Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Theja, Perla Ravi; Babu, S. K. Khadar

doi:10.1515/cait-2016-0023

Cited by 15 publications

(12 citation statements)

References 25 publications

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“…A drawback in some EAs [7,10,13] is that they fail to take into account the adverse effects of heavy weather. In this particular case it is safe to divide the evolutionary algorithms into two major categories:…”

Section: Division Of the Easmentioning

confidence: 99%

Application of Information Technologies and Algorithms in Ship Passage Planning

Belev

Dimitranov

Spasov

et al. 2019

Cybernetics and Information Technologies

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With the continuous increase of international oil prices, more and more shipping companies look for new solutions to the ever present question: How to reduce operational fuel consumption and decrease air pollution. Ship route planning is an indispensable part of the ship navigation process. In the modern world, the passage planning aspect of navigation is shifting. No longer do we see mariners drawing course lines on a paper chart. No longer do they calculate distances with compasses. Elaborate algorithms on various digital devices perform all these tasks. Algorithms plot the optimum tracks on digital charts and algorithms can decide how to avoid collision situations. Nowadays charter companies do not rely solely on the experienced navigators on board their vessels to decide the best route. Instead, this task is outsourced ashore to routing and weather-routing enterprises. The algorithms used by those enterprises are continuously evolving and getting better and better. They are coming popular because of another reason – more and more the shipping society support the newly idea for using crewless ships. However, are they up to the task to eliminate the human element in passage planning? In this article, we are going to review some of the weak points of the algorithms in use.

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Section: Division Of the Easmentioning

confidence: 99%

Application of Information Technologies and Algorithms in Ship Passage Planning

Belev

Dimitranov

Spasov

et al. 2019

Cybernetics and Information Technologies

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“…Therefore, if the host has a high load, this approach will select the virtual machine with the least amount of memory in comparison with other virtual transfer devices to the host for transfer. The above method chooses virtual device u to have the following conditions [17], [19].…”

Section: Minimum Migration Time (Mmt)mentioning

confidence: 99%

“…So, if a host has heavy load, machines that use the most resources on the host (use processor resources and memory more than others and known as a large virtual machine) is chosen for displacement. The above approach chooses the virtual machine VM u if it satisfies the following condition [17], [18].…”

Section: Maximum Correlation (Mc)mentioning

confidence: 99%

Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine Placement

Mohazabiyeh

Amirizadeh

2018

IJECE

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With the increasing expansion of cloud data centers and the demand for cloud services, one of the major problems facing these data centers is the “increasing growth in energy consumption ". In this paper, we propose a method to balance the burden of virtual machine resources in order to reduce energy consumption. The proposed technique is based on a four-adaptive threshold model to reduce energy consumption in physical servers and minimize SLA violation in cloud data centers. Based on the proposed technique, hosts will be grouped into five clusters: hosts with low load, hosts with a light load, hosts with a middle load, hosts with high load and finally, hosts with a heavy load. Virtual machines are transferred from the host with high load and heavy load to the hosts with light load. Also, the VMs on low hosts will be migrated to the hosts with middle load, while the host with a light load and hosts with middle load remain unchanged. The values of the thresholds are obtained on the basis of the mathematical modeling approach and the 𝐾-Means Clustering Algorithm is used for clustering of hosts. Experimental results show that applying the proposed technique will improve the load balancing and reduce the number of VM migration and reduce energy consumption.

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“…Computer networks, communication systems [5], data centers [9,16], huge production of mobile phones [4,7], and other IT infrastructures have caused severe environmental problems by consuming significant amounts of power [15], increasing greenhouse gas emissions, and lead to pollution during the production and disposal.…”

Section: Related Workmentioning

confidence: 99%

Measuring Energy Efficiency of Selected Working Software

Szabo

Alzeyani

2018

Studia UBB Informatica

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Energy consumption is a key performance indicator of any software run on mobile devices. Working or application software is the main category of software where such energy (in)efficient performance becomes accelerated between users and other stakeholders. Measuring energy efficiency is becoming a part of automated and manual performance testing as well-both answering to the increasing usage requirements and addressing acceptance testing optimization. In this paper, we select three software tools-an e-mail client and two social network applications, those energy consumption is being measured and analyzed. We decided to apply very generic profiles during our measurements, where the actions were performed all manually. Our results show that besides the difference in the number of features covered by the software, also their implementation plays an important role in energy consumption. Focusing on a specific feature within the working software does not imply that all quality indicators of it are the best among the software group's implementations.

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Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Cited by 15 publications

References 25 publications

Application of Information Technologies and Algorithms in Ship Passage Planning

Application of Information Technologies and Algorithms in Ship Passage Planning

Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine Placement

Measuring Energy Efficiency of Selected Working Software

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