Designing a Web-Based Graphical Interface for Virtual Machine Management

Donepudi, Harinivesh; Bhavineni, Bindu; Galloway, Michael

doi:10.1007/978-3-319-32467-8_36

Cited by 2 publications

(1 citation statement)

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“…Note that the VMs for users can be manually pre-assigned to each server during offline processing by an administrator, and managed in the standby mode instead of in the shutdown mode when users are receiving the results of the statistical analysis. In addition, the networks are managed by network separation to ensure stable services [18][19][20][21][22] with a management network and a user network. User access and the management of VM states occur through the management network [23].…”

Section: Cloud Computing For a Heterogeneous Platformmentioning

confidence: 99%

Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment

Lee

Jeong

2016

Symmetry

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Abstract:A fundamental key for enterprise users is a cloud-based parameter-driven statistical service and it has become a substantial impact on companies worldwide. In this paper, we demonstrate the statistical analysis for some certain criteria that are related to data and applied to the cloud server for a comparison of results. In addition, we present a statistical analysis and cloud-based resource allocation method for a heterogeneous platform environment by performing a data and information analysis with consideration of the application workload and the server capacity, and subsequently propose a service prediction model using a polynomial regression model. In particular, our aim is to provide stable service in a given large-scale enterprise cloud computing environment. The virtual machines (VMs) for cloud-based services are assigned to each server with a special methodology to satisfy the uniform utilization distribution model. It is also implemented between users and the platform, which is a main idea of our cloud computing system. Based on the experimental results, we confirm that our prediction model can provide sufficient resources for statistical services to large-scale users while satisfying the uniform utilization distribution.

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