MEnSuS: An efficient scheme for energy management with sustainability of cloud data centers in edge–cloud environment

2018 ◽  
Vol 86 ◽  
pp. 1279-1300 ◽  
Author(s):  
Gagangeet Singh Aujla ◽  
Neeraj Kumar
Keyword(s):  
Energy Management ◽  
Data Centers ◽  
Cloud Environment ◽  
Cloud Data ◽  
Efficient Scheme ◽  
Cloud Data Centers

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

Kybernetes ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Haiyan Zhuang ◽  
Babak Esmaeilpour Ghouchani
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Cloud Services ◽  
Future Research ◽  
Cloud Environment ◽  
Content Type ◽  
Cloud Data ◽  
Refining Performance ◽  
Cloud Data Centers ◽  
Cloud Environments

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.

scholarly journals Real-Time Energy Management for Cloud Data Centers in Smart Microgrids

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 941-950 ◽  
Author(s):  
Liang Yu ◽  
Tao Jiang ◽  
Yulong Zou
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Enhanced IPFIX flow monitoring for VXLAN based cloud overlay networks

2019 ◽  
Vol 9 (6) ◽  
pp. 5519
Author(s):  
Osman Ghazali ◽  
Shahzada Khurram
Keyword(s):  
Network Traffic ◽  
Data Centers ◽  
Overlay Networks ◽  
Overlay Network ◽  
Monitoring Systems ◽  
Cloud Environment ◽  
Cloud Data ◽  
Flow Monitoring ◽  
Monitoring Mechanism ◽  
Cloud Data Centers

<span lang="EN-US">The demands for cloud computing services is rapidly growing due to its fast adoption and the migration of workloads from private data centers to cloud data centers. Many companies, small and large, prefer switching their data to the enterprise cloud environment rather than expanding their own data centers. As a result, the network traffic in cloud data centers is increasing rapidly. However, due to the dynamic resource provisioning and high-speed virtualized cloud networks, the traditional flow-monitoring systems is unable to provide detail visibility and information of traffic traversing the cloud overlay network environment. Hence, it does not fulfill the monitoring requirement of cloud overlay traffic. As the growth of cloud network traffic causes difficulties for the service providers and end-users to manage the traffic efficiently, an enhanced IPFIX flow monitoring mechanism for cloud overlay networks was proposed to address this problem. The monitoring mechanism provided detail visibility and information of overlay network traffic that traversed the cloud environment, which is not available in the current network monitoring systems. The experimental results showed that the proposed monitoring system able to capture overlay network traffic and segregated the tenant traffic based on virtual machines as compare to the standard monitoring system.</span>

scholarly journals Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Shanchen Pang ◽  
Weiguang Zhang ◽  
Tongmao Ma ◽  
Qian Gao
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Task Scheduling ◽  
Data Centers ◽  
System Model ◽  
Management Problem ◽  
Ant Colony Optimization Algorithm ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Dynamic Energy Management

With the wide deployment of cloud computing data centers, the problems of power consumption have become increasingly prominent. The dynamic energy management problem in pursuit of energy-efficiency in cloud data centers is investigated. Specifically, a dynamic energy management system model for cloud data centers is built, and this system is composed of DVS Management Module, Load Balancing Module, and Task Scheduling Module. According to Task Scheduling Module, the scheduling process is analyzed by Stochastic Petri Net, and a task-oriented resource allocation method (LET-ACO) is proposed, which optimizes the running time of the system and the energy consumption by scheduling tasks. Simulation studies confirm the effectiveness of the proposed system model. And the simulation results also show that, compared to ACO, Min-Min, and RR scheduling strategy, the proposed LET-ACO method can save up to 28%, 31%, and 40% energy consumption while meeting performance constraints.

An Adaptive Grid Frequency Support Mechanism for Energy Management in Cloud Data Centers

2020 ◽  
Vol 14 (1) ◽  
pp. 1195-1205 ◽  
Author(s):  
Kuljeet Kaur ◽  
Sahil Garg ◽  
Neeraj Kumar ◽  
Gagangeet Singh Aujla ◽  
Kim-Kwang Raymond Choo ◽  
...  
Keyword(s):  
Energy Management ◽  
Data Centers ◽  
Adaptive Grid ◽  
Cloud Data ◽  
Support Mechanism ◽  
Cloud Data Centers
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