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Resource storage management model for ensuring quality of service in the cloud archive systems

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Nowadays, service providers offer a lot of IT services in the public or private cloud. Clients can buy various kinds of services, such as SaaS, PaaS, etc. Recently, Backup as a Service (BaaS), a variety of SaaS, was introduced there. At the moment, there are several different BaaS’s available to archive data in the cloud, but they provide only a basic level of service quality. In this paper, we propose a model which ensures QoS for BaaS and some methods for management of storage resources aimed at achieving the required SLA.This model introduces a set of parameters responsible for an SLA level which can be offered at the basic or higher level of quality. The storage systems (typically HSM), which are distributed between several Data Centers, are built based on disk arrays, VTL’s, and tape libraries. The RSMM model does not assume bandwidth reservation or control, but rather is focused on management of storage resources.
Słowa kluczowe
EN
storage   backup   cloud   management   QoS   SLA  
Wydawca
Czasopismo
Rocznik
Strony
3--18
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • AGH University of Science and Technology, Krakow, Poland
autor
  • AGH University of Science and Technology, Krakow, Poland
autor
  • AGH University of Science and Technology, Krakow, Poland
Bibliografia
  • [1] Słota R., Nikolow D., Polak S., Kuta M., Kapanowski M., Skałkowski K., Pogoda M., Kitowski J.: Prediction and Load Balancing System for Distributed Storage. Scalable Computing Practice and Experience, Special Issue: Grid and Cloud Computing and their Application 11(2): 121–130, 2010, ISSN 1895-1767.
  • [2] Słota R., Nikolow D., Kuta M., Kapanowski M., Skałkowski K., Pogoda M., Kitowski J.: Replica Management for National Data Storage, In: R. Wyrzykowski, J. Dongarra, K. Karczewski, J. Wasniewski (Eds.), Proceedings of Parallel Processing and Applied Mathematics – PPAM 2009, 8th International Conference, Wroclaw, Poland, September 2009, LNCS 6068, vol. II, Springer 2010, pp. 184–193.
  • [3] Hey T., Trefethen A. E.: Cyberinfrastructure for e-Science. Science 308(5723): 817–821, 2005
  • [4] Hey T., Tansly S., Tolle K. (Eds.): The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, October 2009.
  • [5] Mell P., Grance T.: Effectively and securely using the cloud computing paradigm. National Institute of Standards and Technology. October 7, 2009.
  • [6] Kryza B., Król D., Wrzeszcz M., Dutka L., Kitowski J.: Interactive Cloud Data Farming Environment For Military Mission Planning Support, Computer Science 13(3): 89–100, 2012.
  • [7] Brandstein A., Horne G.: Data farming: A meta-technique for research in the 21st century. In: Maneuver Warfare Science 1998. Marine Corps Combat Development Command Publication, 1998.
  • [8] Choo C. S., Ng E. C., Ang C. K., Chua C. L.: Systematic data farming – an application to a military scenario. In: Proc. of Army Science Conference, 2006.
  • [9] Forsyth A., Horne G., Upton S.: Marine corps applicatons of data farming. In Kuhl M. E., Steiger N. M., Armstrong F. B., Joines J. A. (Eds.), Proc. of the 2005 Winter Simulation Conference, pp. 1077–1081, 2005.
  • [10] Horne G., Meyer T.: Data farming: Discovering surprise. In Ingalls R. G., Rossetti M. D., Smith J. S., Peters B. A. (Eds.), Proceedings of the 2004 Winter Simulation Conference, pp. 1082–1087, 2004.
  • [11] Słota R., Nikolow D., Skałkowski K., Kitowski J.: Management of Data Access with Quality of Service in PL-GRID Environment. Computing and 31(2): 463–479, 2012.
  • [12] Nikolow D., Słota R., Lakovic D., Winiarczyk P., Pogoda M., Kitowski J.: Management methods in slaaware distributed storage systems, Computer Science 13(3): 35–44, 2012.
  • [13] Mozy,http://mozy.com (last access 30th of June 2013)
  • [14] iBard, http://www.ibard24.com/products/ibard24-backup-online(last access 30th of June 2013)
  • [15] Msejf, http://www.msejf.pl (last access 30th of April 2013)
  • [16] Asigra, http://www.asigra.com (last access 30th of June 2013)
  • [17] Sejf Danych http://www.sejfdanych.pl (last access 30th of June 2013)
  • [18] National Data Storage project, http://nds.psnc.pl(last access 10 2013).
  • [19] Nikolow D., S lota R., Polak S., Mitera D., Pogoda M., Winiarczyk P., Kitowski J.: Model of QoS Management in a Distributed Data Sharing and Archiving System. International Conference on Computational Science, ICCS 2013, Procedia Computer Science, vol. 18, 2013, pp. 100–109.
  • [20] Słota R., Król D., Skałkowski K., Orzechowski M., Nikolow D., Kryza B., Wrzeszcz M., Kitowski M.: A Toolkit for Storage QoS Provisioning for Data-Intensive Applications, Computer Science, 13(1): 63–73, 2013.
  • [21] Funika W., Szura F.: Data Storage Management Using AI Methods, Computer Science, 14(2): 177–190, 2013
  • [22] Król D., Funika W., Słota R., Kitowski J.: SLA-Oriented Semi-Automatic Management of Data Storage and Applications in Distributed Environments, Computer Science, 11: 37–50, 2010.
  • [23] Nikolow D., Słota R., Kitowski J.: Grid Services for HSM Systems Monitoring, In: R. Wyrzykowski, J. Dongarra, K. Karczewski, J. Wasniewski (Eds.), Proc. of 7-th International Conference, PPAM 2007, Gdansk, Poland, September 2007, LNCS 4967, Springer 2008, pp. 321–330.
  • [24] Dutka L., Kitowski J.: Stochastic Approach for Secondary Storage Data Access Cost, In: P. M. A. Sloot, A. G. Hoekstra, T. Priol, A. Reinefeld, M. Bubak (Eds.), Proc. of Advances in Grid Computing – EGC 2005 European Grid Conference, Amsterdam, The Netherlands, February 14–16, 2005, Lecture Notes in Computer Science, no. 3470, Springer, 2005, pp. 796–804.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-96a82a8f-071b-4821-97ca-448a714e2145
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