Swarm-Inspired Task Scheduling Strategy in Cloud Computing

  • Ramakrishna Goddu
  • Kiran Kumar Reddi
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


Cloud computing is the most emerging technology which provides sharing of computing resources and data storage through virtualization concept. However, managing plenty of virtualized resources made scheduling a difficult task in cloud computing. Task scheduling must be done in such a way that it must satisfy customer requirements and maintain the quality of service (QoS). In this paper, we proposed a method for resource allocation based on particle swarm optimization (PSO) algorithm and with two objectives which produce optimal task scheduling. The first objective is related to virtual machine processing, and the second objective is related to the time elapsed to complete the given task. Based on the throughput of these objectives, the virtual machines are allotted to the resources.


Cloud computing Task scheduling Particle swarm optimization Virtual machines 


  1. 1.
    Tilak S, Patil D (2012) A survey of various scheduling algorithms in cloud environment. Int J Eng Invent 1(2):36–39Google Scholar
  2. 2.
    Buyya R, Vecchiola C, Selvi ST (2013) Mastering cloud computing: foundations and applications programming. NewnesGoogle Scholar
  3. 3.
    Alkayal ES, Jennings NR, Abulkhair MF (2016) Efficient task scheduling multi-objective particle swarm optimization in cloud computing. In: 2016 IEEE 41st conference on local computer networks workshops (LCN workshops). IEEE, pp 17–24Google Scholar
  4. 4.
    Vinothina V, Sridaran R, Ganapathi P (2012) A survey on resource allocation strategies in cloud computing. Int J Adv Comput Sci Appl 3(6):97–104Google Scholar
  5. 5.
    Juan W, Fei L, Aidong C (2012) An improved PSO based task scheduling algorithm for cloud storage system. Adv Inf Sci Serv Sci 4(18):465–471Google Scholar
  6. 6.
    Ardagna D, Casale G, Ciavotta M, Pérez JF, Wang W (2014) Quality-of-service in cloud computing: modeling techniques and their applications. J Internet Serv Appl 5(1):11CrossRefGoogle Scholar
  7. 7.
    Wu X, Deng M, Zhang R, Zeng B, Zhou S (2013) A task scheduling algorithm based on QoS-driven in cloud computing. Proc Comput Sci 17:1162–1169CrossRefGoogle Scholar
  8. 8.
    Kassarwani N, Ohri J, Singh A (2019) Performance analysis of dynamic voltage restorer using improved PSO technique. Int J Electron 106(2):212–236CrossRefGoogle Scholar
  9. 9.
    Arumugam P, Panchapakesan M, Balraj S, Subramanian RC (2019) Reverse search strategy based optimization technique to economic dispatch problems with multiple fuels. J Electr Eng Technol 1–7Google Scholar
  10. 10.
    Ghosh P, Karmakar A, Sharma J, Phadikar S (2019) CS-PSO based intrusion detection system in cloud environment. In: Emerging technologies in data mining and information security. Springer, Singapore, pp. 261–269Google Scholar
  11. 11.
    Krishnasamy K (2013) Task scheduling algorithm based on hybrid particle swarm optimization in cloud computing environment. J Theoret Appl Inf Technol 55(1)Google Scholar
  12. 12.
    Dordaie N, Navimipour NJ (2017) A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments. ICT ExpressGoogle Scholar
  13. 13.
    Deepak BBVL, Parhi DR, Raju BMVA (2014) Advance particle swarm optimization-based navigational controller for mobile robot. Arab J Sci Eng 39(8):6477–6487CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ramakrishna Goddu
    • 1
  • Kiran Kumar Reddi
    • 1
  1. 1.Department of Computer ScienceKrishna UniversityMachilipatanamIndia

Personalised recommendations