Proposal for a Resource Allocation Model Aimed at Fog Computing

被引:0
|
作者
D'Amato, Andre [1 ]
Dantas, Mario [2 ]
机构
[1] Univ Tecnol Fed Parana UTFPR, Apucarana, Brazil
[2] Univ Fed Juiz de Fora UFJF, Juiz De Fora, Brazil
关键词
Distributed System; Job Management; Resource Allocation; Quality of Experience; Throughput; Quality of Context; Users satisfaction;
D O I
10.1007/978-3-031-57870-0_34
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The emergence of fog computing has presented challenges in effectively allocating resources within this environment. Addressing user satisfaction, many of these challenges can be mitigated through the quality of experience paradigm, which incorporates various contextual parameters. To optimize resource utilization, leveraging the quality of context paradigm can significantly enhance system performance. Consequently, this paper introduces a model aimed at dynamically enhancing individual user experiences while concurrently boosting overall system performance within the fog computing environment through quality of context considerations. Experimental results demonstrate tangible enhancements in runtime job execution and noticeable improvements in the overall system performance upon the implementation of our proposed model.
引用
收藏
页码:385 / 396
页数:12
相关论文
共 50 条
  • [31] Optimization-Oriented Resource Allocation Management for Vehicular Fog Computing
    Lin, Fuhong
    Zhou, Yutong
    Pau, Giovanni
    Collotta, Mario
    IEEE ACCESS, 2018, 6 : 69294 - 69303
  • [32] PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems
    Gao, Xin
    Huang, Xi
    Bian, Simeng
    Shao, Ziyu
    Yang, Yang
    IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (01) : 72 - 87
  • [33] Resource allocation in fog computing: a survey on current state and research challenges
    Nemati, Amir Mohammad
    Mansouri, Najme
    KNOWLEDGE AND INFORMATION SYSTEMS, 2025, 67 (03) : 2091 - 2170
  • [34] Deep Reinforcement Learning for Joint Offloading and Resource Allocation in Fog Computing
    Bai, Wenle
    Qian, Cheng
    PROCEEDINGS OF 2021 IEEE 12TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS), 2021, : 131 - 134
  • [35] Deep Reinforcement Learning Empowered Resource Allocation in Vehicular Fog Computing
    Sun, Lijun
    Liu, Mingzhi
    Guo, Jiachen
    Yu, Xu
    Wang, Shangguang
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (05) : 7066 - 7076
  • [36] Comprehensive Analysis of Resource Allocation and Service Placement in Fog and Cloud Computing
    Gowri, A. S.
    Bala, PShanthi
    Ramdinthara, Immanuel Zion
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (03) : 62 - 79
  • [37] A Location-allocation Model for Fog Computing Infrastructures
    de Queiroz, Thiago Alves
    Canali, Claudia
    Iori, Manuel
    Lancellotti, Riccardo
    PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE (CLOSER), 2020, : 253 - 260
  • [38] AI and Blockchain Assisted Framework for Offloading and Resource Allocation in Fog Computing
    Aknan, Mohammad
    Singh, Maheshwari Prasad
    Arya, Rajeev
    JOURNAL OF GRID COMPUTING, 2023, 21 (04)
  • [39] PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems
    Gao, Xin
    Huang, Xi
    Bian, Simeng
    Shao, Ziyu
    Yang, Yang
    ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2019,
  • [40] Introduction on cloud, fog and mist computing - Resource allocation and scheduling perspectives
    Karatza, Helen D.
    SIMULATION MODELLING PRACTICE AND THEORY, 2023, 128