org/10.1504/ijcnds.2010.034946.

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9

Ramírez-Velarde, Raul, Andrei Tchernykh, Carlos Barba-Jimenez, Adán Hirales-Carbajal, and Juan Nolazco-Flores. "Adaptive Resource Allocation with Job Runtime Uncertainty." Journal of Grid Computing 15, no. 4 (2017): 415–34. http://dx.doi.org/10.1007/s10723-017-9410-6.

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10

Han, Bohyung, Seong-Wook Joo, and Larry S. Davis. "Multi-Camera Tracking with Adaptive Resource Allocation." International Journal of Computer Vision 91, no. 1 (2010): 45–58. http://dx.doi.org/10.1007/s11263-010-0373-3.

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11

Tereso, Anabela P., M. Madalena T. Araújo, and Salah E. Elmaghraby. "Adaptive resource allocation in multimodal activity networks." International Journal of Production Economics 92, no. 1 (2004): 1–10. http://dx.doi.org/10.1016/j.ijpe.2003.09.005.

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12

Vermeulen, Ivan B., Sander M. Bohte, Sylvia G. Elkhuizen, Han Lameris, Piet J. M. Bakker, and Han La Poutré. "Adaptive resource allocation for efficient patient scheduling." Artificial Intelligence in Medicine 46, no. 1 (2009): 67–80. http://dx.doi.org/10.1016/j.artmed.2008.07.019.

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13

Afzali, Mahboubeh, Kamalrulnizam AbuBakar, and Jaime Lloret. "Adaptive Resource Allocation for WiMAX Mesh Network." Wireless Personal Communications 107, no. 2 (2019): 849–67. http://dx.doi.org/10.1007/s11277-019-06305-1.

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14

Wang, Lei, and Yu Yun Kang. "Resource Optimization Based on Adaptive Genetic Algorithm." Applied Mechanics and Materials 339 (July 2013): 784–88. http://dx.doi.org/10.4028/www.scientific.net/amm.339.784.

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In order to allocate tasks and optimize resources well in dynamical manufacturing environment, the model for task allocation is established. An adaptive genetic algorithm (AGA) is applied to deal with it. A machine-based encoding approach is also adopted. The simulation results testify the validity of this method, and therefore the task allocation and resources optimization problem could be dealt with efficiently.

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15

Xu, XiaoDong, Da Wang, XiaoFeng Tao, and Tommy Svensson. "Resource pooling for frameless network architecture with adaptive resource allocation." Science China Information Sciences 56, no. 2 (2013): 1–12. http://dx.doi.org/10.1007/s11432-013-4788-7.

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16

Malhotra, Manisha, and Rahul Malhotra. "Cloud Adaptive Resource Allocation Mechanism for Efficient Parallel Processing." International Journal of Cloud Applications and Computing 4, no. 4 (2014): 1–6. http://dx.doi.org/10.4018/ijcac.2014100101.

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As cloud based services becomes more assorted, resource provisioning becomes more challenges. This is an important issue that how resource may be allocated. The cloud environment offered distinct types of virtual machines and cloud provider distribute those services. This is necessary to adjust the allocation of services with the demand of user. This paper presents an adaptive resource allocation mechanism for efficient parallel processing based on cloud. Using this mechanism the provider's job becomes easier and having the least chance for the wastage of resources and time.

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17

Qi, Ning, Xiao Jun Zhang, Bin Qiang Wang, and Jia Guo. "A Self-Adaptive Grid Resource Selection Algorithm." Key Engineering Materials 474-476 (April 2011): 1955–60. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1955.

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In order to provide high Quality of Service (QoS), rational scheduling and resource allocation are needed when a great deal of tasks requesting grid resources. By quantifying some important properties of Grid QoS and maximizing cost-performance ratio of Grid system, a QoS-guaranteed self-adaptive grid resource selection algorithm based on SAGA which is named QoS-SGRSA is proposed, and the flow of the algorithm is introduced. In the low load condition, traditional resource selection algorithm is adopted, so an allocation policy is found rapidly. While in the high load condition, by means of usi

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18

Yang, Ying. "Adaptive Resource Allocation for the Multi-User Multi-Carrier Networks." Advanced Materials Research 663 (February 2013): 722–25. http://dx.doi.org/10.4028/www.scientific.net/amr.663.722.

