Data Center Simulations in OMNET++ ASAD W. MALIK NUST SCHOOL OF - PowerPoint PPT Presentation

CloudNetSim++: A Toolkit for Data Center Simulations in OMNET++ ASAD W. MALIK NUST SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, PAKISTAN

Team Members  Kashif Bilal: North Dakota State University, Fargo, USA  Khurram Aziz: Comsats institute of information technology, PAK  Dzmitry Kliazovich: University of Luxembourg, Luxembourg  Nasir Ghani: University of south florida, Florida, USA  Samee U. Khan: North Dakota State University, Fargo, USA  Rajkumar Buyya: University of Melbourne, Australia

Outline  Introduction  Related Work  Motivation  CloudNetSim++ Features  CloudNetSim++ Architecture  Performance Evaluation  Conclusion

Introduction  Cloud computing services have become increasingly popular  “Market Tends” estimates that cloud -based SaaS will increase from US $ 13.4 billion in 2011 to $32.2 billion in 2016 *  Similarly, in IaaS and PaaS markets are estimed growth from $7.6 billion in 2011 to $ 35.5 billion in 2016 *  Require massive infrastructure to support this enormous growth  Large geographically distributed data centers requires considerable amount of energy  High power consumption generates heat and requires an accompanying cooling system that costs in a range of $2 to $5 million per year * L . Columbus, “Cloud Computing and Enterprise Software Forecast Update, 2012,” Forbes , 8 Nov. 2012; www.forbes.com/sites/louiscolumbus/2012/11/08/cloud-computing-and- enterprisesoftware-forecast-update-2012

Introduction  Failure to keep data center temperature within operational ranges drastically decreases hardware reliability  The techniques, Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM) is widely adopted  Idle server may consume about 2/3 of the peak load*  Workload of data center fluctuates on the hourly basis  Average load account only 30% of data center resources**  This allow putting rest 70% into a sleep mode for most of the time  To achieve this, central coordination and energy-aware scheduling is required *Chen G, He W, Liu J, Nath S, Rigas L, Xiao L, Zhao F (2008) Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: The 5th USENIX symposium on networked systems design and implementation, Berkeley, CA, USA **Liu J, Zhao F, Liu X, He W (2009) Challenges Towards Elastic Power Management in Internet Data Centers. In: Proceedings of the 2nd international workshop on cyber-physical systems (WCPS), in conjunction with ICDCS 2009, Montreal, Quebec, Canada, June

Related Work Simulator Available Language GUI Comm. Energy Simulation Model Model Time CloudSim Open Source Java No Limited Yes Second NetworkCloudSim Open Source Java No Full No Second iCanCloud Open Source C++ Yes Full No Second DCSim+ Open Source Java No No No Minutes GreenCloud Open Source C++, oTcl Limited Full Yes Minutes

Motivation  To build a comprehensive Cloud simulator that facilitate  Students  Researchers  Industry

CloudNetSim++: Features  Support Service Level Agreement (SLA)  Support various scheduling algorithms  Distributed data centers  Configurable number of data centers  Configurable number of racks and servers  Configurable physical link properties  Energy Module  Support multiple users

CloudNetSim++: High Level Architecture Data center-II Data center-I Multiple Client Centralize Scheduler INET OMNeT++

CloudNetSim++: Node Level Architecture Compute Node Router/Switches App Module Energy Module Queue Module Energy Module Communication Communication Module Module

CloudNetSim++  Energy Computation  Flexible data center model, compute energy utilization of following components  Servers  Data center architecture, router and switches  Power management, Dynamic Voltage Frequency Scaling (DVFS) technique  V 2 ∗ F  The average power consumption is stated as below  P = P C + CPU f ∗ f C : power consumed not scale to frequency  P  CPU f ∗ f : represent frequency depended power consumption

CloudNetSim++  Power consumption of switches stated as: 𝑆  𝑄 𝑡𝑥𝑗𝑢𝑑ℎ = 𝑄 𝑑ℎ𝑏𝑡𝑡𝑗𝑡 + 𝑜 𝑚𝑗𝑜𝑓𝑑𝑏𝑠𝑒 . 𝑄 𝑚𝑗𝑜𝑓𝑑𝑏𝑠𝑒 + 𝑗=0 𝑜 𝑞𝑝𝑠𝑢,𝑠 . 𝑄 𝑠 𝑑ℎ𝑏𝑡𝑡𝑗𝑡 : Power consumed by switch hardware  𝑄  𝑄 𝑚𝑗𝑜𝑓𝑑𝑏𝑠𝑒 : Power consumed by a line card 𝑠 : Power consumed by a port operating at rate r  𝑄

