IDEAS home Printed from https://ideas.repec.org/a/rsr/journl/v65y2017i1p3-18.html
   My bibliography  Save this article

Expert System and Heuristics Algorithm for Cloud Resource Scheduling

Author

Listed:
  • Mamatha E.

    (Dept of Engineering Mathematics, GITAM University, Bangalore, India)

  • Sasritha S.

    (Dept of Engineering Mathematics, GITAM University, Bangalore, India)

  • CS Reddy

    (School of Computing, SASTRA University, Thanjavur, India)

Abstract

Rule-based scheduling algorithms have been widely used on cloud computing systems and there is still plenty of room to improve their performance. This paper proposes to develop an expert system to allocate resources in cloud by using Rule based Algorithm, thereby measuring the performance of the system by letting the system adapt new rules based on the feedback. Here performance of the action helps to make better allocation of the resources to improve quality of services, scalability and flexibility. The performance measure is based on how the allocation of the resources is dynamically optimized and how the resources are utilized properly. It aims to maximize the utilization of the resources. The data and resource are given to the algorithm which allocates the data to resources and an output is obtained based on the action occurred. Once the action is completed, the performance of every action is measured that contains how the resources are allocated and how efficiently it worked. In addition to performance, resource allocation in cloud environment is also considered.

Suggested Citation

  • Mamatha E. & Sasritha S. & CS Reddy, 2017. "Expert System and Heuristics Algorithm for Cloud Resource Scheduling," Romanian Statistical Review, Romanian Statistical Review, vol. 65(1), pages 3-18, March.
    • Handle: RePEc:rsr:journl:v:65:y:2017:i:1:p:3-18
    as

    Download full text from publisher

    File URL: http://www.revistadestatistica.ro/wp-content/uploads/2017/03/A1_RRS1_2017.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    2. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fátima Pilar & Eliana Costa e Silva & Ana Borges, 2023. "Optimizing Vehicle Repairs Scheduling Using Mixed Integer Linear Programming: A Case Study in the Portuguese Automobile Sector," Mathematics, MDPI, vol. 11(11), pages 1-23, June.
    2. Sheikh, Shaya & Komaki, G.M. & Kayvanfar, Vahid & Teymourian, Ehsan, 2019. "Multi-Stage assembly flow shop with setup time and release time," Operations Research Perspectives, Elsevier, vol. 6(C).
    3. Liou, Cheng-Dar & Hsieh, Yi-Chih, 2015. "A hybrid algorithm for the multi-stage flow shop group scheduling with sequence-dependent setup and transportation times," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 258-267.
    4. Pan, Quan-Ke & Ruiz, Rubén, 2012. "An estimation of distribution algorithm for lot-streaming flow shop problems with setup times," Omega, Elsevier, vol. 40(2), pages 166-180, April.
    5. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    6. Rossi, Andrea, 2014. "Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships," International Journal of Production Economics, Elsevier, vol. 153(C), pages 253-267.
    7. Abdolreza Rasouli Kenari & Mahboubeh Shamsi, 2021. "A hyper-heuristic selector algorithm for cloud computing scheduling based on workflow features," OPSEARCH, Springer;Operational Research Society of India, vol. 58(4), pages 852-868, December.
    8. Shen, Liji & Dauzère-Pérès, Stéphane & Neufeld, Janis S., 2018. "Solving the flexible job shop scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 265(2), pages 503-516.
    9. Javad Rezaeian & Reza Alizadeh Foroutan & Toraj Mojibi & Yacob Khojasteh, 2023. "Sensitivity Analysis of the Unrelated Parallel Machine Scheduling Problem with Rework Processes and Machine Eligibility Restrictions," SN Operations Research Forum, Springer, vol. 4(3), pages 1-24, September.
    10. Marko Ɖurasević & Domagoj Jakobović, 2019. "Creating dispatching rules by simple ensemble combination," Journal of Heuristics, Springer, vol. 25(6), pages 959-1013, December.
    11. Mehravaran, Yasaman & Logendran, Rasaratnam, 2012. "Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times," International Journal of Production Economics, Elsevier, vol. 135(2), pages 953-963.
    12. Dongni Li & Xianwen Meng & Miao Li & Yunna Tian, 2016. "An ACO-based intercell scheduling approach for job shop cells with multiple single processing machines and one batch processing machine," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 283-296, April.
    13. Zhengcai Cao & Lijie Zhou & Biao Hu & Chengran Lin, 2019. "An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 299-309, June.
    14. Bozorgirad, Mir Abbas & Logendran, Rasaratnam, 2013. "Bi-criteria group scheduling in hybrid flowshops," International Journal of Production Economics, Elsevier, vol. 145(2), pages 599-612.
    15. Shen, Liji & Buscher, Udo, 2012. "Solving the serial batching problem in job shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 14-26.
    16. Omid Shahvari & Rasaratnam Logendran & Madjid Tavana, 2022. "An efficient model-based branch-and-price algorithm for unrelated-parallel machine batching and scheduling problems," Journal of Scheduling, Springer, vol. 25(5), pages 589-621, October.
    17. Wang, Ling & Sun, Lin-Yan & Sun, Lin-Hui & Wang, Ji-Bo, 2010. "On three-machine flow shop scheduling with deteriorating jobs," International Journal of Production Economics, Elsevier, vol. 125(1), pages 185-189, May.
    18. Gupta, Jatinder N.D. & Koulamas, Christos & Kyparisis, George J., 2006. "Performance guarantees for flowshop heuristics to minimize makespan," European Journal of Operational Research, Elsevier, vol. 169(3), pages 865-872, March.
    19. Ganesan, Viswanath Kumar & Sivakumar, Appa Iyer, 2006. "Scheduling in static jobshops for minimizing mean flowtime subject to minimum total deviation of job completion times," International Journal of Production Economics, Elsevier, vol. 103(2), pages 633-647, October.
    20. A. Dolgui & M. Kovalyov & K. Shchamialiova, 2011. "Multi-product lot-sizing and sequencing on a single imperfect machine," Computational Optimization and Applications, Springer, vol. 50(3), pages 465-482, December.

    More about this item

    Keywords

    Cloud computing; Scheduling and Expert System; Heuristic Models;
    All these keywords.

    JEL classification:

    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rsr:journl:v:65:y:2017:i:1:p:3-18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Adrian Visoiu (email available below). General contact details of provider: https://edirc.repec.org/data/stagvro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.