Virtual Machine Placement Optimization Supporting Performance SLAs

Anand, Ankit; Lakshmi, J.; Nandy, S. K.

doi:10.1109/cloudcom.2013.46

Cited by 51 publications

(28 citation statements)

References 16 publications

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“…The Google cluster data trace [21] The migration impact factor, w net 0.8 [12] The migration impact factor, w men 0.6 [12] The migration impact factor, w disk 0.4 [12] The migration impact factor, w cpu 0.1 [12] Initially, each Avatar is deployed in the available cloudlet, which is the closest to its UE. First, we set up the penalty coefficient α = 5 and run the simulation.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…7, we have Eq. 4 and thus prove Lemma 1. proportional to the weighted sum of utilization of different resources [12]:…”

Section: ) Total Migration Timementioning

confidence: 99%

“…where u be derived through experiments [12]); α is the penalty coefficient that maps the weighted sum of utilization of resources to the cost coefficient. α is an important parameter in the system.…”

Section: ) Cost Coefficientmentioning

confidence: 99%

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PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

Sun

Ansari

2016

2016 IEEE International Conference on Communications (ICC)

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We propose a cloudlet network architecture to bring the computing resources from the centralized cloud to the edge. Thus, each User Equipment (UE) can communicate with its Avatar, a software clone located in a cloudlet, with lower end-to-end (E2E) delay. However, UEs are moving over time, and so the low E2E delay may not be maintained if UEs' Avatars stay in their original cloudlets. Thus, live Avatar migration (i.e., migrating a UE's Avatar to a suitable cloudlet based on the UE's location) is enabled to maintain low E2E delay between each UE and its Avatar. On the other hand, the migration itself incurs extra overheads in terms of resources of the Avatar, which compromise the performance of applications running in the Avatar. By considering the gain (i.e., the E2E delay reduction) and the cost (i.e., the migration overheads) of the live Avatar migration, we propose a PRofIt Maximization Avatar pLacement (PRIMAL) strategy for the cloudlet network in order to optimize the tradeoff between the migration gain and the migration cost by selectively migrating the Avatars to their optimal locations.Simulation results demonstrate that as compared to the other two strategies (i.e., Follow Me Avatar and Static), PRIMAL maximizes the profit in terms of maintaining the low average E2E delay between UEs and their Avatars and minimizing the migration cost simultaneously.

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Section: Simulation Resultsmentioning

confidence: 99%

“…7, we have Eq. 4 and thus prove Lemma 1. proportional to the weighted sum of utilization of different resources [12]:…”

Section: ) Total Migration Timementioning

confidence: 99%

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PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

Sun

Ansari

2016

2016 IEEE International Conference on Communications (ICC)

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“…Research works demonstrated that considering the VMP problem for efficient management of resources could result in significant improvement in energy-efficiency, Quality of Service (QoS) and carbon dioxide emissions; all of them with high economical and ecological impact [2], [5], [7].…”

Section: Introductionmentioning

confidence: 99%

A Virtual Machine Placement Taxonomy

Pires¹,

Barán²

2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Cloud computing datacenters dynamically provide millions of virtual machines (VMs) in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant research topics such as energy efficiency, Service Level Agreement (SLA), Quality of Service (QoS), cloud service pricing schemes and carbon dioxide emissions; all of them with high economical and ecological impact. This work classifies an extensive up-to-date survey of the most relevant VMP literature proposing a novel taxonomy in order to identify research opportunities and define a general vision on this research area.

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“…Migrating a proxy VM from the source cloudlet to the destination cloudlet may introduce non-negligible energy consumption from both source and destination cloudlets [45]; thus, designing a proxy VM migration strategy without considering the migration energy consumption may significantly increase the total ongrid energy consumption [46]. From the performance of the proxy VM perspective, the migration overheads indicate the performance degradation of the proxy VM [47]. Specifically, conducting data sharing and analytics in a proxy VM consumes CPU, memory, and network resource of the proxy VM; meanwhile, proxy VM migration is considered as an expensive application, which consumes a significant amount of resources in the proxy VM.…”

Section: Future Workmentioning

confidence: 99%

Mobile Edge Computing Empowers Internet of Things

Ansari

Sun

2018

IEICE Trans. Commun.

100

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SUMMARY In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in realtime. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations.

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Virtual Machine Placement Optimization Supporting Performance SLAs

Cited by 51 publications

References 16 publications

PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

A Virtual Machine Placement Taxonomy

Mobile Edge Computing Empowers Internet of Things

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