This section presents some suggestions for the future development of CloudSimDisk.
At rst, real case scenarios should be performed using benchmarking tool to mea-sure the energy consumption of a specic HDD under dierent workload. Similar scenarios should be run using CloudSimDisk module and results should be compared with real world measures.
Then, models should be expanded to improve modeling of modern HDDs, including read/write dierentiation, internal buer and spin-down capability. Also, while the seek time is one of the most important performance specication, it is a challenging task to model it accurately. In fact, this time is not linear and, for each operation, it is aected by a multitude of factors, including the size of the le to process, the disk fragmentation, the le system type or even performed operation (read or write).
Thus, new parameters should be implemented in CloudSimDisk.
Furthermore, the network package in CloudSim could be linked with CloudSimDisk to model the network latency between servers and the persistent storage, and its related energy consumption. However, this amount of energy consumed should not be merged with the current persistent storage energy consumption. The modularity of the implementation should be preserved; the network is an independent part of the data center architecture.
REFERENCES
[1] M. Castells, End of Millennium: The Information Age: Economy, Society, and Culture. John Wiley & Sons, 2010, vol. 3.
[2] M. Castells, The power of identity: The information age: Economy, Society, and Culture. John Wiley & Sons, 2011, vol. 2.
[3] M. Pickavet, W. Vereecken, S. Demeyer, P. Audenaert, B. Vermeulen, C. De-velder, D. Colle, B. Dhoedt, and P. Demeester, Worldwide Energy Needs for ICT: The Rise of Power-Aware Networking, in 2nd International Symposium on Advanced Networks and Telecommunication Systems (ANTS'08). IEEE, 2008, pp. 13.
[4] J. G. Charney, A. Arakawa, D. J. Baker, B. Bolin, R. E. Dickinson, R. M.
Goody, C. E. Leith, H. M. Stommel, and C. I. Wunsch, Carbon Dioxide and Climate: a Scientic Assessment, National Academy of Sciences, Washington, DC, 1979.
[5] J.-R. Petit, J. Jouzel, D. Raynaud, N. I. Barkov, J.-M. Barnola, I. Basile, M. Bender, J. Chappellaz, M. Davis, G. Delaygue et al., Climate and Atmo-spheric History of the Past 420,000 Years From the Vostok Ice Core, Antarc-tica, Nature, vol. 399, no. 6735, pp. 429436, 1999.
[6] T. POOG, What Is Your Carbon Foolprint? http://thepoog.com/?p=2732, July, 2, 2012, accessed: 2015.05.15.
[7] Global e-Sustainability Initiative et al., Smart 2020: Enabling the Low Carbon Economy in the Information Age, Global ICT Solution Case Studies, The Climate Group, Tech. Rep, 2008.
[8] A. Adams and N. Mishra, User Survey Analysis: Key Trends Shaping the Future of Data Center Infrastructure Through 2011, Gartner Market Analysis and Statistics, 2010.
[9] J. Gantz and D. Reinsel, Extracting Value From Chaos, IDC iView, vol. IDC, no. 1142, pp. 910, 2011.
[10] J. Gantz and D. Reinsel, The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East, IDC iView: IDC Analyze the Future, vol. 2007, pp. 116, 2012.
[11] M. Hilbert and P. López, The World's Technological Capacity to Store, Com-municate, and Compute Information, Science, vol. 332, no. 6025, pp. 6065, 2011.
[12] L. Atzori, A. Iera, and G. Morabito, The Internet of Things: A Survey, Com-puter Networks, vol. 54, no. 15, pp. 27872805, 2010.
[13] SINTEF, Big Data, for Better or Worse: 90% of World's Data Gener-ated Over Last Two Years, http://www.sciencedaily.com/releases/2013/05/
130522085217.htm. ScienceDaily., May 2013, accessed: 2015.04.15.
[14] V. Turner, J. F. Gantz, D. Reinsel, and S. Minton, The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things, International Data Corporation, White Paper, IDC_1672, 2014.
[15] Gil Press, A Very Short History Of Big Data, http://www.forbes.com/
sites/gilpress/2013/05/09/a-very-short-history-of-big-data/, Forbes, Decem-ber 2013, accessed: 2015.03.13. [Online]. Available: http://www.forbes.com/
sites/gilpress/2013/05/09/a-very-short-history-of-big-data/
[16] R. Ranjan, Modeling and Simulation in Performance Optimization of Big Data Processing Frameworks, Cloud Computing, IEEE, vol. 1, no. 4, pp. 1419, 2014.
