A Generic Arrival Process Model for Generating Hybrid Cloud Workload

被引:2
|
作者
An, Chunyan [1 ]
Zhou, Jian-tao [1 ]
Mou, Zefeng [1 ]
机构
[1] Inner Mongolia Univ, Hohhot 010021, Peoples R China
来源
COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, CHINESECSCW 2018 | 2019年 / 917卷
关键词
Cloud computing; Cloud workload generation; Generic arrival process;
D O I
10.1007/978-981-13-3044-5_8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In cloud computing, the arrival process of user requests is becoming more diversiform with the globalization of users and the popularization of mobile technology. Moreover, the workloads in cloud computing are tending towards a hybrid of more applications types. It is hardly for the traditional arrival process models to cover the ever-increasing new arrival processes in reality. For that, we propose a general and flexible arrival process model to describe various arrival processes. At the same time, we present a unified generation algorithm to generate the corresponding workload arrival instance based on the arrival process model automatically. The model defines the arrival process by four steps: firstly defines the number of intervals during the workload lifetime, then defines the length of each time interval, next defines the number of requests arriving during each time interval, lastly defines the arrival time points during each time interval. In the case study, we use the generic arrival process model to describe three arrival process models of typical cloud application types and a custom arrival process model, and present corresponding arrival instances using the generation algorithm. The cases showed the flexibility and extensibility of the model. The model and algorithm are simple and generic and are more approaching to realistic hybrid arrival processes.
引用
收藏
页码:100 / 114
页数:15
相关论文
共 50 条
  • [1] A generic cloud migration process model
    Fahmideh, Mahdi
    Daneshgar, Farhad
    Rabhi, Fethi
    Beydoun, Ghassan
    EUROPEAN JOURNAL OF INFORMATION SYSTEMS, 2019, 28 (03) : 233 - 255
  • [2] Intelligent Workload Factoring for A Hybrid Cloud Computing Model
    Zhang, Hui
    Jiang, Guofei
    Yoshihira, Kenji
    Chen, Haifeng
    Saxena, Akhilesh
    2009 IEEE CONGRESS ON SERVICES (SERVICES-1 2009), VOLS 1 AND 2, 2009, : 701 - 708
  • [3] Proactive Workload Management in Hybrid Cloud Computing
    Zhang, Hui
    Jiang, Guofei
    Yoshihira, Kenji
    Chen, Haifeng
    IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2014, 11 (01): : 90 - 100
  • [4] Generic SDE and GA-based workload modeling for cloud systems
    Cédric St-Onge
    Souhila Benmakrelouf
    Nadjia Kara
    Hanine Tout
    Claes Edstrom
    Rafi Rabipour
    Journal of Cloud Computing, 10
  • [5] Generic SDE and GA-based workload modeling for cloud systems
    St-Onge, Cedric
    Benmakrelouf, Souhila
    Kara, Nadjia
    Tout, Hanine
    Edstrom, Claes
    Rabipour, Rafi
    JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2021, 10 (01):
  • [6] A Self-Optimized Generic Workload Prediction Framework for Cloud Computing
    Jayakumar, Vinodh Kumaran
    Lee, Jaewoo
    Kim, In Kee
    Wang, Wei
    2020 IEEE 34TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM IPDPS 2020, 2020, : 779 - 788
  • [7] Hybrid Resource Scaling for Dynamic Workload in Cloud Computing
    Daraje, Megersa
    Shaikh, Javed
    2021 IEEE INTERNATIONAL CONFERENCE ON MOBILE NETWORKS AND WIRELESS COMMUNICATIONS (ICMNWC), 2021,
  • [8] Time series-based workload prediction using the statistical hybrid model for the cloud environment
    Devi, K. Lalitha
    Valli, S.
    COMPUTING, 2023, 105 (02) : 353 - 374
  • [9] Time series-based workload prediction using the statistical hybrid model for the cloud environment
    K. Lalitha Devi
    S. Valli
    Computing, 2023, 105 : 353 - 374
  • [10] PredictOptiCloud: A hybrid framework for predictive optimization in hybrid workload cloud task scheduling
    Sugan, J.
    Sajan, Isaac R.
    SIMULATION MODELLING PRACTICE AND THEORY, 2024, 134