Cutting Down the Energy Cost of Geographically Distributed Cloud Data Centers

2013 ◽  
pp. 279-286 ◽  
Author(s):  
Huseyin Guler ◽  
B. Barla Cambazoglu ◽  
Oznur Ozkasap
Keyword(s):  
Energy Cost ◽  
Data Centers ◽  
Cloud Data ◽  
Geographically Distributed ◽  
Cloud Data Centers ◽  
Distributed Cloud

scholarly journals On demand clock synchronization for live VM migration in distributed cloud data centers

2020 ◽  
Vol 138 ◽  
pp. 15-31 ◽  
Author(s):  
Yashwant Singh Patel ◽  
Aditi Page ◽  
Manvi Nagdev ◽  
Anurag Choubey ◽  
Rajiv Misra ◽  
...  
Keyword(s):  
Data Centers ◽  
Clock Synchronization ◽  
Vm Migration ◽  
Cloud Data ◽  
On Demand ◽  
Cloud Data Centers ◽  
Distributed Cloud

scholarly journals Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

2019 ◽  
Vol 9 (17) ◽  
pp. 3550 ◽  
Author(s):  
A-Young Son ◽  
Eui-Nam Huh
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Response Time ◽  
Data Centers ◽  
Cloud Data ◽  
Multi Objective ◽  
Service Placement ◽  
Service Response Time ◽  
Cloud Data Centers ◽  
Distributed Cloud

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

Energy and Carbon Footprint-Aware Management of Geo-Distributed Cloud Data Centers

2017 ◽  
pp. 27-46 ◽  
Author(s):  
Atefeh Khosravi ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Carbon Footprint ◽  
Data Centers ◽  
Target System ◽  
Cloud Data ◽  
Carbon Efficiency ◽  
Cloud Data Centers ◽  
Distributed Cloud ◽  
System Objective

Cloud computing provides on-demand access to computing resources for users across the world. It offers services on a pay-as-you-go model through data center sites that are scattered across diverse geographies. However, cloud data centers consume huge amount of electricity and leave high amount of carbon footprint in the ecosystem. This makes data centers responsible for 2% of the global CO2 emission. Therefore, having energy and carbon-efficient techniques for resource management in distributed cloud data centers is inevitable. This chapter presents a taxonomy and classifies the existing research works based on their target system, objective, and the technique they use for resource management in achieving a green cloud computing environment. Finally, it discusses how each work addresses the issue of energy and carbon-efficiency and also provides an insight into future directions.

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