[17] C. Snijders, U. Matzat, and U.-D. Reips, Big Data: Big Gaps of Knowledge in the Field of Internet Science, International Journal of Internet Science, vol. 7, no. 1, pp. 15, 2012.
[18] A. De Mauro, M. Greco, and M. Grimaldi, What is Big Data? A Consensual Denition and a Review of Key Research Topics, in 4th International Confer-ence on Integrated Information, Madrid. doi: 10.13140/2.1.2341.5048. AIP Conference Proceedings, 2014, pp. 23415048.
[19] D. Laney, 3D Data Management: Controlling Data Volume, Velocity and Va-riety, META Group Research Note, vol. 6, 2001.
[20] M. van Rijmenam, Why The 3V's Are Not Sucient To Describe Big Data, https://dataoq.com/read/3vs-sucient-describe-big-data/166, August 2013, accessed: 2015.05.07.
[21] E. Kern, Facebook is Collecting Your Data - 500 Terabytes a Day, https://gigaom.com/2012/08/22/facebook-is-collecting-your-data-500-terabytes-a-day/, August, 22, 2012, accessed: 2015.05.15.
[22] YouTube, Statistics, https://www.youtube.com/yt/press/statistics.html, May, 08, 2015, accessed: 2015.05.15.
[23] YouTube Ocal Blog, Holy Nyans! 60 Hours per Minute and 4 Billion Views a Day on YouTube, http://youtube-global.blogspot.se/2012/01/holy-nyans-60-hours-per-minute-and-4.html, January, 23, 2012, accessed: 2015.05.15.
[24] J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. H.
Byers, and M. G. Institute, Big Data: The Next Frontier for Innovation, Com-petition, and Productivity, 2011.
[25] M. Chalmers, Large Hadron Collider: The Big Reboot, http://www.nature.
com/news/large-hadron-collider-the-big-reboot-1.16095, October, 08, 2014, ac-cessed: 2015.05.15.
[26] I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, The Rise of "Big Data" on Cloud Computing: Review and Open Research Issues, Information Systems, vol. 47, pp. 98115, 2015.
[27] C. Ji, Y. Li, W. Qiu, U. Awada, and K. Li, Big Data Processing in Cloud Computing Environments, in Proceedings of the International Symposium on Parallel Architectures, Algorithms and Networks, I-SPAN, 2012.
[28] P. Mell and T. Grance, The NIST Denition of Cloud Computing, National Institute of Standards and Technology, vol. 53, no. 6, p. 7, September 2011.
[29] A. Milenkoski, A. Iosup, S. Kounev, K. Sachs, P. Rygielski, J. Ding, W. Cirne, and F. Rosenberg, Cloud Usage Patterns: A Formalism for Description of Cloud Usage Scenarios, SPEC Research Group -Cloud Working Group, Standard Performance Evaluation Corporation (SPEC), Tech. Rep. SPEC-RG-2013-001 v.1.0.1, May 2013. [Online].
Available: http://research.spec.org/leadmin/user_upload/documents/rg_
cloud/endorsed_publications/SPEC-RG-2013-001_CloudUsagePatterns.pdf [30] S. Kächele, C. Spann, F. J. Hauck, and J. Domaschka, Beyond IaaS and PaaS:
An Extended Cloud Taxonomy for Computation, Storage and Networking, in Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing. IEEE Computer Society, 2013, pp. 7582.
[31] M. Armbrust, O. Fox, R. Grith, A. D. Joseph, Y. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica et al., Above the Clouds: a Berkeley View of Cloud Computing, Dept. Electrical Eng. and Comput. Sciences, University of California, Berkeley, Rep. UCB/EECS, vol. 28, p. 13, 2009.
[32] J. L. Hennessy and D. A. Patterson, Computer Architecture: A Quantitative Approach. Elsevier, 2012.
[33] D. Kliazovich, P. Bouvry, F. Granelli, and N. L. Fonseca, Energy Consump-tion OptimizaConsump-tion in Cloud Data Centers, Cloud Services, Networking and Management, 2014.
[34] Emerson Network Power, Energy Logic: Reducing Data Center Energy Con-sumption by Creating Savings that Cascade Across Systems, White paper, Emerson Electric Co, 2009.
[35] S. Pelley, D. Meisner, T. F. Wenisch, and J. W. VanGilder, Understanding and Abstracting Total Data Center Power, in Workshop on Energy-Ecient Design, 2009.
