A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment

Zhao, Jia; Hu, Liang; Ding, Yan; Xu, Gaochao; Hu, Ming

doi:10.1371/journal.pone.0108275

Cited by 35 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another work is recommended that can monitor the interr elationship between energy consumption, frequency of VM migrations and SLA violation [23]. Now the focus of research has been shifted to live VM migration techniques for energy -aware computing [24].…”

Section: Eneragy Savign Vm Migration Researchmentioning

confidence: 99%

Affinity based VM Migration: Taxonomy and Challenges

Gupta*¹,

Sharma²,

Pathak³

et al. 2019

IJITEE

View full text Add to dashboard Cite

using virtualization many Virtual Machines can run parallel on the same Host. For dynamic resource management, virtual machines can be migrated from residing host to a different. But before starting the migration some questions need to be answered like when to start the virtual machine migration, which VM to migrated and where? The Virtual Machine migration methods on the Virtual cloud environment has already been researched at length, but very few studies have focused on affinity-relations among virtual machines during migration, hence the key objective of this research paper, is to explore the Affinity-aware VM migration in detail and propose Affinity-Aware VM migration algorithms for migration of a group of VMs with affinity to a destination Host with less capacity than required. This paper also provides a brief review of several virtual machine migration techniques

show abstract

Section: Eneragy Savign Vm Migration Researchmentioning

confidence: 99%

Affinity based VM Migration: Taxonomy and Challenges

Gupta*¹,

Sharma²,

Pathak³

et al. 2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…The authors formulate the problem as a multiobjective optimization problem. Zhao et al [56] present a heuristic for live VM migration policy which combines the particle swarm optimization (PSO) with the idea of simulated annealing (SA). The main idea is to improve the accuracy of the global optimum solution provided by the PSO by introducing SA.…”

Section: Energy-efficient Resource Provisionmentioning

confidence: 99%

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

2018

View full text Add to dashboard Cite

Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.

show abstract

“…Considering two scenarios about resource utilization, one scenario is that the utilization of one physical machine is far greater than the utilization of another for a long time, and the virtual machine migration [20] is usually used to balance load for such cases. The number of migrations should be reduced as much as possible because of the high costs.…”

Section: Problem Descriptionmentioning

confidence: 99%

Multi-objective Virtual Machine Placement for Load Balancing

Feng

2017

ITM Web Conf.

View full text Add to dashboard Cite

Abstract. The virtual machine placement is closely related to the efficient and balanced utilization of physical resources. In this paper, the influence of two scenarios about resource utilization on load balancing is analyzed. A multi-objective ant colony optimization algorithm is proposed to solve the virtual machine placement problem, which balances the load among physical machines and the internal load of physical machine simultaneously. The proposed algorithm is compared with two single objective ant colony optimization algorithms, first fit algorithm and greedy algorithm under some instances. The results show that the proposed algorithm can search and find solutions that exhibit good balance among objectives while others cannot. This demonstrates the proposed algorithm can balance the load in the process of mapping virtual machines to physical machines.

show abstract

A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment

Cited by 35 publications

References 16 publications

Affinity based VM Migration: Taxonomy and Challenges

Affinity based VM Migration: Taxonomy and Challenges

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

Multi-objective Virtual Machine Placement for Load Balancing

Contact Info

Product

Resources

About