Cluster Computing

, Volume 16, Issue 4, pp 625–638 | Cite as

ORTHRUS: a lightweighted block-level cloud storage system

  • Jian Wan
  • Jianliang Zhang
  • Li Zhou
  • Yicheng Wang
  • Congfeng Jiang
  • Yongjian Ren
  • Jue Wang


Taking advantage of distributed storage technology and virtualization technology, cloud storage systems provide virtual machine clients customizable storage service. They can be divided into two types: distributed file system and block level storage system. There are two disadvantages in existing block level storage system: Firstly, Some of them are tightly coupled with their cloud computing environments. As a result, it’s hard to extend them to support other cloud computing platforms; Secondly, The bottleneck of volume server seriously affects the performance and reliability of the whole system. In this paper we present a lightweighted block-level storage system for clouds—ORTHRUS, based on virtualization technology. We first design the architecture with multiple volume servers and its workflows, which can improve system performance and avoid the problem. Secondly, we propose a Listen-Detect-Switch mechanism for ORTHRUS to deal with contingent volume servers’ failure. At last we design a strategy that dynamically balances load between multiple volume servers. We characterize machine capability and load quantity with black box model, and implement the dynamic load balance strategy which is based on genetic algorithm. Extensive experimental results show that the aggregated I/O throughputs of ORTHRUS are significantly improved (approximately two times of that in Orthrus), and both I/O throughputs and IOPS are also remarkably improved (about 1.8 and 1.2 times, respectively) by our dynamic load balance strategy.


Cloud storage Virtual block store Logical volume Load balance Genetic algorithm 



This paper is supported by State Key Development Program of Basic Research of China under grant No. 2007CB310900, the Hi-Tech Research and Development Program (863) of China under Grant. 2011AA01A205, Natural Science Fund of China under grant Nos. 61202094, 61003077, 60873023, 60973029, The science and technology major project of Zhejiang Province (Grant No. 2011C11038), Zhejiang Provincial Natural Science Foundation under grant Nos. Y1101104, Y1101092, Y1090940. Zhejiang Provincial Education Department Scientific Research Project (No. Y201016492). We also thank the developer of VBS–Xiaoming Gao, who gave us much help in our work.


  1. 1.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010) CrossRefGoogle Scholar
  2. 2.
    Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 164–177. ACM Press, New York (2003) CrossRefGoogle Scholar
  3. 3.
    Kivity, A., Kamay, Y., Laor, D., Lublin, U., Liguori, A.: kvm: the Linux virtual machine monitor. In: Proceedings of Ottawa Linux Symposium, pp. 225–230. Linux Symposium, Ottawa (2007) Google Scholar
  4. 4.
  5. 5.
    Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., Zagorodnov, D.: The eucalyptus open-source cloud-computing system. In: CCGRID, Shanghai, China (2009) Google Scholar
  6. 6.
  7. 7.
    Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: MSST, Incline Village. IEEE Press, New York (2010) Google Scholar
  8. 8.
    Amazon EBS service:
  9. 9.
    Gao, X., Lowe, M., Ma, Y., Pierce, M.: Supporting cloud computing with the virtual block store system. In: Proceedings of e-Science, Oxford, UK, pp. 208–215. IEEE Press, New York (2009) Google Scholar
  10. 10.
    Gao, X., Ma, Y., Pierce, M., Lowe, M., Fox, G.: Building a distributed block storage system for cloud infrastructure. In: Proceedings of IEEE Second International Conference on Cloud Computing Technology and Science, Indianapolis, USA, pp. 312–318 (2010) Google Scholar
  11. 11.
    Amazon EC2 service:
  12. 12.
    Schwan, P.: Lustre: building a file system for 1,000-node clusters. In: Proceedings of Ottawa Linux Symposium, pp. 380–386. Linux Symposium, Ottawa (2003) Google Scholar
  13. 13.
  14. 14.
    Teigland, D., Mauelshagen, H.: Volume managers in Linux. In: Proceedings of the 2001 USENIX Annual Technical Conference, Boston, USA, pp. 185–198 (2001) Google Scholar
  15. 15.
    iSCSI Enterprise Target:
  16. 16.
  17. 17.
  18. 18.
  19. 19.
    Fraser, A.S.: Simulation of genetic systems by automatic digital computers. I. Introduction. Aust. J. Biol. Sci. 10, 484–491 (1957) Google Scholar
  20. 20.
    Gulati, A., Kumar, C., Ahmad, I. Kumar, K.: BASIL: automated IO load balancing across storage devices. In: Proceedings of the 8th USENIX Conference on File and Storage Technologies (FAST2010), p. 13. USENIX Association, Berkeley (2010) Google Scholar
  21. 21.
  22. 22.
  23. 23.
    Abu-Libdeh, H., Princehouse, L., Weatherspoon, H.: Racs: a case for cloud storage diversity. In: Proceedings of the 1st ACM Symposium on Cloud Computing, SoCC 2010, pp. 229–240. ACM Press, New York (2010) Google Scholar
  24. 24.
    Zeng, W., Zhao, Y., Ou, K., Song, W.: Research on cloud storage architecture and key technologies. In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, pp. 1044–1048. ACM Press, New York (2009) CrossRefGoogle Scholar
  25. 25.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989) MATHGoogle Scholar
  26. 26.
    Bowers, K.D., Juels, A., Oprea, A.: Hail: a high-availability and integrity layer for cloud storage. Cryptology ePrint archive, report 2008/489, (2008) Google Scholar
  27. 27.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: Above the clouds: A Berkeley view of cloud computing. Technical Rep UCB/EECS-2009-28, University of California at Berkley, USA (2009) Google Scholar
  28. 28.
    Shirazi, B.A., Hurson, A.R., Kavi, K.M.: Scheduling and load balancing. In: Parallel and Distributed Systems. IEEE Computer Society Press, Los Alamitos (1995) Google Scholar
  29. 29.
    Subrata, R., Zomaya, A.Y., Landfeldt, B.: Game theoretic approach for load balancing in computational grids. IEEE Trans. Parallel Distrib. Syst. 19(1) (2008) Google Scholar
  30. 30.
    Sharma, S., Singh, S., Sharma, M.: Performance analysis of load balancing algorithms. World Acad. Sci., Eng. Technol. 38, 269–272 (2008) Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Jian Wan
    • 1
  • Jianliang Zhang
    • 1
  • Li Zhou
    • 1
  • Yicheng Wang
    • 1
  • Congfeng Jiang
    • 1
  • Yongjian Ren
    • 1
  • Jue Wang
    • 2
  1. 1.School of Computer Science and TechnologyHangzhou Dianzi UniversityZhejiangChina
  2. 2.Supercomputing Center of Computer Network Information CenterChinese Academy of SciencesBeijingChina

Personalised recommendations