Power-efficient resource-guaranteed VM placement and routing for time-aware data center applications

Dalvandi, Aissan; Gurusamy, Mohan; Chua, Kee Chaing

doi:10.1016/j.comnet.2015.06.017

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…5), and a fuzzy set is created for each interval. The idea is to divide the Universe of Discourse from time series in intervals/partitions (the fuzzy sets) and learn how each area behaves (extracting rules through the time series patterns) [10]. The rules of these models tell how the partitions relate with themselves over time, as values jump from one place to another.…”

Section: Resultsmentioning

confidence: 99%

Fuzzy Model for Time Series Forecasting

Ibraeva

Bektemyssova

Ahmad

2023

sjaitu

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In 2007, in Kazakhstan, there was a transition of TDM (Time Division Multiplexing) circuit-switched technologies to IP (Internet Protocol) packet technology, which created a modern infrastructure for the ICT (information communication technologies) sphere. The advent of the IoT (Internet of Things) concept has led to the growth of a functioning network at a faster rate. It is currently developing in the direction of a cognitive infocommunication network. Its evolutionary development is characterized by a change in the volume of transmitted information, types of its presentation, methods of transmission and storage, the number of sources and consumers, distribution among users, requirements for timeliness and reliability (quality) [1]. Types of traffic and their structure are changing, therefore data processing becomes more complicated. For this reason, the tasks of analyzing and predicting network traffic remain relevant. In this work, the prediction of the measured traffic on a real network is performed. The series under study shows the totality of packets transmitted over the backbone network for each second. Forecasting of a one-dimensional time series is carried out on the basis of fuzzy logic methods. This class of models is well suited for modeling nonlinear systems and time series forecasting. The use of fuzzy sets is based on the ability of fuzzy models to approximate functions, as well as on the readability of rules using linguistic variables. The results of the software algorithm of fuzzy inference models were obtained using the Python environment. Membership functions, predictive graphs were built and their evaluation was carried out. The numerical values of the root mean square error (MSE) are calculated. As a result, it was found that the Cheng fuzzy prediction model has higher forecast accuracy than the Chen forecasting method.

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Section: Resultsmentioning

confidence: 99%

Fuzzy Model for Time Series Forecasting

Ibraeva

Bektemyssova

Ahmad

2023

sjaitu

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“…In the cloud environment, virtualization is one of the main technologies (Gerrard et al , 2018). It divides services and resources from the fundamental physical delivery environment (Dalvandi et al , 2015). The placement of the VM includes opting for an appropriate host physical machine for a VM (Farhan et al , 2018; Mishra and Bellur, 2014).…”

Section: Review Of Selected Papersmentioning

confidence: 99%

Virtual machine placement mechanisms in the cloud environments: a systematic review

Zhuang

Ghouchani

2020

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Purpose Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future. Design/methodology/approach Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms. Findings The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance. Research limitations/implications This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP. Practical implications The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area. Originality/value The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.

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“…Probing into the power-efficient and resource-guaranteed VM placement and routing problem (PER-TTA) for dynamically arriving TTA requests, [11] suggested using the multi-component utilization-based power model to minimize the total power consumption, and adopted the Least-Active Most-Utilized policy, which avoids powering on new devices by maximizing the resource utilization of the powered-on devices. In this way, the PER-TTA was transformed into a mixedinteger linear programming (MILP) optimization problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Virtual Machine Scheduling Model Based on Energy and Interference Awareness in Cloud Environment

Xin¹,

Li²,

Liú³

et al. 2017

AMA_B

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Thanks to the rapid development of cloud datacenter, virtual machine (VM) scheduling has become the key to optimizing energy consumption, service-level agreement, network traffic, etc. Focusing on the optimization of server CPU utilization, energy consumption, network traffic, service performance and so on, the current VM scheduling model often fails to recognize the performance interference between VMs as an optimization parameter. In light of the above, this paper proposes a VM scheduling model, considering both server power consumption and VM performance interference, seeking to lower the energy consumption of the datacenter and the interference between VMs. The experimental results demonstrate that the proposed model outshines the other two models in server CPU utilization, energy consumption, and VM process 443 time.

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Power-efficient resource-guaranteed VM placement and routing for time-aware data center applications

Cited by 10 publications

References 24 publications

Fuzzy Model for Time Series Forecasting

Fuzzy Model for Time Series Forecasting

Virtual machine placement mechanisms in the cloud environments: a systematic review

Virtual Machine Scheduling Model Based on Energy and Interference Awareness in Cloud Environment

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