• Journal of Information Processing Systems
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• v.13 no.3
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• pp.476-490
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• 2017

In the centralized cloud controlled environment, the decision-making and monitoring play crucial role where in the host controller (HC) manages the resources across hosts in data center (DC). HC does virtual machine (VM) and physical hosts management. The VM management includes VM creation, monitoring, and migration. If HC down, the services hosted by various hosts in DC can't be accessed outside the DC. Decentralized VM management avoids centralized failure by considering one of the hosts from DC as HC that helps in maintaining DC in running state. Each host in DC has many VM's with the threshold limit beyond which it can't provide service. To maintain threshold, the host's in DC does VM migration across various hosts. The data in migration is in the form of plaintext, the intruder can analyze packet movement and can control hosts traffic. The incorporation of security mechanism on hosts in DC helps protecting data in migration. This paper discusses an approach for dynamic HC selection, VM selection and secure VM migration over cloud environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.6
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• pp.1398-1417
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• 2013

A method is proposed to reduce excess resources from a virtual machine(VM) while avoiding subsequent migrations for a computer cluster that provides cloud service. The proposed scheme cuts down on the resources of a VM based on the probability that migration may occur after a reduction. First, it finds a VM that can be scaled down by analyzing the history of the resource usage. Then, the migration probability is calculated as a function of the VM resource usage trend and the trend error. Finally, the amount of resources needed to eliminate from an underutilized VM is determined such that the migration probability after the resource reduction is less than or equal to an acceptable migration probability. The acceptable migration probability, to be set by the cloud service provider, is a criterion to assign a weight to the resource reduction either to prevent VM migrations or to enhance VM utilization. The results of simulation show that the proposed scheme lowers migration frequency by 31.6~60.8% depending on the consistency of resource demand while losing VM utilization by 9.1~21.5% compared to other known approaches, such as the static and the prediction-based methods. It is also verified that the proposed scheme extends the elapsed time before the first occurrence of migration after resource reduction 1.1~2.3-fold. In addition, changes in migration frequency and VM utilization are analyzed with varying acceptable migration probabilities and the consistency of resource demand patterns. It is expected that the analysis results can help service providers choose a right value of the acceptable migration probability under various environments having different migration costs and operational costs.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.315-320
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• 2015

In cloud computing, server virtualization supports one or more virtual machines loaded on multiple operating systems on a single physical host server. Migration of a VM is moving the VM running on a source host to another physical machine called target host. A VM live migration is essential to support task performance optimization, energy efficiency and energy saving, fault tolerance and load balancing. In this paper, we propose open source based adaptive VM live migration technique. For this, we design VM monitoring module to decide VM live migration and open source based full-virtualization hypervisor.

• KNOM Review
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• v.24 no.1
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• pp.1-12
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• 2021

VM (Virtual Machine) live migration is a server virtualization technique for deploying a running VM to another server node while minimizing downtime of a service the VM provides. Currently, in cloud data centers, VM live migration is widely used to apply load balancing on CPU workload and network traffic, to reduce electricity consumption by consolidating active VMs into specific location groups of servers, and to provide uninterrupted service during the maintenance of hardware and software update on servers. It is critical to use VMlive migration as a prevention or mitigation measure for possible failure when its indications are detected or predicted. In this paper, we propose two VNF live migration methods; one for predictive load balancing and the other for a proactive measure in failure. Both need machine learning models that learn periodic monitoring data of resource usage and logs from servers and VMs/VNFs. We apply the second method to a vEPC (Virtual Evolved Pakcet Core) failure scenario to provide a detailed case study.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.60-66
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• 2024

