Feedback Scheduling for Realtime Task on Xen Virtual Machine

Kim, Byung Ki; Hur, Kyung Woo; Jang, Jae Hyuck; Ko, Young Woong

doi:10.1007/978-3-642-27201-1_32

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The scheduling algorithms that support real time tasks in the VM have to finish before their deadline have been proposed in [13][14][15]. HwanjuKim et al [16] present virtual machine scheduling techniques for transparently bridging the semantic gap between the VMM and VMs in order to improve I/O performance without compromising CPU fairness.…”

Section: Related Workmentioning

confidence: 99%

A Formal Method of CPU Resources Scheduling in the Cloud Computing Environment

Liu¹,

Xu²,

Li³

2015

IJGDC

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Section: Related Workmentioning

confidence: 99%

A Formal Method of CPU Resources Scheduling in the Cloud Computing Environment

Liu¹,

Xu²,

Li³

2015

IJGDC

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“…The authors of [14] proposed a scheduling algorithm based on the task types. To support real-time tasks in VMs, Byung Ki Kim et al [15] proposed a scheduling algorithm that support real time tasks in VM have to finish before their deadline. Y. Hyun-jun et al [16] considered the overhead of privileged domain Dom0 and proposed a technique to make VM responded with expected response time.…”

Section: Related Workmentioning

confidence: 99%

A Workload-aware Resources Scheduling Method for Virtual Machine

Qu¹,

Liu²,

Xu³

2015

IJGDC

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“…These assessments can provide an optimal solution for SMEs, schools, and social groups. This research will setup and implement five heterogeneous virtual machine management systems which are (a) vSphere ESX/ESXi, (b) Hyper-V R2, (c) Proxmox virtual environment, (d) Ubuntu KVM [12], and (e) CentOS Xen [13], where they are shown in Figures 1, 2, 3, 4, and 5, respectively. Moreover, five heterogeneous hypervisors all link to a shared storage Openfiler through LAN, which is a type of IPSAN storage as shown in Figure 6.…”

Section: Structure Of Virtual Machine and Shared Storagementioning

confidence: 99%

Empirical Analysis of Server Consolidation and Desktop Virtualization in Cloud Computing

Chang

Tsai

Chen

2013

Mathematical Problems in Engineering

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Physical server transited to virtual server infrastructure (VSI) and desktop device to virtual desktop infrastructure (VDI) have the crucial problems of server consolidation, virtualization performance, virtual machine density, total cost of ownership (TCO), and return on investments (ROI). Besides, how to appropriately choose hypervisor for the desired server/desktop virtualization is really challenging, because a trade-off between virtualization performance and cost is a hard decision to make in the cloud. This paper introduces five hypervisors to establish the virtual environment and then gives a careful assessment based on C/P ratio that is derived from composite index, consolidation ratio, virtual machine density, TCO, and ROI. As a result, even though ESX server obtains the highest ROI and lowest TCO in server virtualization and Hyper-V R2 gains the best performance of virtual machine management; both of them however cost too much. Instead the best choice is Proxmox Virtual Environment (Proxmox VE) because it not only saves the initial investment a lot to own a virtual server/desktop infrastructure, but also obtains the lowest C/P ratio.

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Feedback Scheduling for Realtime Task on Xen Virtual Machine

Cited by 6 publications

References 7 publications

A Formal Method of CPU Resources Scheduling in the Cloud Computing Environment

A Formal Method of CPU Resources Scheduling in the Cloud Computing Environment

A Workload-aware Resources Scheduling Method for Virtual Machine

Empirical Analysis of Server Consolidation and Desktop Virtualization in Cloud Computing

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