Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

Mann, Zoltán Ádám

doi:10.1016/j.future.2015.04.004

Cited by 30 publications

(18 citation statements)

References 24 publications

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“…Although a great deal of research has been done on applying server consolidation technique to achieve energy efficiency in most of these researches the VMs are assumed to be independent of each other. [125][126][127] If a distributed policy is used for satisfying some of the requests (on several different VMs), the assumption of VMs' independence in consolidation process may degrade performance. In recent years, handling this challenge has been noted by some researchers.…”

Section: Hybrid Server Consolidation Techniquesmentioning

confidence: 99%

Server consolidation techniques in virtualized data centers of cloud environments: A systematic literature review

2018

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Summary At the virtualized data centers, services are presented by active virtual machines (VMs) in physical machines (PMs). The manner in which VMs are mapped to PMs affects the performance of data centers and the energy efficiency. By employing the server consolidation technique, it is possible to configure the VMs on a smaller number of PMs, while the quality of service is guaranteed. In this way, the rate of active PM utilization increases and fewer active PMs would be required. Moreover, the server consolidation technique reacts to the management of underloaded and overloaded PMs by using the VM migration technology. Considering the capabilities of the server consolidation technique and its role in developing the cloud computing infrastructure, many researches have been conducted in this context. Still, a comprehensive and systematic study has not yet been performed on various consolidation techniques to check the capabilities, advantages, and disadvantages of current approaches. In this paper, a systematic study is conducted on a number of credible researches related to server consolidation techniques. In order to do so and by studying the selected works, proposed solutions are categorized based on the type of decision for running the consolidation algorithm in 4 groups of static method, dynamic method, prediction‐based dynamic method, and hybrid method. Thereafter, the advantages and disadvantages of suggested approaches are studied and compared in each research by specifying the technique and idea applied therein. In addition, by categorizing aims of researches and specifying assessment parameters, optimization approaches and type of architecture, a possibility has been provided to get familiarized with the views of the researchers.

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Section: Hybrid Server Consolidation Techniquesmentioning

confidence: 99%

Server consolidation techniques in virtualized data centers of cloud environments: A systematic literature review

2018

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“…Indeed, the usage of FF has been suggested [15], just like BF [9], WF [51,65,95], FFD [99,101] and BFD [7,6,42]. It should be noted though that the approximation results concerning these algorithms on bin packing do not automatically carry over to the more complicated VM placement problem [68].…”

Section: Heuristics For the Single-dc Problemmentioning

confidence: 99%

“…For some restricted problem versions, polynomial-time approximation algorithms have been presented with rigorously proven approximation guarantees [1,2,18,68].…”

Section: Rigorous Analysismentioning

confidence: 99%

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

Mann

2015

ACM Comput. Surv.

Self Cite

210

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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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“…One of our aims was to investigate to what extent approximation algorithms are possible for VM allocation. In [148], we gave formal proofs that some of the previously proposed heuristics are actually approximation algorithms for some important sub problems, like the optimal selection of VMs to migrate from an overloaded host.…”

Section: Algorithmic Approachesmentioning

confidence: 99%

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

Vajk¹,

Harsányi

Poppe

et al. 2016

Period. Polytech. Elec. Eng. Comp. Sci.

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PrefaceThroughout László Vajta, Dean of BME VIK János Levendovszky, Deputy Dean of BME VIK Department of Automation and Applied InformaticsDepartment of Automation and Applied Informatics (AUT) is one of the largest and dynamically developing departments at BME with diverse scope competences such as control theory, embedded systems, software modelling, applied software development, Internet of Things, power electronics, mechatronics, and many others. The department's main activities are education, research and development. Training versatile electrical and software engineers with solid practical knowledge is our top priority. Our profile also includes developing high quality software and hardware solutions for industry partners. All of our activities are backed by strong research background.In 2015, the department has organized the 21 st European Wireless (EW) Conference. The EW 2015 conference hosted more than 140 participants from 30 different countries. The 2015 edition of EW was aimed at addressing a key theme on "5G and beyond."The next sections summarize the key research results of the department in year 2015. IoT and Smart CityThe Internet of Things (IoT) is transforming the surrounding physical objects into an ecosystem of information that enriches our everyday life. The IoT represents the convergence of advances in miniaturization, wireless connectivity, and increased data storage, and is driven by various sensors. Application and service development methods and frameworks are required to support the realization of solutions covering data collection, transmission, data processing, analysis, reporting, and advanced querying. Our SensorHUB framework URBMOBI is a mobile environmental sensor that (i) provides temporally and spatially distributed environmental data, (ii) fulfills the need for monitoring at various places without the costs for a large number of fixed measurement stations, (iii) integrates small and precise sensors in a system that can be operated on buses, trams, or other vehicles, (iv) concentrates on urban heat and thermal comfort, and (v) aims at providing climate services and integration with real-time climate models.utilizes the state-of-the-art open source technologies and provides a unified tool chain for IoT related application and ser-URBMOBI project has been worked out between 2013 and 2015 by the following consortium: RWTH Aachen University (Germany), Netherlands Organization for Applied Scientific Research TNO (Netherlands), ARIA Technologies (France), Budapest University of Technology and Economics (Hungary), and Meteorological and Environmental Earth Observation (Italy).The SOLSUN (Sustainable Outdoor Lighting & Sensory Urban Networks) project is about to demonstrate how intelligent city infrastructure can be created in a cost-effective and sustainable way by reusing existing street lighting as the communications backbone. We apply different technologies and methods to reduce energy consumption at the same time as turning streetlights into nodes on a scalable network that is al...

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Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

Cited by 30 publications

References 24 publications

Server consolidation techniques in virtualized data centers of cloud environments: A systematic literature review

Server consolidation techniques in virtualized data centers of cloud environments: A systematic literature review

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

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

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