CosMig: Modeling the Impact of Reconfiguration in a Cloud

Verma, Akshat; Kumar, G. Santhosh; Koller, Ricardo; Sen, Aritra

doi:10.1109/mascots.2011.37

Cited by 27 publications

(26 citation statements)

References 11 publications

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“…A possible model for predicting the duration and overhead of live migration was presented by Verma et al [102,103]. According to their findings, migration increases the load of the source PM, but not the load of the target PM.…”

Section: Live Migrationmentioning

confidence: 99%

“…There are simulators specifically for cloud research, for example CloudSim [21], but many researchers used their own simulation environments. Relatively few researchers tested their algorithms in a real environment [31,51,57,66,70,74,101,103,105] or using a combination of real hardware and simulation [85,93,95,104,109]. It has to be added though that in most of these cases, the "real" environment used for evaluation was rather small (e.g., just a handful of PMs and VMs).…”

Section: Empirical Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

2015

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Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future.

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Section: Live Migrationmentioning

confidence: 99%

Section: Empirical Evaluationmentioning

confidence: 99%

Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

2015

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“…However, energy consumption of VM migration is not taken into account in these works. Other works like [10], [11], [12] focused on the cost of live migration for Cloud data centres, but considered only performance and did not take energy consumption into account. Further works like [13] implemented a model for VM migration in a Cloud simulator, but do not provide models for its energy consumption.…”

Section: Related Workmentioning

confidence: 99%

A Workload-Aware Energy Model for Virtual Machine Migration

Maio¹,

Kecskeméti²,

Prodan³

2015

2015 IEEE International Conference on Cluster Computing

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Abstract-Energy consumption has become a significant issue for data centres. Assessing their consumption requires precise and detailed models. In the latter years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine migration or do not consider the variation of the workload on (1) the virtual machines (VM) and (2) the physical machines hosting the VMs. In this paper, we show that omitting migration and workload variation from the models could lead to misleading consumption estimates. Then, we propose a new model for data centre energy consumption that takes into account the previously omitted model parameters and provides accurate energy consumption predictions for paravirtualised virtual machines running on homogeneous hosts. The new model's accuracy is evaluated with a comprehensive set of operational scenarios. With the use of these scenarios we present a comparative analysis of our model with similar state-of-the-art models for energy consumption of VM Migration, showing an improvement up to 24% in accuracy of prediction.

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“…On the other hand, others ( [2], [4], [15], [16] ) have studied the cost of VM migration and reconfiguration with SDN. VM migrations can deteriorate network performance [2], while modification to the data forwarding rules yield traffic congestions at the controller in the control plane [4].…”

Section: B Cloud Resource Managementmentioning

confidence: 99%

RSU cloud and its resource management in support of enhanced vehicular applications

Salahuddin

Al-Fuqaha

Guizani

et al. 2014

2014 IEEE Globecom Workshops (GC Wkshps)

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Abstract-We propose Roadside Unit (RSU) Clouds as a novel way to offer non-safety application with QoS for VANETs. The architecture of RSU Clouds is delineated, and consists of traditional RSUs and specialized micro-datacenters and virtual machines (VMs) using Software Defined Networking (SDN). SDN offers the flexibility to migrate or replicate virtual services and reconfigure the data forwarding rules dynamically. However, frequent changes to service hosts and data flows not only result in degradation of services, but are also costly for service providers.In this paper, we use Mininet to analyze and formally quantify the reconfiguration overhead. Our unique RSU Cloud Resource Management (CRM) model jointly minimizes reconfiguration overhead, cost of service deployment and infrastructure routing delay. To the best of our knowledge, we are the first to utilize this approach. We compare the performance of purist approach to our Integer Linear Programming (ILP) model and our innovative heuristic for the CRM technique and discuss the results. We will show the benefits of a holistic approach in Cloud Resource Management with SDN.

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CosMig: Modeling the Impact of Reconfiguration in a Cloud

Cited by 27 publications

References 11 publications

Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

A Workload-Aware Energy Model for Virtual Machine Migration

RSU cloud and its resource management in support of enhanced vehicular applications

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