Adaptive virtual machine migration mechanism for energy efficiency

Sohrabi, Sahar; Tang, Antony; Moser, Irene; Aleti, Aldeida

doi:10.1145/2896967.2896969

Cited by 19 publications

(24 citation statements)

References 33 publications

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“…The box plots for number of VM migrations performed by EAM in comparison to ThrMMT, MOH, ThrMaxUtil, and BMH. EAM, Energy‐efficient Adaptive Migration; MMT, Minimum Migration Time; MOH, Multi‐Objective Heuristic …”

Section: Discussionmentioning

confidence: 99%

“…In this section, our Bayesian based migration mechanism is explained. Our Energy‐efficient Adaptive Migration (EAM) mechanism extends our BMH by including an adaptive VM mapping mechanism on top of its adaptive VM migration mechanism. This inclusion is not a joint effort of previously reported works but rather the integration of VM selection and VM mapping mechanisms.…”

Section: Proposed Energy‐efficient Adaptive Migration Mechanismmentioning

confidence: 99%

“…One of the differences between this mechanism and its precedent is the way VM types are defined. In our earlier study, the VM types are defined based on the quartiles of the observed VM CPU utilization values.…”

Section: Proposed Energy‐efficient Adaptive Migration Mechanismmentioning

confidence: 99%

“…Our BMH mechanism was developed to achieve adaptivity while optimizing energy consumption and execution time. Although BMH proved to outperform leading migration strategies, it can be further optimized by incorporating adaptive mapping mechanism to it.…”

Section: Related Workmentioning

confidence: 99%

“…This paper is an extended version of our adaptive VM migration mechanism . In this paper, we make the following contributions: (1) developing a VM migration mechanism inclusive of an adaptive VM selection and VM mapping and (2) significantly reducing total energy consumption in the context of private Clouds.…”

Section: Introductionmentioning

confidence: 99%

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Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

Sohrabi

Yang

Moser

et al. 2017

Concurrency and Computation

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Summary Private Cloud provides Cloud services with its relatively limited resources compared to public Clouds. Resources in private Clouds should be used energy efficiently. The resource utilization is determined by the assignment of virtual machines (VMs) to hosts. Because of the frequent changes in the resource requests on VMs, the system might become imbalanced, some hosts are overloaded/underloaded. Virtual machine migration is a solution to ease the imbalance problem. Virtual machine migration includes the selection of a VM for migration and decision upon where it should be taken to (mapped). In this work, VM selection and VM mapping are integrated and aimed to ease the imbalance problem for energy efficiency. Moreover, selection and mapping have become adaptive to ease imbalance, while optimizing energy consumption and adaptively responding to changes in the system. Our proposed adaptive mechanism applies Bayesian inference to estimate the likelihood of a VM migration decision, both VM selection for migration and VM mapping, optimizing energy consumption. The proposed mechanism is evaluated on CloudSim using PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumption significantly, (on average) by 116%, while its total execution time is also, (on average) 5.39 times, shorter than the competing state‐of‐the‐art policies.

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Section: Discussionmentioning

confidence: 99%

Section: Proposed Energy‐efficient Adaptive Migration Mechanismmentioning

confidence: 99%

Section: Proposed Energy‐efficient Adaptive Migration Mechanismmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

Sohrabi

Yang

Moser

et al. 2017

Concurrency and Computation

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An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

Chen

Wen

Wang

2023

Concurrency and Computation

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To meet the ever-increasing requirements of the applications, cloud service providers have further built and managed multiple cloud data centers in multiple regions across geographies with many physical machines (PMs). However, most of the existing resource allocation algorithms are developed for a single cloud data center, which normally cannot efficiently handle the load burst occasions where a single cloud data center may not be enough to satisfy the demands burst of applications. Therefore, it is necessary to consider how to efficiently manage multiple cloud data centers while meeting application requirements and reducing energy consumption. This paper first systematically analyzes multiple cloud data centers and energy consumption models.Then, an energy-efficient method of Resource Allocation based on Request Prediction in multiple cloud data centers (RARP) is proposed. The RARP method constructs a resource allocation framework based on request prediction in multiple cloud data centers, which anticipates the application request volume in advance. At the same time, the RARP method allocates VMs and PMs based on the principle of minimum remaining resources available to achieve minimum usage of PMs, thus minimizing energy consumption to complete application requests. Extensive experiments are conducted on the proposed RARP method through the simulation platform CloudSim. Finally, the experimental test results show that the accuracy of request detection and the energy consumption of cloud data centers are significantly better than those of the comparison algorithms.

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Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

Kilanioti

Fernández-Montes

Fernández-Cerero

et al. 2019

Lecture Notes in Computer Science

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This chapter presents the authors' work for the Case Study entitled "Delivering Social Media with Scalability" within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with

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Adaptive virtual machine migration mechanism for energy efficiency

Cited by 19 publications

References 33 publications

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

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