Ontology-Based Approach to Scheduling of Jobs Processed by Applications Running in Virtual Environments

  • Maksim Khegai
  • Dmitrii Zubok
  • Alexandr Maiatin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 518)


This paper presents an ontology-based approach to the problem of jobs scheduling in case where jobs are processed by applications running in virtual environments and number of applications and their performance varies over time. Using ontology-based framework brings benefits when system has a varying number of components and their performing properties are also non-constant. The work is focused on ontology model needed to organize information exchange for intelligent agents embedded into virtual machines and gathering information about applications performance. In cases when jobs of one type can be processed by several applications having different performance, the existence of optimal threshold queuing policy has been proven earlier. It can reduce the average job processing time. In order to calculate thresholds we need relevant information about active applications and their current performance, the rate of jobs stream, the number of jobs in the queues, etc. The presented approach solves the problem of effective gathering of relevant information about the system state based on intelligent agents interaction where each intelligent agent uses ontology to publish only information about changes that are relevant to decision making. This reduces the system’s overhead for monitoring of ongoing parameters.


Ontology Scheduling Performance Virtualization Intelligent agents 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abdullah, M., Othman, M.: Cost-Based Multi-QoS job scheduling using divisible load theory in cloud computing. In: Dell’Olmo, P., Pesenti, R., Speranza, M.G. (eds.) Computers & Operations Research, vol. 34, pp. 928–935. Elsevier (2007)Google Scholar
  2. 2.
    Arzuaga, E., Kaeli, D.R.: Quantifying load imbalance on virtualized enterprise servers. In: Proceedings of the First Joint WOSP/SIPEW International Conference on Performance Engineering, pp. 235–242 (2010)Google Scholar
  3. 3.
    Saraswathia, A.T., Kalaashrib, Y.R.A., Padmavathi, S.: Dynamic resource allocation scheme in cloud computing. In: Procedia Computer Science, vol. 47, pp. 30–36. Elsevier (2015)Google Scholar
  4. 4.
    Funika, W., Janczykowski, M., Jopek, K., Grzegorczyk, M.: An ontology-based approach to performance monitoring of MUSCULE-bound multi-scale applications. In: Procedia Computer Science, vol. 18, pp. 1126–1135. Elsevier (2013)Google Scholar
  5. 5.
    Hu, W., Hicks, A., Zhang, L., Dow, E.M., Soni, V., Jiang, H., Bull, R., Matthews, J.N.: A quantitative study of virtual machine live migration. In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference (2013)Google Scholar
  6. 6.
    Jina, H., Linga, X., Ibrahimb, S., Caoa, W., Wua, S., Antoniub, G.: Flubber: Two-level disk scheduling in virtualized environment. In: Future Generation Computer Systems, vol. 29, pp. 2222–2238. Elsevier (2013)Google Scholar
  7. 7.
    Mivule, K., Turner, C.: Applying moving average filtering for non-interactive differential privacy settings. In: Procedia Computer Science, vol. 36, pp. 409–415. Elsevier (2014)Google Scholar
  8. 8.
    Rykov, V., Efrosinin, D.: Numerical analysis of optimal control policies for queueing systems with heterogeneous servers (2002)Google Scholar
  9. 9.
    Tanga, R., Yuea, Y., Dinga, X., Qiua, Y.: Credibility-based cloud media resource allocation algorithm. Journal of Network and Computer Applications 46, 315–321 (2014)Google Scholar
  10. 10.
    Tangmunarunkit, H., Decker, S., Kesselman, C.: Ontology-Based resource matching in the grid – the grid meets the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 706–721. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  11. 11.
    Yoo, H., Hur, C., Kim, S., Kim, Y.: An ontology-based resource selection service on science cloud. In: Slezak, D., Kim, T., Yau, S.S., Gervasi, O., Kang, B.-H. (eds.) GDC 2009. CCIS, vol. 63, pp. 221–228. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  12. 12.
    Zhang, Z.G., Love, E., Song, Y.: The optimal service time allocation of a versatile server to queue jobs and stochastically available non-queue jobs of different typess. In: Dell’Olmo, P., Pesenti, R., Speranza, M.G. (eds.) Procedia Computer Science, vol. 18, pp. 1857–1870 (2013)Google Scholar
  13. 13.
    Zubok, D., Maiatin, A., Kiryushkina, V., Khegai, M.: Functional model of a software system with random time horizon. In: 2015 17TH Conference of Open Innovations Association (FRUCT), pp. 259–266 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Maksim Khegai
    • 1
  • Dmitrii Zubok
    • 1
  • Alexandr Maiatin
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussia

Personalised recommendations