A Novel Container Placement Mechanism Based on Whale Optimization Algorithm for CaaS Clouds

被引:0
|
作者
Alwabel, Abdulelah [1 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Dept Comp Sci, Alkharj 1194, Saudi Arabia
关键词
cloud computing; CaaS; container placement; CP; energy efficiency; ENERGY; CONSOLIDATION; ARCHITECTURE;
D O I
10.3390/electronics12153369
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Advancements in container technology can improve the efficiency of cloud systems by reducing the initiation time of virtual machines (VMs) and improving portability. Therefore, many cloud service providers offer cloud services based on the container as a service (CaaS) model. Container placement (CP) is a mechanism that allocates containers to a pool of VMs by mapping new containers to VMs and simultaneously considering VM placements on physical machines. The CP mechanism can serve several purposes, such as reducing power consumption and optimizing resource availability. This study presents directed container placement (DCP), a novel policy for placing containers in CaaS cloud systems. DCP extends the whale optimization algorithm, an optimization technique aimed at reducing the power consumption in cloud systems with a minimum effect on the overall performance. The proposed mechanism is evaluated against established methods, namely, improved genetic algorithm and discrete whale optimization using two criteria: energy savings and search time. The experiments demonstrate that DCP consumes approximately 78% less power and reduces the search time by approximately 50% in homogeneous clouds. In addition, DCP saves power by approximately 85% and reduces the search time by approximately 30% in heterogeneous clouds.
引用
收藏
页数:19
相关论文
共 50 条
  • [21] A Novel Approach Based on Average Swarm Intelligence to Improve the Whale Optimization Algorithm
    Serkan Dereli
    Arabian Journal for Science and Engineering, 2022, 47 : 1763 - 1776
  • [22] A novel community detection method based on whale optimization algorithm with evolutionary population
    Feng, Yunfei
    Chen, Hongmei
    Li, Tianrui
    Luo, Chuan
    APPLIED INTELLIGENCE, 2020, 50 (08) : 2503 - 2522
  • [23] A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm
    Kumar, C. H. Santhan
    Rao, R. Srinivasa
    INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED, 2016, 5 (03): : 225 - 232
  • [24] A novel community detection method based on whale optimization algorithm with evolutionary population
    Yunfei Feng
    Hongmei Chen
    Tianrui Li
    Chuan Luo
    Applied Intelligence, 2020, 50 : 2503 - 2522
  • [25] A Novel Resource Scheduling Approach in Container Based Clouds
    Xu, Xin
    Yu, Huiqun
    Pei, Xin
    2014 IEEE 17TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE), 2014, : 257 - 264
  • [26] A Novel Fault Diagnosis Method Based on the KELM Optimized by Whale Optimization Algorithm
    Liang, Ruijun
    Chen, Yao
    Zhu, Rupeng
    MACHINES, 2022, 10 (02)
  • [27] A Hybrid Algorithm Based on BPSO and Immune Mechanism for PMU Optimization Placement
    Peng, Chunhua
    Xu, Xuesong
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 7036 - 7040
  • [28] Novel Genetic Algorithm with Dual Chromosome Representation for Resource Allocation in Container-based Clouds
    Tan, Boxiong
    Ma, Hui
    Mei, Yi
    2019 IEEE 12TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (IEEE CLOUD 2019), 2019, : 452 - 456
  • [29] A novel Q-learning algorithm based on improved whale optimization algorithm for path planning
    Li, Ying
    Wang, Hanyu
    Fan, Jiahao
    Geng, Yanyu
    PLOS ONE, 2022, 17 (12):
  • [30] Group-based whale optimization algorithm
    Farinaz Hemasian-Etefagh
    Faramarz Safi-Esfahani
    Soft Computing, 2020, 24 : 3647 - 3673