Novel heuristics for consolidation of virtual machines in cloud data centers using multi-criteria resource management solutions

Arianyan, Ehsan; Taheri, Hassan; Sharifian, Saeed

doi:10.1007/s11227-015-1603-9

Cited by 50 publications

(48 citation statements)

References 26 publications

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“…In Arianyan et al, 30,31 the authors focused on consolidation problem as an efficient resource management solution to reduce energy consumption in cloud data centers. They proposed novel heuristics for 2 main phases of consolidation problem including Window Moving Average (WMA) policy for detection of overloaded hosts that considers all input criteria including CPU, RAM, and network bandwidth in the decision process and reduces the occurrence of VMs' migrations caused by instantaneous load peaks and also a novel multi-criteria VM selection method namely Multi-criteria Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with Prediction VM Selection (MTPVS) policy that selects the VMs to be migrated from overloaded hosts to both eliminating the hotspots quickly and minimizing the SLAVs due to VM migrations.…”

Section: Related Workmentioning

confidence: 99%

A learning‐based approach for virtual machine placement in cloud data centers

Ghobaei‐Arani

Rahmanian

Shamsi

et al. 2018

Int J Communication

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In recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on bestfit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well-familiar baseline VM allocation algorithms.

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Section: Related Workmentioning

confidence: 99%

A learning‐based approach for virtual machine placement in cloud data centers

Ghobaei‐Arani

Rahmanian

Shamsi

et al. 2018

Int J Communication

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“…Arianyan et al 15 proposed a novel dynamic voltage frequency system (DVFS)-aware consolidation procedure to eliminate the inconsistencies between consolidation and DVFS techniques and also proposes novel DVFS-aware algorithms for the four subproblems of online consolidation problem as well as a novel DVFS governor. More importantly, they propose a novel fuzzy multicriteria and objective resource allocation algorithm that makes it possible for resource administrators to apply the importance of different criteria in the resource management solution using fuzzy weights.…”

Section: Related Workmentioning

confidence: 99%

“…After removing outliers from the data, it is now to predict the future by an accurate ensemble prediction algorithm. The error rate of individual prediction model and the accumulative error rate of all prediction models are calculated for past k time intervals (see lines [12][13][14][15][16]. Then, a probability for choosing individual prediction models is calculated (see lines [16][17][18].…”

Section: The Proposed Prediction-based Allocation Algorithmmentioning

confidence: 99%

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

Tarahomi

Izadi

2018

Int J Communication

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With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction-based and power-aware virtual machine allocation algorithm is proposed. Also, we present a three-tier framework for energy-efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service-level agreement violation (SLAV) is also improved.KEYWORDS cloud computing, dynamic consolidation, power efficiency, three-tier cloud data centers, virtual machine allocation, virtualization | INTRODUCTIONWith the increasing speed of the Internet over the world, the cloud computing technology is becoming more and more attractive for both customers and service providers. 1,2 The rapid increase in the popularity of cloud services has led to increase in the number of cloud customers. It leads to a daily increase in cloud service demands. That is why main cloud providers such as Amazon, IBM, and Google etc have forced to increase their number of cloud data centers. This makes cloud computing area as one of the most challenging areas in computer science today. It needs a markedly increase in the amount of monetary costs. Hence, there should be a push to have more efficiency in maintenance and execution costs. There has been proposed a number of approaches to get close to this aim; however, it needs much more work to reach optimality. One of these approaches is to reduce the amount of power consumed by cloud resources and other network elements. The less the amount of power consumed, the lower is the amount of monetary costs which has to be paid pack by cloud providers. 3,4 Regarding power and cost efficiency, the amount of carbon footprint has increased drastically due to the increasing amount of power consumption. Hence, reducing power consumption not only reduces the amount of monetary costs for cloud operations but also reduces the threat of a natural disaster. 5,6 To do so, the dynamic virtual machine (VM) consolidation by live migration over runtime is known as a popular and welcoming approach. 7 By this means, we can migrate in or/and out VMs among hosts. More precisely, VMs located on a host with low load (underloaded host) can be migrated out to other active hosts, and then, the host can be switched to

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“…If the focus is merely on energy efficiency, the performance and QoS may decline. Research shows that there is a trade‐off between energy efficiency and QoS . Cao and Dong presented a heuristic framework for consolidating VM by taking power consumption and QoS into view.…”

Section: Related Workmentioning

confidence: 99%

“…Research shows that there is a trade-off between energy efficiency and QoS. 52 Cao and Dong 53 presented a heuristic framework for consolidating VM by taking power consumption and QoS into view. The proposed framework includes a decision algorithm for recognizing service level agreement (SLA) violation.…”

Section: Related Workmentioning

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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Novel heuristics for consolidation of virtual machines in cloud data centers using multi-criteria resource management solutions

Cited by 50 publications

References 26 publications

A learning‐based approach for virtual machine placement in cloud data centers

A learning‐based approach for virtual machine placement in cloud data centers

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

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

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