[36] The Natural Resources Defense Council (NRDC), Data Center E-ciency Assessment, https://www.nrdc.org/energy/les/data-center-eE-ciency- https://www.nrdc.org/energy/les/data-center-eciency-assessment-IP.pdf, August 2014, accessed: 2015.05.07.
[37] Gadgetzz, Facebooks Server Hall in Lulea Sweden, http://www.
yousaytoo.com/facebooks-server-hall-in-lulea-sweden/2102255, 2012, accessed:
2015.04.23.
[38] M. Mahdi, M. Raheleh, and S. I. Abdul, Grid and Cloud Computing Simulation Tools. International Journal of Networks and Communications, pp. 4552, 2013.
[39] S. Utkal and S. Mayank, Comparison of Various Cloud Simulation Tools Avail-able in Cloud Computing, International Journal of Advanced Research in Com-puter and Communication Engineering, vol. 4, pp. 171176, March 2015.
[40] R. Habibur and M. Adnan, A Survey of Cloud Simulation Tools, http://
www.slideshare.net/habibur01/survey-on-cloud-simulator, July 2013, accessed:
2015.04.16.
[41] A. Ahmed and A. S. Sabyasachi, Cloud Computing Simulators: A Detailed Survey and Future Direction, in IEEE International Advance Computing Con-ference (IACC). IEEE, 2014, pp. 866872.
[42] S. K. Garg and R. Buyya, Networkcloudsim: Modelling Parallel Applications in Cloud Simulations, in Fourth IEEE International Conference on Utility and Cloud Computing (UCC). IEEE, 2011, pp. 105113.
[43] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, and R. Buyya, CloudSim: a Toolkit for Modeling and Simulation of Cloud Computing En-vironments and Evaluation of Resource Provisioning Algorithms, Software:
Practice and Experience, vol. 41, no. 1, pp. 2350, 2011.
[44] S.-H. Lim, B. Sharma, G. Nam, E. K. Kim, and C. R. Das, MDCSim: A Multi-tier Data Center Simulation Platform, in IEEE International Conference on Cluster Computing and Workshops (CLUSTER'09). IEEE, 2009, pp. 19.
[45] D. Kliazovich, P. Bouvry, and S. U. Khan, GreenCloud: A Packet-Level Sim-ulator of Energy-Aware Cloud Computing Data Centers, The Journal of Su-percomputing, vol. 62, no. 3, pp. 12631283, 2012.
[46] J. C. McCallum, Disk Drive Prices (1955-2014), http://www.jcmit.com/
diskprice.htm, September, 14, 2014, accessed: 2015.05.06.
[47] Statistic Brain Research Institute, Average Cost of Hard Drive Storage, http:
//www.statisticbrain.com/average-cost-of-hard-drive-storage/, November, 11, 2014, accessed: 2015.05.06.
[48] MKOMO, A History of Storage Cost (update), http://www.mkomo.com/
cost-per-gigabyte-update, March, 9, 2014, accessed: 2015.05.06.
[49] G. D. I. S. R. Fontana and T. Group, Volumetric Density Trends (TB/in3) -Tape, HDD, NAND FLASH, Blu-Ray, http://www.digitalpreservation.gov/
meetings/documents/storage14/Fontana_Volumetric%20Density%20Trends%
20for%20Storage%20Components%20--%20LOC%2009222014.pdf, August, 22, 2014, library Of Congress meeting, Designing Storage Architectures for Digital Collections. Accessed: 2015.05.01.
[50] HGST Press room, World's First 10TB HDD with the Lowest $/TB and Watt/TB, http://www.hgst.com/press-room/press-releases/HGST-unveils-intelligent-dynamic-storage-solutions-to-transform-the-data-center, September, 9, 2014, accessed: 2015.05.06.
[51] S. Anthony, Seagate Starts Shipping 8TB Hard Drives, with 10TB and HAMR on the Horizon, http://www.extremetech.com/computing/186624-seagate-starts-shipping-8tb-hard-drives-with-10tb-and-hamr-on-the-horizon, July, 21, 2014, accessed: 2015.05.06.
[52] D. Riley, Surveillance Hard Drive Shoot-Out: WD And Seagate Square O, http://www.tomshardware.com/reviews/surveillance-hard-drive-performance,3831-5.html, June, 19, 2014, accessed: 2015.05.06.
[53] J. Clark, HDDs Versus SSDs in the Data Center, http://www.
datacenterjournal.com/hdds-versus-ssds-in-the-data-center/, November, 20, 2012, accessed: 2015.05.06.
