Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing

Self-adaptive architecture for virtual machines consolidation based on probabilistic model... By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cluster Computing Springer Journals

Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing

Download PDF
23 pages
Read Article

Loading next page...
 
/lp/springer_journal/self-adaptive-architecture-for-virtual-machines-consolidation-based-on-npIOUOX08m
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Processor Architectures; Operating Systems; Computer Communication Networks
ISSN
1386-7857
eISSN
1573-7543
DOI
10.1007/s10586-018-2806-7
Publisher site
See Article on Publisher Site

Abstract

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.

Journal

Cluster Computing – Springer Journals

Published: Jun 6, 2018

References

  • A break in the clouds: towards a cloud definition
    Vaquero, LM; Rodero-Merino, L; Caceres, J; Lindner, M
  • A view of cloud computing
    Armbrust, M; Fox, A; Griffith, R; Joseph, AD; Katz, R; Konwinski, A; Lee, G; Patterson, D; Rabkin, A; Stoica, I
  • Server consolidation techniques in virtualized data centers: a survey
    Varasteh, A; Goudarzi, M
  • The Case for Energy-Proportional Computing
    Barroso, Luiz André; Hölzle, Urs
  • Handbook of Cloud Computing
    Furht, Borko
  • Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers
    Beloglazov, A; Buyya, R
  • Using matrix approximation for high-dimensional discrete optimization problems: server consolidation based on cyclic time-series data
    Setzer, T; Bichler, M
  • Topology-aware virtual machine placement in data centers
    Silva, RA; Fonseca, NL
  • Sla-based virtual machine management for heterogeneous workloads in a cloud datacenter
    Garg, SK; Toosi, AN; Gopalaiyengar, SK; Buyya, R
  • Vm consolidation: a real case based on openstack cloud
    Corradi, A; Fanelli, M; Foschini, L
  • Scale 8 cap: Scaling-aware resource management for consolidated multi-threaded applications
    Hankendi, C; Coskun, AK
  • Energy-oriented partial desktop virtual machine migration
    Bila, N; Wright, EJ; Lara, ED; Joshi, K; Lagar-Cavilla, HA; Park, E; Goel, A; Hiltunen, M; Satyanarayanan, M
  • Queueing Systems, Volume 2: Computer Applications
    Kleinrock, L
  • Performance analysis of cloud computing services for many-tasks scientific computing
    Iosup, A; Ostermann, S; Yigitbasi, MN; Prodan, R; Fahringer, T; Epema, D
  • Expert cloud: a cloud-based framework to share the knowledge and skills of human resources
    Navimipour, NJ; Rahmani, AM; Navin, AH; Hosseinzadeh, M
  • A multi-parameter scheduling method of dynamic workloads for big data calculation in cloud computing
    Hanani, A; Rahmani, AM; Sahafi, A
  • Load-balancing algorithms in cloud computing: a survey
    Ghomi, EJ; Rahmani, AM; Qader, NN
  • Formal process algebraic modeling, verification, and analysis of an abstract fuzzy inference cloud service
    Rezaee, A; Rahmani, AM; Movaghar, A; Teshnehlab, M
  • Identifying fake feedback in cloud trust management systems using feedback evaluation component and bayesian game model
    Siadat, S; Rahmani, AM; Navid, H
  • Highly reliable architecture using the 80/20 rule in cloud computing datacenters
    Mesbahi, MR; Rahmani, AM; Hosseinzadeh, M
  • A multi-objective ant colony system algorithm for virtual machine placement in cloud computing
    Gao, Y; Guan, H; Qi, Z; Hou, Y; Liu, L
  • Taxonomy of distributed denial of service mitigation approaches for cloud computing
    Shameli-Sendi, A; Pourzandi, M; Fekih-Ahmed, M; Cheriet, M
  • Performance-controlled server consolidation for virtualized data centers with multi-tier applications
    Wang, Y; Wang, X
  • Cost-minimizing dynamic migration of content distribution services into hybrid clouds
    Qiu, X; Li, H; Wu, C; Li, Z; Lau, FC
  • Performance analysis of cloud computing centers using m/g/m/m+ r queuing systems
    Khazaei, H; Misic, J; Misic, VB
  • Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing
    Esfandiarpoor, S; Pahlavan, A; Goudarzi, M
  • Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds
    Kumar, MRV; Raghunathan, S
  • Probabilistic consolidation of virtual machines in self-organizing cloud data centers
    Mastroianni, C; Meo, M; Papuzzo, G
  • Soc: satisfaction-oriented virtual machine consolidation in enterprise data centers
    Li, X; Ventresque, A; Murphy, J; Thorburn, J
  • Power efficient server consolidation for cloud data center
    Mazumdar, S; Pranzo, M
  • Heuristics based server consolidation with residual resource defragmentation in cloud data centers
    Rao, KS; Thilagam, PS
  • Distributed consolidation of virtual machines for power efficiency in heterogeneous cloud data centers
    Khani, H; Latifi, A; Yazdani, N; Mohammadi, S
  • Dataabc: a fast abc based energy-efficient live vm consolidation policy with data-intensive energy evaluation model
    Jiang, J; Feng, Y; Zhao, J; Li, K
  • Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior
    Sedaghat, M; Hernández-Rodriguez, F; Elmroth, E
  • Bayesian network-based virtual machines consolidation method
    Li, Z; Yan, C; Yu, X; Yu, N
  • Improving consolidation of virtual machine based on virtual switching overhead estimation
    Li, M; Bi, J; Li, Z
  • Energy-aware framework with markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers
    Rajabzadeh, M; Haghighat, AT
  • Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints
    Beloglazov, A; Buyya, R
  • Predictive control for energy-aware consolidation in cloud datacenters
    Gaggero, M; Caviglione, L
  • M-convex vm consolidation: towards a better vm workload consolidation
    Huang, Z; Tsang, DH
  • Energy efficiency for cloud computing system based on predictive optimization
    Bui, DM; Yoon, Y; Huh, EN; Jun, S; Lee, S
  • Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters
    Deng, W; Liu, F; Jin, H; Liao, X; Liu, H
  • Multi-capacity combinatorial ordering ga in application to cloud resources allocation and efficient virtual machines consolidation
    Hallawi, H; Mehnen, J; He, H
  • Using ant colony system to consolidate vms for green cloud computing
    Farahnakian, F; Ashraf, A; Pahikkala, T; Liljeberg, P; Plosila, J; Porres, I; Tenhunen, H
  • Euro-Par 2011 Parallel Processing
    Ferreto, Tiago; De Rose, César A. F.; Heiss, Hans-Ulrich
  • Introduction to probability models
    Ross, SM
  • A queuing theory model for cloud computing
    Vilaplana, J; Solsona, F; Teixidó, I; Mateo, J; Abella, F; Rius, J
  • An energy-aware heuristic framework for virtual machine consolidation in cloud computing
    Cao, Z; Dong, S

You’re reading a free preview. Subscribe to read the entire article.

Try 2 weeks free now

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

or browse the journals available

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off

Sign up
for free
Start 14 day
Free Trial