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In this paper, we investigate the problem of allocating transmission data rates to users in multi-user multi-carrier networks. The paper provides a general problem of power and rate allocation that the utility functions can be nonconcave and nondifferetiable. This constrained optimization problem turns utility maximization into nonconvex, which is well-known to be difficult. To solve this problem, the dual optimization is analyzed to decompose the original optimization problem, and then a simple bisection algorithm is proposed.

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19

Ibrahim, Mohammed, and Haider AlSabbagh. "Adaptive OFDMA Resource Allocation using Modified Multi-Dimension Genetic Algorithm." Iraqi Journal for Electrical and Electronic Engineering 12, no. 1 (2016): 103–13. http://dx.doi.org/10.37917/ijeee.12.1.11.

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A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal soluti

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20

K.S., Tanuja, Shanmukaswamy C.V., Gurushankar H.B., and Dinesh H.A. "DYNAMIC BANDWIDTH ALLOCATION SCHEME FOR ENHANCED PERFORMANCE IN 5G POINT-TO-POINT NETWORKS." ICTACT Journal on Communication Technology 14, no. 2 (2023): 2945–51. http://dx.doi.org/10.21917/ijct.2023.0438.

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This paper proposes a novel dynamic bandwidth allocation scheme for enhancing performance in 5G point-to-point networks. The scheme aims to optimize bandwidth utilization by dynamically allocating resources based on traffic demands and quality of service (QoS) requirements. Through continuous traffic monitoring, QoS analysis, and adaptive allocation algorithms, the scheme ensures optimal resource allocation in real-time. Additionally, load balancing techniques and a feedback mechanism further improve performance by distributing traffic evenly and incorporating user feedback. The proposed schem

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21

Sadr, Sanam, Alagan Anpalagan, and Kaamran Raahemifar. "Suboptimal Rate Adaptive Resource Allocation for Downlink OFDMA Systems." International Journal of Vehicular Technology 2009 (August 18, 2009): 1–10. http://dx.doi.org/10.1155/2009/891367.

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This paper aims to study the performance of low complexity adaptive resource allocation in the downlink of OFDMA systems with fixed or variable rate requirements (with fairness consideration). Two suboptimal resource allocation algorithms are proposed using the simplifying assumption of transmit power over the entire bandwidth. The objective of the first algorithm is to maximize the total throughput while maintaining rate proportionality among the users. The proposed suboptimal algorithm prioritizes the user with the highest sensitivity to the subcarrier allocation, and the variance over the s

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22

Su, Qiong, and Shi Hua He. "Application of Complex Adaptive System Theory to Water Resources Allocation in Dianchi Basin." Applied Mechanics and Materials 212-213 (October 2012): 536–42. http://dx.doi.org/10.4028/www.scientific.net/amm.212-213.536.

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Based on complex adaptive system theory, the characteristics of water resources allocation system of river basin are analyzed. Evolutionary mechanisms and process of complex adaptive water resources allocation system in Dianchi basin are researched, and also characteristics of "learning". A complex adaptive system model of water-resource allocation is established during analyzing the influence factors and the reaction rules of water consumer agents and water provider agents. And based on this model, water resources in Dianchi basin is allocated only under Dianchi water provider and Zhangjiu ri

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23

Sathiyamoorthi V., Keerthika P., Suresh P., Zuopeng (Justin) Zhang, Adiraju Prasanth Rao, and Logeswaran K. "Adaptive Fault Tolerant Resource Allocation Scheme for Cloud Computing Environments." Journal of Organizational and End User Computing 33, no. 5 (2021): 135–52. http://dx.doi.org/10.4018/joeuc.20210901.oa7.

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Cloud computing is an optimistic technology that leverages the computing resources to offer globally better and more efficient services than the collection of individual use of internet resources. Due to the heterogeneous and high dynamic nature of resources, failure during resource allocation is a key risk in cloud. Such resource failures lead to delay in tasks execution and have adverse impacts in achieving quality of service (QoS). This paper proposes an effective and adaptive fault tolerant scheduling approach in an effort to facilitate error free task scheduling. The proposed method consi

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24

L.Ujjwal, R., C. S. Rai, and Nupur Prakash. "Adaptive Resource Allocation in OFDMA System: A Review." International Journal of Computer Applications 114, no. 16 (2015): 21–24. http://dx.doi.org/10.5120/20063-2122.

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25

Du, Lei, Ru Huo, Chuang Sun, Shuo Wang, Jie Guo, and Tao Huang. "Adaptive Resource Allocation of Vehicles under Dynamic Environment." Wireless Communications and Mobile Computing 2022 (May 4, 2022): 1–13. http://dx.doi.org/10.1155/2022/8255733.