[54] A. Jan Willem, SSD Prices Drop 20Cheapest Drive, http://www.myce.com/
news/ssd-prices-drop-20-compared-to-august-0-35-per-gb-for-cheapest-drive-73603/, November, 28, 2014, accessed: 2015.05.06.
[55] J. C. McCallum, Flash Memory Prices (2003-2014), http://www.jcmit.com/
ashprice.htm, September, 14, 2014, accessed: 2015.05.06.
[56] J. Brodkin, Why Facebook Thinks Blu-ray Discs Are Perfect for the Data Center, http://arstechnica.com/information-technology/2014/01/31/
why-facebook-thinks-blu-ray-discs-are-perfect-for-the-data-center/, January, 31, 2014, accessed: 2015.05.07.
[57] T. Trader, Blu-ray for Cold Storage, What About Tape? http://www.
hpcwire.com/2014/02/13/blu-ray-cold-storage-tape/, February, 13, 2014, ac-cessed: 2015.05.07.
[58] J. Niccolai, Facebook Puts 10,000 Blu-ray Discs in Low-Power Storage Sys-tem, http://www.computerworld.com/article/2487031/data-center/facebook-puts-10-000-blu-ray-discs-in-low-power-storage-system.html, January, 28, 2014.
[59] Eran, Saving Data Center Power by Reducing HDD
Spin Speed, http://www.opencompute.org/blog/saving-data-center-power-by-reducing-hdd-spin-speed/, Open Compute Project, August 2011, accessed:
2015.03.13. [Online]. Available: http://www.opencompute.org/blog/saving-data-center-power-by-reducing-hdd-spin-speed/
[60] Seagate, Breaking Capacity Barriers With Seagate Shingled Magnetic Record-ing, http://www.seagate.com/gb/en/tech-insights/breaking-areal-density-barriers-with-seagate-smr-master-ti/, May, 06, 2015, accessed: 2015.05.06.
[61] N. Nishikawa, M. Nakano, and M. Kitsuregawa, Energy Ecient Storage Man-agement Cooperated with Large Data Intensive Applications, in IEEE 28th International Conference on Data Engineering (ICDE). IEEE, 2012, pp. 126 137.
[62] J. Wan, C. Yin, J. Wang, and C. Xie, A New High-Performance, Energy-Ecient Replication Storage System with Reliability Guarantee, in IEEE 28th
Symposium on Mass Storage Systems and Technologies (MSST). IEEE, 2012, pp. 16.
[63] A. Taal, D. Drupsteen, M. X. Makkes, and P. Grosso, Storage to Energy: Mod-eling the Carbon Emission of Storage Task Ooading Between Data Centers, in IEEE 11th Consumer Communications and Networking Conference (CCNC).
IEEE, 2014, pp. 5055.
[64] J. Shuja, K. Bilal, S. A. Madani, M. Othman, R. Ranjan, P. Balaji, and S. U.
Khan, Survey of Techniques and Architectures for Designing Energy-Ecient Data Centers, IEEE Systems Journal, vol. PP, pp. pages 1 13, July 2014.
[65] A. G. Yoder, Energy Ecient Storage Technologies for Data Centers, in Work-shop on Energy-Ecient Design (WEED), 2010.
[66] Q. Zhu, Z. Chen, L. Tan, Y. Zhou, K. Keeton, and J. Wilkes, Hibernator:
Helping Disk Arrays Sleep Through the Winter, in ACM SIGOPS Operating Systems Review, vol. 39. ACM, 2005, pp. 177190.
[67] X. Bai, M. Li, B. Chen, W.-T. Tsai, and J. Gao, Cloud Testing Tools, in IEEE 6th International Symposium on Service Oriented System Engineering (SOSE).
IEEE, 2011, pp. 112.
[68] W. Zhao, Y. Peng, F. Xie, and Z. Dai, Modeling and Simulation of Cloud Computing: A Review, in IEEE Asia Pacic Cloud Computing Congress (AP-CloudCC). IEEE, 2012, pp. 2024.
[69] A. Oujani and R. Jain, A Survey on Cloud Computing Simulations and Cloud Testing, http://students.cec.wustl.edu/~azinoujani/, April 2013, ac-cessed: 2015.04.28.
[70] T. Issariyakul and E. Hossain, Introduction to Network Simulator NS2.
Springer Science & Business Media, 2011.
