MiyakoDori: A Memory Reusing Mechanism for Dynamic VM Consolidation

Akiyama, Soramichi; Hirofuchi, Takahiro; Takano, Ryousei; Honiden, Shinichi

doi:10.1109/cloud.2012.56

Cited by 22 publications

(19 citation statements)

References 17 publications

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“…Soramichi Akiyama et.al [2012], [9] proposed a method called "memory reusing" that lessens the measure of exchanged memory of live relocation. At the point when a VM relocates to an alternate have, the memory picture of the VM is kept in the source host.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the proposed approach, the memory consumption has been reduced by taking a further step towards it. The main idea behind this concept is, whenever a VM is ready for migration from one host to another, its current state is stored on source host for future use to reduce the data transfer when it come back from the destination host after processing [9]. Miyakodori provides a new way in VM Migration to `reduce the cost of data transfer, but it increases the memory consumption of the source host.…”

Section: Proposed Schemementioning

confidence: 99%

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Advanced Memory Reusing Mechanism for Virtual Machines in Cloud Computing

Singh

Behal

Taneja

2015

Procedia Computer Science

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Cloud computing is an emerging computing technology for large data centers that maintains computational resources through the internet, rather than on local computers. With the increasing popularity of cloud computing, also increase the demand of cloud resources. In Infrastructure-as-a-Service datacenters, the placements of Virtual Machines (VMs) on physical hosts are dynamically optimized in response to resource utilization of the hosts. VM migration provides the capability to balance the load, system maintenance and fault tolerance etc. However, existing migration techniques, used to migrate virtual machines keeping data images of VMs in host and skipping transfer of unchanged data fields to reduce the amount of transfer data during migration, if number of migrations increases, number of data images stored on host are also increased, this causes memory starvation. In this paper, we propose a technique that reduces the size of data image stored on source host before migration. When a Virtual Machine migrates to another host, the data image for that VM is kept in the source host after removing unwanted data according to the probability factor. When the VM migrates back to the original host later, the kept memory image will be "reused", i.e. data which are identical to the kept data will not be transferred and comparative to existing system the size of memory image is small. To validate this approach, results evaluated using different threshold levels and probability factor of change in data. Proposed system required less memory to store the memory image and allow more VMs to be hosted. Specifically, our proposed work is used to improve resource efficiency throughout by reducing the size of memory image that is stored on source host. Evaluations show that size of memory image reduced 33% (approx) of unnecessary memory consumption.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed Schemementioning

confidence: 99%

Advanced Memory Reusing Mechanism for Virtual Machines in Cloud Computing

Singh

Behal

Taneja

2015

Procedia Computer Science

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“…In the Entropy [4] algorithm the no op migrations are identified and the resources considered are the cpu and memory. In the Miyako [5] algorithm the metrics used are the dirty page bit. Here the resource considered is the memory.…”

Section: Related Workmentioning

confidence: 99%

Residual Resource Defragmentation Based on ECRC (Enhanced Cloud Resource Consolidating)

Priyatharsini¹,

Malarvizhi²

2016

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In cloud computing, server consolidation is the part where very few persons go through the same. By consolidating the unused server space, memory can be reused for another data allocation. The objective of this paper is to improve resource utilization. Residual resource fragmentation refers to the state of the data center where sufficient amount of residual resources are available for any new VM allocation. To achieve this, there are three methods followed here. Active physical servers are identified. Then the maximum utilization of the resources is found out. Finally the resources are allocated and scheduled using the developed algorithm. In this work, we have proposed a new algorithm enhanced cloud consolidating algorithm. This algorithm improves some of the qualities of the cloud consolidating algorithm. Here the allocation technique is based on the cost and the memory.

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“…Other research has taken a different approach, which involves memory reusing mechanisms for dynamic VM consolidation [26] in order to reduce the amount of transferred data during migration. They [26] have claimed that the existing live migration techniques take a long time to migrate since they have to transfer the entire image of the memory from one host to another.…”

mentioning

confidence: 99%

“…They [26] have claimed that the existing live migration techniques take a long time to migrate since they have to transfer the entire image of the memory from one host to another. They have two types of physical hosts; a shared server that handles VMs, which doesn't take on a heavy computation load (close to idle server), and dedicated servers, which take active VMs.…”

mentioning

confidence: 99%

A Review of Performance and Energy Aware Improvement Methods for Future Green Cloud Computing

Al-Mahruqi¹,

Stewart²,

Hainey³

et al. 2016

IJCA

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This version is available at https://strathprints.strath.ac.uk/58630/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. ABSTRACTWith the advent of increased use of computers and computing power, state of the art of cloud computing has become imperative in the present-day global scenario. It has managed to remove the constraints in many organizations in terms of physical internetworking devices and human resources, leaving room for better growth of many organizations. With all these benefits, cloud computing is still facing a number of impediments in terms of energy consumption within data centers and performance degradation to end users. This has led many industries and researchers to find feasible solutions to the current problems.In the context of realizing the problems faced by cloud data centers and end users, this paper presents a summary of the work done, experimentation setup and the need for a greener cloud computing technique/algorithm which satisfies minimum energy consumption, minimum carbon emission and maximum quality of service.

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MiyakoDori: A Memory Reusing Mechanism for Dynamic VM Consolidation

Cited by 22 publications

References 17 publications

Advanced Memory Reusing Mechanism for Virtual Machines in Cloud Computing

Advanced Memory Reusing Mechanism for Virtual Machines in Cloud Computing

Residual Resource Defragmentation Based on ECRC (Enhanced Cloud Resource Consolidating)

A Review of Performance and Energy Aware Improvement Methods for Future Green Cloud Computing

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