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With the rapid development of the Internet of Vehicles (IoV), the capabilities and intelligence of vehicles are rapidly increasing, which will have the potential to support a large number of services for the users of vehicles. However, taking into account the mobility of the vehicle, how to combine the computing resources from nearby vehicles with high mobility and RSUs to provide services collaboratively is a crucial problem. In this paper, we propose an adaptive allocation scheme of computing resources to allocate the resources more reasonably. First, the vehicular mobility model is built to

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26

Soursouri, Masoud, and Mahmood Ahmadi. "Adaptive resource allocation for software defined networking controllers." Journal of High Speed Networks 23, no. 3 (2017): 237–53. http://dx.doi.org/10.3233/jhs-170569.

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27

Christidis, Konstantinos, and Michael Devetsikiotis. "Adaptive multi-tiered resource allocation policy for microgrids." AIMS Energy 4, no. 2 (2016): 300–312. http://dx.doi.org/10.3934/energy.2016.2.300.

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28

Liu, Zhanjun, Yue Shen, Zhonghua Yu, Fengxie Qin, and Qianbin Chen. "Adaptive Resource Allocation Algorithm in Wireless Access Network." TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, no. 3 (2016): 887. http://dx.doi.org/10.12928/telkomnika.v14i3.3615.

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29

Liang, Hongbin, Tianyi Xing, Lin X. Cai, Dijiang Huang, Daiyuan Peng, and Yan Liu. "Adaptive Computing Resource Allocation for Mobile Cloud Computing." International Journal of Distributed Sensor Networks 9, no. 4 (2013): 181426. http://dx.doi.org/10.1155/2013/181426.

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30

LEE, ALAN, and ILZE ZIEDINS. "ADAPTIVE RESOURCE ALLOCATION TO MAXIMIZE RUN-OUT TIMES." Asia-Pacific Journal of Operational Research 29, no. 03 (2012): 1240017. http://dx.doi.org/10.1142/s0217595912400179.

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We introduce a method for maximizing the run-out time for a system where the number of components available to make repairs is finite, and some of the components may be substituted for one another. The objective is to maximize the time at which the earliest run-out of any component occurs. The approach proposed here is to find the minimum time horizon such that no feasible allocation exists for a related linear programming problem. An adaptive version of this algorithm is proposed as a heuristic for the stochastic problem.

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31

Harada, Fumiko, Toshimitsu Ushio, and Yukikazu Nakamoto. "Adaptive Resource Allocation Control for Fair QoS Management." IEEE Transactions on Computers 56, no. 3 (2007): 344–57. http://dx.doi.org/10.1109/tc.2007.39.

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32

Guo, Jin, Biqiang Mu, Le Yi Wang, George Yin, and Lijian Xu. "Decision-Based System Identification and Adaptive Resource Allocation." IEEE Transactions on Automatic Control 62, no. 5 (2017): 2166–79. http://dx.doi.org/10.1109/tac.2016.2612483.

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33

Fan, Jiancun, Qinye Yin, Geoffrey Ye Li, Bingguang Peng, and Xiaolong Zhu. "Adaptive Block-Level Resource Allocation in OFDMA Networks." IEEE Transactions on Wireless Communications 10, no. 11 (2011): 3966–72. http://dx.doi.org/10.1109/twc.2011.092011.110624.

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34

El Baamrani, Khalid, Abdellah Ait Ouahman, and Said Allaki. "Rate adaptive resource allocation for OFDM downlink transmission." AEU - International Journal of Electronics and Communications 61, no. 1 (2007): 30–34. http://dx.doi.org/10.1016/j.aeue.2006.02.002.

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35

Yonghong, S., P. Saratchandran, and N. Sundararajan. "Minimal resource allocation network for adaptive noise cancellation." Electronics Letters 35, no. 9 (1999): 726. http://dx.doi.org/10.1049/el:19990484.

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36

Xenakis, Dionysis, Dimitris Tsolkas, Nikos Passas, Nancy Alonistioti, and Lazaros Merakos. "Dynamic resource allocation in adaptive wireless OFDMA systems." Wireless Communications and Mobile Computing 12, no. 11 (2010): 985–98. http://dx.doi.org/10.1002/wcm.1030.

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37

Shan, Chenggang, Chuge Wu, Yuanqing Xia, Zehua Guo, Danyang Liu, and Jinhui Zhang. "Adaptive resource allocation for workflow containerization on Kubernetes." Journal of Systems Engineering and Electronics 34, no. 3 (2023): 723–43. http://dx.doi.org/10.23919/jsee.2023.000073.