[71] A. Medina, A. Lakhina, I. Matta, and J. Byers, BRITE: An Approach to Uni-versal Topology Generation, in Modeling, Analysis and Simulation of Com-puter and Telecommunication Systems, 2001. Proceedings. 9th International Symposium on Modeling, Analysis and Simulation of Computer and Telecom-munication Systems (MASCOTS 2001). IEEE, August 2001, pp. 346353.
[72] A. Beloglazov and R. Buyya, Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Ecient Dynamic Consolida-tion of Virtual Machines in Cloud Data Centers, Concurrency and Computa-tion: Practice and Experience, vol. 24, no. 13, pp. 13971420, 2012.
[73] K.-D. Lange, Identifying Shades of Green: The SPECpower Benchmarks.
IEEE Computer, vol. 42, no. 3, pp. 9597, 2009.
[74] B. Wickremasinghe, R. N. Calheiros, and R. Buyya, Cloudanalyst: A Cloudsim-Based Visual Modeller for Analysing Cloud Computing Environ-ments and Applications, in 24th IEEE International Conference on Advanced Information Networking and Applications (AINA). IEEE, 2010, pp. 446452.
[75] Cloud Computing and Distributed Systems (CLOUDS) Laboratory, CloudSim:
A Framework for Modeling and Simulation of Cloud Computing Infras-tructures and Services, http://www.cloudbus.org/cloudsim/, 2013, accessed:
2015.04.30.
[76] R. Buyya and M. Murshed, Gridsim: A Toolkit for the Modeling and Simula-tion of Distributed Resource Management and Scheduling for Grid Computing, Concurrency and computation: practice and experience, vol. 14, no. 13-15, pp.
11751220, 2002.
[77] R. Calheiros, B. Louis, and tanya33k, Issue 24: HarddriveStorage Con-fused, in CloudSim v3.0.3, https://code.google.com/p/cloudsim/issues/detail?
id=24, February 2012, accessed: 2015.04.08.
[78] R. Calheiros, B. Louis, A. Ashly, and Tanya, HarddriveStorage Con-fused, in CloudSim v3.0.3, https://groups.google.com/d/topic/cloudsim/
BkVfMwGhFLM/discussion, February 2012, accessed: 2015.04.03.
[79] R. Calheiros, Class SanStorage, http://www.cloudbus.org/cloudsim/doc/
api/org/cloudbus/cloudsim/SanStorage.html, April 2009, accessed: 2015.04.08.
[80] T. Sturm, StorageCloudSim Repository Source Code, https://github.com/
toebbel/StorageCloudSim, February 2015, accessed: 2015.04.30.
[81] N. Grozev and M. Alrokayan, CloudSimEx Repository Source Code, https:
//github.com/Cloudslab/CloudSimEx, October 2014, accessed: 2015.04.30.
[82] S. Long and Y. Zhao, A Toolkit for Modeling and Simulating Cloud Data Storage: An Extension to Cloudsim, in International Conference on Control Engineering and Communication Technology (ICCECT). IEEE, 2012, pp.
597600.
[83] T. Sturm, Implementation of a Simulation Environment for Cloud Object Storage Infrastructures, http://downloads.tobiassturm.de/projects/
storagecloudsim/thesis.pdf, 2013 August, accessed: 2015.04.08.
[84] Nikolayg, Alrokayan, and Mohammed, CloudSim Project, https://github.
com/Cloudslab/CloudSimEx, October 2012, accessed: 2015.04.10.
[85] N. Grozev and R. Buyya, Performance Modelling and Simulation of Three-tier Applications in Cloud and Multi-cloud Environments, The Computer Journal, vol. 58, no. 1, pp. 122, 2015.
[86] N. Grozev, CloudSim and CloudSimEx [Part 2] - Disk Operations, https://nikolaygrozev.wordpress.com/2014/06/13/cloudsim-and-cloudsimex-part-2-disk-opeartions/, June 2014, accessed: 2015.04.30.
[87] PC Tech Guide, Hard Disk (Hard Drive) Performance - Transfer Rates, Latency, and Seek Times, http://www.pctechguide.com/hard-disks/hard-disk-hard-drive-performance-transfer-rates-latency-and-seek-times, November 2011, accessed: 2015.04.10.
[88] W. Rachele, What Are Characteristics of a Hard Drive? http://www.ehow.
com/list_6684495_characteristics-hard-drive_.html, March 2012, accessed:
2015.04.10.
[89] Red Hat Enterprise Linux, 5.4. Hard Drive Performance Characteristics, https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_
Linux/4/html/Introduction_To_System_Administration/s1-storage-perf.html, accessed: 2015.04.10.
