FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

被引:1
|
作者
Hamzeh, Hamed [1 ]
Meacham, Sofia [1 ]
Khan, Kashaf [2 ]
Phalp, Keith [1 ]
Stefanidis, Angelos [1 ]
机构
[1] Bournemouth Univ, Fac Sci & Technol, Poole, Dorset, England
[2] British Telecommun PLC, Ipswich, Suffolk, England
关键词
Allocation; Cloud computing; dominant; non-dominant; fairness; resource; MECHANISM;
D O I
10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00091
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The need for effective and fair resource allocation in cloud computing has been identified in the literature and in industrial contexts for a while. Cloud computing seen as a promising technology, offers usage-based payment, scalable and on-demand computing resources. However, during the past decade, the growing complexity of the IT world has resulted in making Quality of Service (QoS) in the cloud a challenging subject and an NP-hard problem. Specifically, the fair allocation of resources in the cloud becomes particularly interesting when many users submit several tasks which require multiple resources. Research in this area has been increasing since 2012 by introducing the Dominant Resource Fairness (DRF) algorithm as an initial attempt to solve the fair resource allocation problem in the cloud. Although DRF meets a sort of desirable fairness properties, it has been proven to be inefficient in certain conditions. Noticeably, DRF and other works in its extension are not intuitively fair after all. Those implementations have been unable to utilize all the resources in the system, leaving the system in an imbalanced situation with respect to each specific system resource. In order to address those issues, we propose in this paper a novel algorithm namely a Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments (FFMRA) which allocates resources in a fully fair way considering both dominant and non-dominant shares. The results from the experiments conducted in CloudSim show that FFMRA provides approximately 100% recourse utilization, and distributing them fairly among the users while meeting desirable fairness features.
引用
收藏
页码:279 / 286
页数:8
相关论文
共 50 条
  • [1] H-FFMRA: A Multi Resource Fully Fair Resources Allocation Algorithm in Heterogeneous Cloud Computing
    Hamzeh, Hamed
    Meacham, Sofia
    Khan, Kashaf
    Stefanidis, Angelos
    Phalp, Keith
    [J]. 2021 IEEE 45TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2021), 2021, : 1243 - 1249
  • [2] Multi-Resource Fair Allocation in Heterogeneous Cloud Computing Systems
    Wang, Wei
    Liang, Ben
    Li, Baochun
    [J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2015, 26 (10) : 2822 - 2835
  • [3] Discrete Interior Search Algorithm for Multi-resource Fair Allocation in Heterogeneous Cloud Computing Systems
    Liu, Xi
    Zhang, Xiaolu
    Li, Weidong
    Zhang, Xuejie
    [J]. INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2016, PT I, 2016, 9771 : 615 - 626
  • [4] Capturing Resource Tradeoffs in Fair Multi-Resource Allocation
    Zarchy, Doron
    Hay, David
    Schapira, Michael
    [J]. 2015 IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (INFOCOM), 2015,
  • [5] Multi-resource Fair Allocation with Bounded Number of Tasks in Cloud Computing Systems
    Li, Weidong
    Liu, Xi
    Zhang, Xiaolu
    Zhang, Xuejie
    [J]. THEORETICAL COMPUTER SCIENCE, NCTCS 2017, 2017, 768 : 3 - 17
  • [6] Towards Multi-task Fair Sharing for Multi-resource Allocation in Cloud Computing
    Zhao, Lihua
    Dui, Minghui
    Lei, Weibao
    Chen, Lin
    Yang, Lei
    [J]. CLOUD COMPUTING AND SECURITY, PT II, 2018, 11064 : 322 - 333
  • [7] MRFS: A Multi-Resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing
    Hamzeh, Hamed
    Meacham, Sofia
    Khan, Kashaf
    Phalp, Keith
    Stefanidis, Angelos
    [J]. 2020 IEEE 44TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2020), 2020, : 1653 - 1660
  • [8] Towards Multi-Resource Fair Allocation with Placement Constraints
    Wang, Wei
    Li, Baochun
    Liang, Ben
    Li, Jun
    [J]. SIGMETRICS/PERFORMANCE 2016: PROCEEDINGS OF THE SIGMETRICS/PERFORMANCE JOINT INTERNATIONAL CONFERENCE ON MEASUREMENT AND MODELING OF COMPUTER SCIENCE, 2016, : 415 - 416
  • [9] Multi-resource fair allocation with bandwidth requirement compression in the cloud-edge system
    Li, Xingxing
    Li, Weidong
    Zhang, Xuejie
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2023, 105
  • [10] Dynamic Multi-Resource Fair Allocation with Elastic Demands
    Guo, Hao
    Li, Weidong
    [J]. JOURNAL OF GRID COMPUTING, 2024, 22 (01)