A Dynamic Resource Allocation Model for Virtual Machine Management on Cloud

Yang, Chao-Tung; Cheng, Hsiang-Yao; Huang, Kuan-Lung

doi:10.1007/978-3-642-27180-9_70

Cited by 25 publications

(15 citation statements)

References 19 publications

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“…The present work is a continuation of our prior work being carried out [25]. The complete implementation of the proposed system follows analytical modelling approach.…”

Section: The Proposed Solutionmentioning

confidence: 95%

“…Assessment of the server consolidation was carried out by Chang et al [24] where the problem of selection of a precise hypervisor is discussed for specific virtualization of the server. Study on dynamic allocation of resources was also carried out by Yang et al [25] to perform autonomous migration of the jobs among the VMs depending on the amount of load. The result was assessed using time with increasing size of problem and CPU utilization using OpenNebula.…”

Section: A Backgroundmentioning

confidence: 99%

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Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

Ngenzi¹,

Selvarani²,

Suchithra³

2016

ijacsa

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Abstract-Live Migration is one of the essential operations that require more attention to addressing its high variability problems with virtual machines. We review the existing techniques of resource management to find that there are less modeling to solve this problem. The present paper introduces a novel framework that mainly targets to achieve a computational effective resource management technique. The technique uses the stochastic approach in modelling to design a new traffic management scheme that considers multiple traffic possibilities over VMs along with its switching states. Supported by an analytical modelling approach, the proposed technique offers an efficient placement of virtual machine to the physical server, performs the computation of blocks, and explores reduced resource usage. The study outcome was found to possess potential reduction in live migration, more extent of VM mapping with physical servers, and increased level of capacity.

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“…The present work is a continuation of our prior work being carried out [25]. The complete implementation of the proposed system follows analytical modelling approach.…”

Section: The Proposed Solutionmentioning

confidence: 95%

Section: A Backgroundmentioning

confidence: 99%

Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

Ngenzi¹,

Selvarani²,

Suchithra³

2016

ijacsa

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show abstract

“…Bobroff et al [12] propose an algorithm that proactively adapts to demand changes and migrates virtual machines between physical hosts. Yang et al [38] also propose a load-balancing approach in vm scheduling process. Calcavecchia et al [15] develop a process to select candidate host for a vm by analyzing past behaviors of a host and deploy the request, and Gupta et al [19] propose a process for scheduling HPC related vms together.…”

Section: Related Workmentioning

confidence: 98%

Mitigating Multi-Tenancy Risks in IaaS Cloud Through Constraints-Driven Virtual Resource Scheduling

Bijon

Krishnan

Sandhu

2015

Proceedings of the 20th ACM Symposium on Access Control Models and Technologies

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A major concern in the adoption of cloud infrastructure-asa-service (IaaS) arises from multi-tenancy, where multiple tenants share the underlying physical infrastructure operated by a cloud service provider. A tenant could be an enterprise in the context of a public cloud or a department within an enterprise in the context of a private cloud. Enabled by virtualization technology, the service provider is able to minimize cost by providing virtualized hardware resources such as virtual machines, virtual storage and virtual networks, as a service to multiple tenants where, for instance, a tenant's virtual machine may be hosted in the same physical server as that of many other tenants. It is well-known that separation of execution environment provided by the hypervisors that enable virtualization technology has many limitations. In addition to inadvertent misconfigurations, a number of attacks have been demonstrated that allow unauthorized information flow between virtual machines hosted by a hypervisor on a given physical server. In this paper, we present attribute-based constraints specification and enforcement as a mechanism to mitigate such multi-tenancy risks that arise in cloud IaaS. We represent relevant properties of virtual resources (e.g., virtual machines, virtual networks, etc.) as their attributes. Conflicting attribute values are specified by the tenant or by the cloud IaaS system as appropriate. The goal is to schedule virtual resources on physical resources in a conflict-free manner. The general problem is shown to be NP-complete. We explore practical conflict specifications that can be efficiently enforced. We have implemented a prototype for virtual machine scheduling in OpenStack, a widely-used open-source cloud IaaS software, and evaluated its performance overhead, resource requirements to satisfy conflicts, and resource utilization.

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“…Yang et al proposed a dynamic resource allocation system to ensure load balancing of the nodes when multiple VMs are running on it [13]. The proposed system monitors the workload of VMs running on a node, when any one of the VMs load becomes high that will automatically migrate to another physical node without causing service interrupt.…”

Section: Literature Surveymentioning

confidence: 99%

Energy conservation in cloud data centers by minimizing virtual machines migration through artificial neural network

Radhakrishnan

Kavitha

2016

Computing

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Cloud computing is one of the most attractive cost effective technologies for provisioning information technology (IT) resources to common IT consumers. These resources are provided as service through internet in pay per usage manner, which are mainly classified into application, platform and infrastructure. Cloud provides its services through data centers that possess high configuration servers. The conservation of data centers energy give benefits to both cloud providers and consumers in terms of service time and cost. One of the fundamental services of cloud is infrastructure as a service that provides virtual machines (VMs) as a computing resource to consumers. The VMs are created in data center servers as the machine instances, which could work as a dedicated computer system for consumers. As cloud provides the feature of elasticity, the consumers can change their resource demand during service. This characteristics leads VMs migration is unavoidable in cloud environment. The increased down time of VMs in migration affects the efficiency of cloud service. The minimization of VMs migration reduces the processing time that ultimately saves the energy of data centers. The proposed methodology in this work utilizes genetically weight optimized artificial neural network to predict the near future availability of data center servers. Based on the future availability of resources the VMs management activities are performed. The implementation results demonstrated that the proposed methodology significantly reduces the processing time of data centers and the response time of customer applications by minimizing VMs migration. B A. Radhakrishnan radhakrishnan.a@auttvl.ac.in V. Kavitha

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A Dynamic Resource Allocation Model for Virtual Machine Management on Cloud

Cited by 25 publications

References 19 publications

Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

Mitigating Multi-Tenancy Risks in IaaS Cloud Through Constraints-Driven Virtual Resource Scheduling

Energy conservation in cloud data centers by minimizing virtual machines migration through artificial neural network

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