Host's data during transmission. Data tempering results in loss of host's sensitive information, which includes number of VM, storage availability, and other information. In the distributed cloud environment, each server (computing server (CS)) configured with Local Resource Monitors (LRMs) which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration [21] [22] by each server considering nearby server's CPU usage, roatative decision making capacity [21] among the servers in distributed cloud environment has been proposed. This approaches usage underlying server's computing power for predicting own server's future resource utilization and nearby server's resource usage computation. It results in running VM and its running application to remain in waiting state for computing power. In order to reduce this, a decentralized decision making hybrid model for VM migration need to be proposed where servers in decentralized cloud receives, future resource usage by analytical computing system and takes decision for migrating VM to its neighbor servers. Host's in the decentralized cloud shares, their detail with peer servers after fixed interval, this results in chance to tempering messages that would be exchanged in between HC and CH. At the same time, it reduces chance of over utilization of peer servers, caused due to compromised host. This paper discusses, an roatative decisive (RD) approach for VM migration among peer computing servers (CS) in decentralized cloud environment, preserving confidentiality and integrity of the host's data. Experimental result shows that, the proposed predictive VM migration approach reduces extra VM migration caused due over utilization of identified servers and reduces number of active servers in greater extent, and ensures confidentiality and integrity of peer host's data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1501-1518
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• 2016

This paper presents an effective management of VM (Virtual Machine) for heterogeneous cloud using Common Deployment Model (CDM) brokering mechanism. The effective utilization of VM is achieved by means of task scheduling with VM placement technique. The placements of VM for the physical machine are analyzed with respect to execution time of the task. The idle time of the VMis utilized productively in order to improve the performance. The VMs are also scheduled to maintain the state of the current VM after the task completion. CDM based algorithm maintains two directories namely Active Directory (AD) and Passive Directory (PD). These directories maintain VM with proper configuration mapping of the physical machines to perform two operations namely VM migration and VM roll back. VM migration operation is performed from AD to PD whereas VM roll back operation is performed from PD to AD. The main objectives of the proposed algorithm is to manage the VM's idle time effectively and to maximize the utilization of resources at the data center. The VM placement and VM scheduling algorithms are analyzed in various dimensions of the cloud and the results are compared with iCanCloud model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• KIISE Transactions on Computing Practices
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• v.22 no.6
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• pp.278-283
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• 2016

Due to explosive increase in the amount of data produced recently, cloud storage system is required to offer high and stable performance. However, VM (Virtual Machine) migration may result in lowered storage service performance. Especially, in an environment where the host-side flash cache is used in a cloud system, the existing warmed up cache is lost and the problematic cold start begins at a new cache due to a VM migration. In this paper, we first demonstrate and analyze the cold start problem and then propose Cachemior (Cache migrator) which enables efficient hot start of the flash cache.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.1-7
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• 2014

In this paper, we propose a virtual machine(VM) remapping scheme that reduces VM relocation time on a cloud cluster. The proposed scheme finds VMs that should be migrated in sequence from a given VM map, and exchanges destinations of some VMs among them to reduce the VM relocation time. The VMs, the destinations of which will be exchanged, are chosen based on the amount of physical machine's available resources and migration completion time. The exchange of destinations is repeated until the VM relocation time cannot be shortened any further. Through a simulation, we show that the proposed scheme reduces VM relocation time by 42.7% in maximum.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.215-218
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• 2012

스팟 인스턴스(Spot instance)는 클라우드 환경에서 사용자가 제시한 입찰 가격으로 클라우드 내의 자원을 이용하여 작업을 수행할 수 있게 하는 새로운 방식이다. 사용자는 자신의 입찰 금액이 클라우드 내의 스팟 인스턴스 가격을 초과하는 한 인스턴스를 실행할 수 있다. 그러나 입찰 가격이 스팟 가격보다 낮다면 작업 실패가 발생하고, 이로 인해 작업 완료 시간은 지연되며 서비스 품질은 저하된다. 이 문제를 해결하기 위해, 본 논문에서는 스팟 인스턴스에서 사용자 비드가 초과되어 작업 수행이 중지된 VM에 대하여 체크포인트 기법과 VM 이주(migration) 기법을 이용함으로써 작업 대기 시간을 줄이는 방법을 제안한다. 이는 작업 중인 스팟 인스턴스에서 작업 실패가 발생할 경우 다른 인스턴스로 이주하여 작업을 재수행하는 기법이다. 실험 결과는 제안하는 VM 이주 기법이 작업을 수행할 수 있는 스팟 인스턴스의 가용성을 증가시킬 수 있음을 보여준다.