Efficient performance upsurge in live migration with downturn in the migration time and downtime

Kumar, Amit; Krishnakumar, V.; Kumar, A. Nirmal

doi:10.1007/s10586-018-1753-7

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…Live VM migration [11]- [13] is one of the leading technologies for managing data centers, where VMs continue to run during the migration process. In other words, it allows transferring a VM from one PM to another without disrupting the services.…”

Section: Introductionmentioning

confidence: 99%

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

et al. 2020

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With the widespread usage of cloud computing to benefit from its services, cloud service providers have invested in constructing large scale data centers. Consequently, a tremendous increase in energy consumption has arisen in conjunction with its results, including a remarkable rise in costs of operating and cooling servers. Besides, increasing energy consumption has a significant impact on the environment due to emissions of carbon dioxide. Dynamic consolidation of Virtual Machines (VMs) into the minimal number of Physical Machines (PMs) is considered as one of the magic solutions to manage power consumption. The virtual machine placement problem is a critical issue for good VM consolidation. This paper proposes a Power-Aware technique depending on Particle Swarm Optimization (PAPSO) to determine the near-optimal placement for the migrated VMs. A discrete version of Particle Swarm Optimization (PSO) is adopted based on a decimal encoding to map the migrated VMs to the best appropriate PMs. Furthermore, an effective minimization fitness function is employed to reduce power consumption without violating the Service Level Agreement (SLA). Specifically, PAPSO consolidates the migrated VMs into the minimum number of PMs with a major constraint to decrease the number of overloaded hosts as much as possible. Therefore, the number of VM migrations can be reduced drastically by taking into consideration the main sources for VM migrations; overloaded hosts and underloaded ones. PAPSO is implemented in CloudSim and the experimental results on random workloads with different sizes of VMs and PMs show that PAPSO does not violate SLA and outperforms the Power-Aware Best Fit Decreasing algorithm (PABFD). It can reduce about 8.01%, 39.65%, 66.33%, and 11.87% on average in terms of consumed energy, number of VM migrations, number of host shutdowns and the combined metric Energy SLA Violation (ESV), respectively.

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Section: Introductionmentioning

confidence: 99%

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

et al. 2020

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“…It may cause an increase in downtime. So the VM page dirty rate and the VM's working set (it is a collection of recently referenced segments or memory pages) size [7,10,12,12,[64][65][66][67][68][69][70][71] are relevant parameters for the pre-copy migration.…”

Section: Algorithm 1 Feature Selectionmentioning

confidence: 99%

A Machine Learning-Based Optimization Approach for Pre-Copy Live Virtual Machine Migration

Haris¹,

Khan²,

Nhlabatsi³

2022

SSRN Journal

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“…When processes have an access request to a resource or resources in the local computing system that cannot meet the constraints required by the process to continue the process execution, the load balancer will redistribute the load and will call the process migration through migration mechanisms [24,25]. In the third pattern, the process migration transmits a process to another computing node to meet the process requirements [26]. Each element of resource discovery and load balancer, based on information gathering, resource management and computing elements, are designed to operate at system level, despite the need to process `and to create a stable state, imposes additional load to the computing system.…”

Section: Related Workmentioning

confidence: 99%

ExaMig Matrix: Process Migration based on Matrix Definition of Selecting Destination in Distributed Exascale Environments

Khaneghah

ShowkatAbad

Shadnoush³

et al. 2018

AZJHPC

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In traditional computing system, load balancer, interim selecting the process, determine the destination computing node based on describing Indicators process status. In distributed Exascale computing system, due to the possibility of occurrence of a dynamic and interactive nature in execution time, it is possible. That the chosen destination computing node affected with dynamic and interactive nature so cannot be considered as a destination in process migration. This paper, by changing management approach in process migration. Consider process as an abstract element on the target computing node and calculates the impact of the factors the parameters affecting the process. Considering the above factors make process migration manager able to create sets of computational node that can be considered as destination computing node. In the event of a dynamic and interactive nature, in each element of the set, the process migration management, consider the effects of the factors affecting the activity of the process management and then re-weighs the computing element which make the above set. Using this mechanism allow the process migration management in case of dynamic and interactive nature occurrence in destination able to decide about changing on global activity execution so it is not necessary to recall load balancer manager in order to choose destination computing node. These subject louds to decrease execution time of process migration activity in distributed Exascale computing system.

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Efficient performance upsurge in live migration with downturn in the migration time and downtime

Cited by 11 publications

References 15 publications

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

A Machine Learning-Based Optimization Approach for Pre-Copy Live Virtual Machine Migration

ExaMig Matrix: Process Migration based on Matrix Definition of Selecting Destination in Distributed Exascale Environments

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