Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Ismaeel, Salam; Karim, Raed; Miri, Ali

doi:10.1186/s13677-018-0111-x

Cited by 53 publications

(31 citation statements)

References 153 publications

(230 reference statements)

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“…The approach in [71] estimates the energy consumed and schedules based on the same. Other work as with [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82] orchestrate VMs to the Physical Machines with a focus on conservation of energy.…”

Section:  Orchestration Of Provisioning and Scheduling Of Vms To The Pmentioning

confidence: 99%

Optimizing Energy Efficiencies in Cloud Data Center Resources with Availability Constraints

Phalachandra,

Sitaram

2020

IJEAT

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Cloud infrastructure Resources hosted in Data Centers, support the effective execution of Cloud computing applications. Given the increased adoption of the Cloud Computing Applications and the Businesses getting to be Data-driven, there is a huge increase in the number of Data Centers and the Size and amount of resources hosted in these Data Centers. These Data Center resources consume a significant amount of energy and this continuous scaling of the resources is leading to increased power consumption and a large carbon footprint. Given our fragile eco-system, optimization of the Data Center resources for energy conservation and thus the carbon footprint is the primary area of our focus. Businesses also need to satisfy QoS guarantees on Availability to their customers. Optimization towards Energy efficiencies may compromise on the Availability and thus may warrant a trade-off, and a need for them to be considered together. Although there have been numerous studies towards Energy efficiencies, most of them have been focused on only energy. In this paper, we initially segregate Optimization activities towards the Data Center resources like Compute, Network, and Storage. We then study the different control parameters or approaches which will lead to meeting the objectives of Energy Efficiencies, Availability and Energy Efficiency constrained with Availability. Thus, this will support the selection of approaches for the optimization of energy while meeting the QoS Availability requirement.

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Section:  Orchestration Of Provisioning and Scheduling Of Vms To The Pmentioning

confidence: 99%

Optimizing Energy Efficiencies in Cloud Data Center Resources with Availability Constraints

Phalachandra,

Sitaram

2020

IJEAT

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“…In the process of training, the multi-modal natural language database is used for training. Because this method is directed disambiguation, the selected corpus needs to be labeled manually [21][22][23][24]. At the same time, for each meaning of each polysemous word, the same number of examples should be chosen for learning.…”

Section: The Process Of Training Modelsmentioning

confidence: 99%

Feature Input Symmetry Algorithm of Multi-Modal Natural Language Library Based on BP Neural Network

Lin

2019

Symmetry

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When using traditional knowledge retrieval algorithms to analyze whether the feature input of words in multi-modal natural language library is symmetrical, the symmetry of words cannot be analyzed, resulting in inaccurate analysis results. A feature input symmetric algorithm of multi-modal natural language library based on BP (back propagation) neural network is proposed in this paper. A Chinese abstract generation method based on multi-modal neural network is used to extract Chinese abstracts from images in multi-modal natural language library. The Word Sense Disambiguation (WSD) Model is constructed by the BP neural network. After the word or text disambiguation is performed on the Chinese abstract in the multi-modal natural language library, the feature input symmetry problem in the multi-modal natural language library is analyzed according to the sentence similarity. The experimental results show that the proposed algorithm can effectively analyze the eigenvalue symmetry problem of the multi-modal natural language library. The maximum error rate of the analysis algorithm is 7%, the growth rate of the analysis speed is up to 50%, and the average analysis time is 540.56 s. It has the advantages of small error and high efficiency.

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“…As a cloud is realized on large-scale usually distributed data-centers, it consumes an enormous amount of energy. In 2012, energy consumption by data centers worldwide *Correspondence: fikreyes05@yahoo.com 1 Ethio Telecom, Addis Ababa, Ethiopia 2 School of Electrical and Computer Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia was 300 -400 tera-watt hour, about 2% of the global electricity usage, and it is estimated to triple by 2020 [3]. VM consolidation using live VM migration [4] optimizes power utilization by running VMs in as much few servers as possible and putting the rest in sleep mode or turning them off.…”

Section: Introductionmentioning

confidence: 99%

Energy-aware VM placement algorithms for the OpenStack Neat consolidation framework

Moges

Abebe

2019

J Cloud Comp

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One of the main challenges in cloud computing is an enormous amount of energy consumed in data-centers. Several researches have been conducted on Virtual Machine(VM) consolidation to optimize energy consumption. Among the proposed VM consolidations, OpenStack Neat is notable for its practicality. OpenStack Neat is an open-source consolidation framework that can seamlessly integrate to OpenStack, one of the most common and widely used opensource cloud management tool. The framework has components for deciding when to migrate VMs and for selecting suitable hosts for the VMs (VM placement). The VM placement algorithm of OpenStack Neat is called Modified Best-Fit Decreasing (MBFD). MBFD is based on a heuristic that handles only minimizing the number of servers. The heuristic is not only less energy efficient but also increases Service Level Agreement (SLA) violation and consequently cause more VM migrations. To improve the energy efficiency, we propose VM placement algorithms based on both bin-packing heuristics and servers' power efficiency. In addition, we introduce a new bin-packing heuristic called a Medium-Fit (MF) to reduce SLA violation. To evaluate performance of the proposed algorithms we have conducted experiments using CloudSim on three cloud data-center scenarios: homogeneous, heterogeneous and default. Workloads that run in the data-centers are generated from traces of PlanetLab and Bitbrains clouds. The results of the experiment show up-to 67% improvement in energy consumption and up-to 78% and 46% reduction in SLA violation and amount of VM migrations, respectively. Moreover, all improvements are statistically significant with significance level of 0.01.

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Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Cited by 53 publications

References 153 publications

Optimizing Energy Efficiencies in Cloud Data Center Resources with Availability Constraints

Optimizing Energy Efficiencies in Cloud Data Center Resources with Availability Constraints

Feature Input Symmetry Algorithm of Multi-Modal Natural Language Library Based on BP Neural Network

Energy-aware VM placement algorithms for the OpenStack Neat consolidation framework

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