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38

Gang, Jun, Jiuping Xu, and Yinfeng Xu. "Multiproject Resources Allocation Model under Fuzzy Random Environment and Its Application to Industrial Equipment Installation Engineering." Journal of Applied Mathematics 2013 (2013): 1–19. http://dx.doi.org/10.1155/2013/818731.

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This paper focuses on a multiproject resource allocation problem in a bilevel organization. To solve this problem, a bilevel multiproject resource allocation model under a fuzzy random environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the company manager aims to allocate the company's resources to multiple projects to achieve the lowest cost, which include resource costs and a tardiness penalty. On the lower level, each project manager attempts to schedule their resource-constrained project, with minimization of project duration as the main

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39

T. G., Shivapanchakshari, and H. S. Aravinda. "CL-SA-OFDM: Cross-layer and smart antenna based ofdm system performance enhancement." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4663. http://dx.doi.org/10.11591/ijece.v10i5.pp4663-4670.

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The growing usage of wireless services is lacking in providing high-speed data communication in recent times. Hence, many of the modulation techniques are evolved to attain these communication needs. The recent researches have widely considered OFDM technology as the prominent modulation mechanism to fulfill the futuristic needs of wireless communication. The OFDM can bring effective usage of resources, bandwidth, and system performance enhancement in collaboration with the smart antenna and resource allocation mechanism (adaptive). However, the usage of adaptive beamforming with the OFDM lead

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40

Shivapanchakshari, T. G., and S. Aravinda H. "CL-SA-OFDM: cross-layer and smart antenna based OFDM system performance enhancement." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4663–70. https://doi.org/10.11591/ijece.v10i5.pp4663-4670.

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The growing usage of wireless services is lacking in providing high-speed data communication in recent times. Hence, many of the modulation techniques are evolved to attain these communication needs. The recent researches have widely considered OFDM technology as the prominent modulation mechanism to fulfill the futuristic needs of wireless communication. The OFDM can bring effective usage of resources, bandwidth, and system performance enhancement in collaboration with the smart antenna and resource allocation mechanism (adaptive). However, the usage of adaptive beamforming with the OFDM lead

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41

Das, Deepa, Rajendra Kumar Khadanga, and Deepak Kumar Rout. "Adaptive resource allocation in NOMA-enabled backscatter communications systems." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 1 (2024): 67. http://dx.doi.org/10.11591/ijict.v13i1.pp67-79.

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The integration of NOMA with Backscatter communication (BackCom) is a promising solution for developing a green future wireless network. However, system performance degrades with the deployment of multiple backscatter devices (BDs) in a network. Hence, energy efficiency (EE) maximization with proper resource allocation is among the primary concerns. In this regard, this paper proposes an adaptive resource allocation method for maximizing EE by simultaneously optimizing the transmission power from the base station (BS), power allocation coefficients, and reflection coefficients under the constr

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42

Deepa, Das, Kumar Khadanga Rajendra, and Kumar Rout Deepak. "Adaptive resource allocation in NOMA-enabled backscatter communications systems." International Journal of Informatics and Communication Technology 13, no. 1 (2024): 67–79. https://doi.org/10.11591/ijict.v13i1.pp67-79.

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The integration of NOMA with Backscatter communication (BackCom) is a promising solution for developing a green future wireless network. However, system performance degrades with the deployment of multiple backscatter devices (BDs) in a network. Hence, energy efficiency (EE) maximization with proper resource allocation is among the primary concerns. In this regard, this paper proposes an adaptive resource allocation method for maximizing EE by simultaneously optimizing the transmission power from the base station (BS), power allocation coefficients, and reflection coefficients under the constr

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43

Zhu, Li Li, and Yi Feng Duan. "Research on the Resource Allocation Model for the Satellite Constellation Communication System." Advanced Materials Research 121-122 (June 2010): 669–77. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.669.

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Satellite constellation, emerging as a new paradigm for next-generation communicating, enables large-scale application of the geographically and spatially distributed heterogeneous resources for solving problems in science, engineering, and military affairs. The resource allocation in such a large-scale distributed environment is a complex task. Due to the factors that trigger the deployment of resources in satellite constellation communication system, the artificial immune theory is applied to resource allocation field to propose the task-oriented common mathematic model about resource alloca

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44

Sheetal Singh. "Adaptive Resource Management Framework for Secure and Resilient IoT Communication Using Federated Learning and Quantum Encryption." Journal of Information Systems Engineering and Management 10, no. 21s (2025): 693–702. https://doi.org/10.52783/jisem.v10i21s.3405.