CloudNetSim++: Graphical User Interface

CloudNetSim++

CloudNetSim++: Performance Evaluation  Used two different traffic scenarios  Many-to-one model  Many-to-many model

S.No Simulation Parameters Parameters Value 1 Inter-Data Center (DC) topology Star/Mesh 2 Intra-DC topology three-tier 3 Inter-DC link 100-Gbps 4 Data center to data center link (Bit Error Rate) 10 −12 5 Core to aggregate link 10 Gbps 6 Aggregate to access link 1 Gbps 7 Access to servers link 1 Gbps 8 Core to aggregate link (BER) 10 −12 9 Aggregate to access link (BER) 10 −12 10 Access link to computing servers (BER) 10 −5 11 Packet size 1500 bytes 12 Core nodes 8 13 Aggregate nodes 16 14 Access nodes 256 15 Computing server 2200 - 9000

CloudNetSim++: Performance Evaluation 4.086 Core Switch(kWh) 98 DC-East(kWh) 9.21 156 Aggregate Switch(kWh) DC-West(kWh) Access Switch(kWh) 45 16.218 DC-South(kWh) Server(kWh) DC-North(kWh) 70.633 200

CloudNetSim++: Performance Evaluation

CloudNetSim++: Performance Evaluation

CloudNetSim++: Performance Evaluation

CloudNetSim++: Available download  Available for download at  http://cloudnetsim.seecs.edu.pk/

Conclusion  Designed to facilitate students, researchers and industry requirement  Provide rich Graphical User Interface – GUI  Modular approach, new modules can easily be incorporated  Configurable architecture  Open source, available to download

CloudNetSim++ cloudnetsim.seecs.edu.pk Thank You!

References A. Vahdat, M. Fares, N, Farrington, R. N. Mysore, G. Porter, and S. Radhakrishnan , “Scale -Out  Networking in the Data Center,” IEEE Micro . Vol. 30, no. 4, p. 29-41, 2010. P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan , “Energy Aware Network  Operations,” in INFOCOM Workshops , pp. 25-30, 2009. J. Shuja, S. A. Madani, K. Bilal, K. Hayat, S. U. Khan, and S. Sarwar , “Energy -efficient data  centers,” Computing , vol. 94 no. 12 pp. 973-994, 2012. R. Beik , “Green Cloud Computing: An Energy - Aware Layer in Software Architecture,” in Spring  Congress on Engineering and Technology (S-CET) , pp. 1-4, 2012. J. Moore, J.Chase, P. Ranganathan, and R.Sharma , “Making scheduling cool: temperature -  aware workload placement in data centers,” in USENIX Annual Technical Conference pp. 61-75, 2005. N. Rasmussen, “Calculating Total Cooling Requirements for Data Centers,” produced by  Schneider Electrics Data Center Science Center , 2011, http://www.apcmedia.com/salestools/NRAN5TE6HE/NRAN-5TE6HE R3 EN.pdf. Accessed 31 August 2014.

References K. Bilal, S. U. R. Malik, S. U. Khan, and A. Y. Zomaya, "Trends and Challenges in Cloud Data Centers," IEEE  Cloud Computing Magazine , vol. 1, no. 1, pp. 10-20, 2014. K. Bilal, S. U. Khan, and A. Y. Zomaya, "Green Data Center Networks: Challenges and Opportunities,"  in 11th IEEE International Conference on Frontiers of Information Technology (FIT) , pp. 229-234. 2013. R. Buyya, R. Ranjan, and R.N. Calheiros , “Modeling and simulation of scalable Cloud computing  environments and the CloudSim toolkit: Challenges and opportunities,” in International Conference of High Performance Computing & Simulation , pp. 1-11, 2009. X. Li, X. Jiang, K. Ye, and P. Huang, “ DartCSim+: Enhanced CloudSim with the Power and Network  Models Integrated,” in IEEE Sixth International Conference on Cloud Computing , pp.644- 651,2013. S. K. Garg, and R. Buyya , “ NetworkCloudSim : modelling parallel applications in cloud simulations.” in  Fourth IEEE International Conference on Utility and Cloud Computing (UCC) , pp. 105-113, 2011. D. Kliazovich, P. Bouvry, Y. Audzevich , and S. U. Khan, “ GreenCloud: A Packet-Level Simulator of Energy-  Aware Cloud Computing Data Centers,” in IEEE Global Telecommunications Conference (GLOBECOM), pp. 1-5, 2010.