Abstract
A data center is a facility with a group of networked servers used by an organization for storage, management and dissemination of its data. The increase in data center energy consumption over the past several years is staggering, therefore efforts are being initiated to achieve energy efficiency of various components of data centers. One of the main reasons data centers have high energy inefficiency is largely due to the fact that most organizations run their data centers at full capacity 24/7. This results into a number of servers and switches being underutilized or even unutilized, yet working and consuming electricity around the clock. In this paper, we present Adaptive TrimTree; a mechanism that employs a combination of resource consolidation, selective connectedness and energy proportional computing for optimizing energy consumption in a Data Center Network (DCN). Adaptive TrimTree adopts a simple traffic-and-topology-based heuristic to find a minimum power network subset called ‘active network subset’ that satisfies the existing network traffic conditions while switching off the residual unused network components. A ‘passive network subset’ is also identified for redundancy which consists of links and switches that can be required in future and this subset is toggled to sleep state. An energy proportional computing technique is applied to the active network subset for adapting link data rates to workload thus maximizing energy optimization. We have compared our proposed mechanism with fat-tree topology and ElasticTree; a scheme based on resource consolidation. Our simulation results show that our mechanism saves 50%–70% more energy as compared to fat-tree and 19.6% as compared to ElasticTree, with minimal impact on packet loss percentage and delay. Additionally, our mechanism copes better with traffic anomalies and surges due to passive network provision.
Highlights
Data centers comprise of large computational and storage systems interconnected through a communication network serving a large number of popular services in the Internet such as search engines (e.g., Google), Internet commerce (e.g., Amazon and e-Bay), web based e-mail (e.g., Yahoo mail), social networking (e.g., Myspace and Facebook) and video sharing (e.g., YouTube)
About 20% of energy consumption in a data center occurs in the Data Center Network (DCN) which lies at the core of a data center since it connects a large number of servers at various hierarchies through switches [1]
This paper presents Adaptive TrimTree; a mechanism for a green DCN which proposes a combination of resource consolidation, selective connectedness and proportional computing methods to achieve optimal energy consumption with minimal performance compromises
Summary
Data centers comprise of large computational and storage systems interconnected through a communication network serving a large number of popular services in the Internet such as search engines (e.g., Google), Internet commerce (e.g., Amazon and e-Bay), web based e-mail (e.g., Yahoo mail), social networking (e.g., Myspace and Facebook) and video sharing (e.g., YouTube). In addition to computational and storage systems, the data centers consist of power supply equipment, communication network, air conditioning, security systems and other related devices and can span over an area as large as a small town. Wang et al stated that in a typical Google data center the network power is approximately 20% of the total power when the servers are utilized at 100%, but it increases to 50%. The DCN can connect hundreds or thousands of servers to support various applications and cloud computing. It is a general practice that the network devices of a data center are always kept in an “on” state, resulting in around 67.7 W energy consumption even in an idle state. The ever-increasing DCN cost and energy consumption have spurred the interest of the networking community to develop energy-efficient protocols and to devise methods to reduce energy consumption in DCNs striving for green data centers
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