scholarly journals Honeyguide: A VM Migration-Aware Network Topology for Saving Energy Consumption in Data Center Networks

2013 ◽  
Vol E96.D (9) ◽  
pp. 2055-2064 ◽  
Author(s):  
Hiroki SHIRAYANAGI ◽  
Hiroshi YAMADA ◽  
Kenji KONO
Keyword(s):  
Energy Consumption ◽  
Network Topology ◽  
Data Center ◽  
Data Center Networks ◽  
Saving Energy ◽  
Vm Migration

Energy-Saving in Optical Data Center Networks

2013 ◽  
Vol 411-414 ◽  
pp. 634-637
Author(s):  
Pei Pei Jiang ◽  
Cun Qian Yu ◽  
Yu Huai Peng
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Rapid Expansion ◽  
Optical Data ◽  
Data Center Networks ◽  
Vm Migration ◽  
Server Consolidation

In recent years, with the rapid expansion of network scale and types of applications, cloud computing and virtualization technology have been widely used in the data centers, providing a fast, flexible and convenient service. However, energy efficiency has increased dramatically. The problem of energy consumption has been widespread concern around the world. In this paper, we study the energy-saving in optical data center networks. First, we summarize the traditional methods of energy-saving and meanwhile reveal that the predominant energy consuming resources are the servers installed in the data centers. Then we present the server virtualization technologies based on Virtual Machines (VMs) that have been used widely to reduce energy consumption of servers. Results show server consolidation based on VM migration can efficiently reduce the overall energy consumption compared with traditional energy-saving approaches by reducing energy consumption of the entire network infrastructure in data center networks. For future work, we will study server consolidation based on VM migration in actual environment and address QoS requirements and access latency.

Reducing energy consumption in SDN-based data center networks through flow consolidation strategies

2019 ◽  
Author(s):  
Marcelo da Silva Conterato ◽  
Tiago Coelho Ferreto ◽  
Fábio Rossi ◽  
Wagner dos Santos Marques ◽  
Paulo Silas Severo de Souza
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Data Center Networks ◽  
Through Flow ◽  
Reducing Energy

scholarly journals On energy consumption of switch-centric data center networks

2017 ◽  
Vol 74 (1) ◽  
pp. 334-369 ◽  
Author(s):  
Olusogo Popoola ◽  
Bernardi Pranggono
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Data Center Networks

scholarly journals Energy Optimization for Software-Defined Data Center Networks Based on Flow Allocation Strategies

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1014 ◽  
Author(s):  
Zebin Lu ◽  
Junru Lei ◽  
Yihao He ◽  
Zhengfa Li ◽  
Shuhua Deng ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Savings ◽  
Minimum Energy ◽  
Energy Optimization ◽  
Traffic Load ◽  
Data Center Networks ◽  
Energy Consumption Optimization

Nowadays, energy consumption has become an important issue in data center networks. The most promising energy-saving schemes are those that shut down unnecessary network devices and links while meeting the demand of traffic loads. Existing research mainly focuses on the strategies of energy savings in software-defined data center networks (SD-DCN). Few studies have considered both energy savings and the quality of service (QoS) of the traffic load. In this paper, we investigate the energy savings guaranteed by traffic load satisfaction ratio. To ensure the minimum-power consumption in data centers, we formulate the SD-DCN energy consumption optimization problem as an Integer Linear Programming model. To achieve a high success rate for traffic transmission, we propose three flow scheduling strategies. On this foundation, we propose a strategy-based Minimum Energy Consumption (MEC) heuristic algorithm to ensure the QoS satisfaction ratio in the process of energy optimization. The results show that our algorithm can save energy efficiently under the conditions of low traffic load and medium traffic load. Under high traffic load, our algorithm can achieve better network performance than existing solutions in terms of quality of service satisfaction ratio of flow allocation.

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