[90] The PC Guide, Internal Data Transfer Rate, http://www.pcguide.com/ref/
hdd/perf/intRate-c.html, April 2001, accessed: 2015.04.10.
[91] D. J. Lingenfelter, A. Khurshudov, and D. M. Vlassarev, Ecient Disk Drive Performance Model for Realistic Workloads, IEEE Transactions on Magnetics, vol. 50, no. 5, pp. 19, 2014.
[92] P. Jan, Average Seek Time of a Computing, http://faculty.plattsburgh.edu/
jan.plaza/teaching/papers/seektime.html, 1999, accessed: 2015.04.20.
[93] G. Urdaneta, G. Pierre, and M. van Steen, Wikipedia Workload Analysis for Decentralized Hosting, Elsevier Computer Networks, vol. 53, no. 11, pp. 1830 1845, July 2009, http://www.globule.org/publi/WWADH_comnet2009.html.
[94] C. Kozierok, Zoned Bit Recording, http://www.pcguide.com/ref/hdd/geom/
tracks_ZBR.htm, April, 17, 2001, accessed: 2015.05.13.
CloudSimDisk has been developed on Eclipse Luna SR2 (4.4.2), Windows 64 Bit.
The entire source code is available on GitHub at https://github.com/Udacity2048/
CloudSimDisk (see Figure A1.1).
Figure A1.1. CloudSimDisk home page on GitHub.
CloudSimDisk JavaDoc is provided in the docs folder. The required jar les are gath-ered in the jars folder. The files folder contains necessary les for CloudSimDisk simulation examples (request arrival times, data le names and sizes, Wikipedia trace, etc).
Future improvements of CloudSimDisk will be uploaded on this repository. Also, this github will contain discussion threads about potential issues, general questions and new ideas, and a wiki page providing detail information on the most raised questions.
(continues)
The CloudSim core simulation engine is provided with CloudSimDisk (see Figure A1.2) because several Java Access Modiers had to be changed from "Private" to
"Protected" in order to allow class extensions for CloudSimDisk.
Figure A1.2. CloudSim core simulation engine on CloudSimDisk GitHub repository.
As a start, a new user should run MyExample0. This example aims to understand the basic execution of CloudSimDisk simulations. The scenario consists of 1 request, sent to the data center at 0.5 second. The request contains 1 FileA of 1 MB that need to be stored. The persistent storage is composed of 1 HDD (Seagate Enterprise 6TB Ref:ST6000VN0001). No les are retrieved. No le needed in the storage system before the simulation start.
The example can be nd in cloudsimdisk.examples package. The expected output of the simulation is depicted in Figure A2.1. Note that the time at which the le is added (0.527915 second) can be dierent for the reason that the transaction time (0.027915 second) depends on the randomness of the seek time and the rotation latency. Hence, the energy consumed (0.315 Joule) can vary too.
Figure A2.1. CloudSimDisk console output for "MyExample0".
This appendix section presents the source code of an HDD model implemented in CloudSimDisk (see Figure A3.1). Each HDD model is following the same pattern.
The brand and the reference of the modeled disk are included in the name of the class. Thus, the user knows which disk is modeled by this class.
The core of the class is formed by a "switch-case" statement where each case cor-responds to one disk characteristic. Later, the user can add new characteristics by adding new cases.
Figure A3.1. CloudSimDisk HDD model of the Seagate Enterprise NAS 6TB (Ref:
ST6000VN0001).
Then, the abstract class StorageModelHdd implements one method for each HDD characteristic (see Figure A3.2). The name of these methods are semantically un-derstandable for the end user.
(continues)
Figure A3.2. The abstract class extended by all Hard Disk Drive models.
This appendix section presents the source code of an HDD power model implemented in CloudSimDisk (see Figure A4.1). Each HDD power model is following the same pattern. The brand and the reference of the modeled disk are included in the name of the class. Thus, the user knows which disk is modeled by this class.
The core of the class is formed by a "switch-case" statement where each case cor-responds to the power consumption in one specic operating mode. Later, the user can add new power data by adding new cases.
Figure A4.1. CloudSimDisk HDD power model of the HGST Ultrastar 900GB (Ref:
HUC109090CSS600).
Then, the abstract class PowerModelHdd implements one method for each power mode (see Figure A4.2). The name of these methods are semantically understand-able for the end user.
(continues)
Figure A4.2. The abstract class extended by all Hard Disk Drive power models.