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With the growing Internet of Things (IoT) environment, it is a major challenge to provide security and efficient resource allocation, especially as more sensitive information and devices are connected. In this research, we present a new Dynamic Resource Allocation Framework, by merging Federated Learning (FL) and Quantum Cryptography (QC) to optimize the allocation of resources while improving security on the IOT devices. The framework utilizes Federated Learning (FL) for training models at the edge and avoids the transfer of data from edge devices to central servers, thereby ensuring data pri

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45

Chen, Yijun, Qun Zhang, Ying Luo, and Tat Soon Yeo. "Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect." Journal of Sensors 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/5301253.

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The micromotion feature extraction method based on track-before-detect (TBD) can save the radar resource and improve the real-time performance of micromotion feature extraction by implementing target detecting, tracking, and micromotion feature extraction simultaneously. Usually, multitargets will exist in different areas, and the limited radar resources should be allocated for different areas to achieve the maximal performance of radar. For single-beam phased array radar, an adaptive resource allocation optimization model is established according to the processing steps of the micromotion fea

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46

Xu, Xiao Rong, Ai Ping Huang, Jian Rong Bao, and Hang Guan Shan. "Aadptive Subcarrier Allocation for Multiple Cognitive Users over Fading Channels." Advanced Materials Research 765-767 (September 2013): 647–52. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.647.

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In Cognitive Radio Network (CRN), where Primary User (PU) and multiple Secondary Users (SUs) wish to communicate with their corresponding receivers simultaneously over fading channels, spectrum utilization and efficient resource allocation are both significant points for CRN. Interference between PU and SUs should be eliminated in order to realize spectrum sharing. Multi-user resource allocation with the target of maximizing the spectral efficiency of SUs and satisfying the proportional rate constraint between SUs are proposed under the conditions of total SU interference constraint. An adapti

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47

AlSabbagh, Haider, and Mohammed Ibrahim. "Enhanced Bundle-based Particle Collision Algorithm for Adaptive Resource Optimization Allocation in OFDMA Systems." Iraqi Journal for Electrical and Electronic Engineering 18, no. 2 (2022): 21–32. http://dx.doi.org/10.37917/ijeee.18.2.4.

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The necessity for an efficient algorithm for resource allocation is highly urgent because of the increased demand for utilizing the available spectrum of wireless communication systems. This paper proposes an Enhanced Bundle-based Particle Collision Algorithm (EB-PCA) to get the optimal or near optimal values. It applied to the Orthogonal Frequency Division Multiple Access (OFDMA) to evaluate allocations for the power and subcarrier. The analyses take into consideration the power, subcarrier allocations constrain, channel and noise distributions, as well as the distance between the user’s equi

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48

Qureshi, Basit. "Adaptive Multi-Criteria Selection for Efficient Resource Allocation in Frugal Heterogeneous Hadoop Clusters." Electronics 13, no. 10 (2024): 1836. http://dx.doi.org/10.3390/electronics13101836.

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Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop scheduling is load balancing, as frugal nodes within the cluster can become overwhelmed, resulting in degraded performance and frequent occurrences of out-of-memory errors, ultimately leading to job failures. In this study, we introduce an Adaptive Multi-crite

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49

Kaur, Amardeep, and Amandeep Verma. "Adaptive Access Control Mechanism (AACM) for Enterprise Cloud Computing." Journal of Electrical and Computer Engineering 2023 (July 13, 2023): 1–30. http://dx.doi.org/10.1155/2023/3922393.

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Enterprise cloud computing provides various services to enterprises, but access to these services is controlled by a firewall. The firewall determines the actions and operations a legitimate user can perform on the available resources. Access control policies allow or restrict access to resources, and they also keep a record of attempted access. In the role-based access control model, access to resources is based on a user’s role in the enterprise. As resources are limited, the policy manager has to create policies that optimize resource availability to different roles to improve overall resou

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50

Calmon, Tiago Salviano, Amit Bhaya, Oumar Diene, Jonathan Ferreira Passoni, Vinicius Michel Gottin, and Eduardo Vera Sousa. "Control strategies for adaptive resource allocation in cloud computing." IFAC-PapersOnLine 53, no. 2 (2020): 7865–71. http://dx.doi.org/10.1016/j.ifacol.2020.12.1964.

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