GSAN: Green Cloud-Simulation for Storage Area Networks

The drive towards utilizing green and renewable energy to power large IT data centers is rapidly gaining momentum. Considerable research has gone into developing simulation models to comprehensively study the energy dynamics of different types of data center equipment in order to better understand how best to conserve it. Most of this research has gone towards modeling of servers and switches but relatively less amount of work is available on Storage Area Networks (SAN). Storage Area Networks play a very vital role in the data center architecture by using specialized storage hardware to manage data. In this paper, we have extended the Green Cloud simulator for computing the energy consumed by different SAN components. A simulation environment for SAN has been presented using the 'Dynamic Voltage and Frequency Scaling' (DVFS) and 'Dynamic Shutdown' (DNS) techniques. The simulation results obtained through two SAN prototype models shows the energy consumption of SAN components to vary with the network topology and type of technique used in scheduling of the workloads. Our experimental simulations revealed that there is an increasing percentage of energy savings in DNS mode for both topologies for an increasing number of servers. Results demonstrated up to 35.6% energy savings.

[1]  Rajkumar Buyya,et al.  Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges , 2010, PDPTA.

[2]  Suman Nath,et al.  Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services , 2008, NSDI.

[3]  Giorgio Ventre,et al.  Network Simulator NS2 , 2008 .

[4]  Dzmitry Kliazovich,et al.  Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers , 2013 .

[5]  Vanish Talwar,et al.  No "power" struggles: coordinated multi-level power management for the data center , 2008, ASPLOS.

[6]  Yuanyuan Zhou,et al.  Power-aware storage cache management , 2005, IEEE Transactions on Computers.

[7]  Dzmitry Kliazovich,et al.  GreenCloud: A Packet-Level Simulator of Energy-Aware Cloud Computing Data Centers , 2010, GLOBECOM.

[8]  Winfried W. Wilcke,et al.  Storage-class memory: The next storage system technology , 2008, IBM J. Res. Dev..

[9]  Dzmitry Kliazovich,et al.  GreenCloud: a packet-level simulator of energy-aware cloud computing data centers , 2010, The Journal of Supercomputing.

[10]  Xue Liu,et al.  Dynamic Voltage Scaling in Multitier Web Servers with End-to-End Delay Control , 2007, IEEE Transactions on Computers.

[11]  Sujata Banerjee,et al.  Energy Aware Network Operations , 2009, IEEE INFOCOM Workshops 2009.

[12]  Mi Zhou,et al.  Surge immunity test of personal computer at power lines , 2011, 2011 7th Asia-Pacific International Conference on Lightning.

[13]  Christopher Poelker,et al.  Storage Area Networks For Dummies , 2003 .

[14]  Klara Nahrstedt,et al.  Lightning: self-adaptive, energy-conserving, multi-zoned, commodity green cloud storage system , 2010, HPDC '10.

[15]  Liang Zhong,et al.  EnaCloud: An Energy-Saving Application Live Placement Approach for Cloud Computing Environments , 2009, 2009 IEEE International Conference on Cloud Computing.

[16]  Aameek Singh,et al.  Server-storage virtualization: Integration and load balancing in data centers , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[17]  Enrique V. Carrera,et al.  Load balancing and unbalancing for power and performance in cluster-based systems , 2001 .