A Group Tasks Scheduling Algorithm for Cloud Computing Networks based on QoS

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    This article introduces a Novel Group-Tasks Scheduling Algorithm (NGTSA) which is used for allocating the tasks in the network of cloud computing by means of pertaining quality of services to gratify user’s desires. The tasks are categorized into five classes by the anticipated algorithm. Every one group contains the tasks with akin attributes (like, types of the users and tasks, size and latency of the task). Once the tasks are allocated to a precise group, scheduler starts assigning these tasks to accessible services. This assignment of tasks was performed in two steps: In Step-I is to decide which group tasks is to be scheduled foremost. Such decision will be based on the attributes of the tasks of each group. Hence, the groups which have higher task’s attribute values are scheduled foremost. Step-II is for taking internal decision that is which task from the selected group is scheduled foremost. This decision will be based on time needed for task’s execution. Therefore, the task which has the lowest time for execution will schedule foremost.

     


  • Keywords


    Cloud Computing, Task Scheduling, Latency, Load Balancing, Services, Execution Time.

  • References


      [1] Tilak Sujit et al. A survey of scheduling algo’s in cloud env. Intl Jr Engg Inv, Sep-2012, PP 36–9.

      [2] Wu Xiaonian et al, A task scheduling algo’s based on QOS-driven in cloud comp., Intnl conf on info tech and quantitative management, in China.

      [3] Liu Gang et al Proceedings of the 2012 intnl conf of modern comp science and app’s, Zhenyu Du; 2013. pp. 47–52.

      [4] Selvarani S et al Improved cost-based algo for task scheduling in cloud comp, Intnl conf. IEEE-2010.

      [5] Abdullah Monir et al, Cost-based multi-QOS job scheduling using divisible load theory in cloud comp, Intnl conf on computational science. ICCS-2013.

      [6] Quarati Alfonso et al, Hybrid clouds brokering: business opportunities, QoS and energy-saving issues. J Simul Model Pract Theory 2013, pp. 121–34.

      [7] Chen Tao et al, Dynamic QOS optimization architecture for cloud-based DDDAS. Intnl Jr. Comp. Algorithm June-2013.

      [8] Bittencourt et al , A cost optimization algorithm for workflow scheduling in hybrid clouds. Jr. Internet Serv Appl-2011.

      [9] Ravichandran S et al, ER. Dynamic scheduling of data using genetic algorithm in cloud comp, Innl. Jr. Adv Engg &

      [10] Tech 2013, pp. 327–34.

      [11] Hend Gamal El Din Hassan Ali et al, Grouped tasks scheduling algorithm based on QoS in cloud computing network Cairo University Egyptian Informatics Journal (2017), pp.11–19

      [12] K. Jairam Naik, Dr. A. Jagan, Dr. N. Satyanarayana, “An enhanced mechanism for balanced job scheduling based on deadline control in computational grid”, 2nd International Conference on Emerging Trends in Electrical, Communication and Information Technologies 2015 (ICECIT 2015), SRIT, Anantapur,AP. 19-21Dec, 2015

      [13] K. Jairam Naik, Dr. A. Jagan, Dr. N. Satyanarayana, “A novel algorithm for fault tolerant job Scheduling and load balancing in grid computing environment”, International Conference on Green Computing and Internet of Things (ICGCIoT 2015), Galgotia University, Noida, 8-10 Oct, 2015(IEEE Explore)

      [14] K. Jairam Naik, Dr. N. Satyanarayana, “A Novel Fault-tolerant Task Scheduling Algorithm for Computational Grids”, 15th IEEE International Conference on Advanced Computing Technologies (ICACT-2013), AITS, Rajampet, Andrapradesh, 10th – 11th August 2013(IEEE Explore)

      [15] K. Jairam Naik, Dr. A. Jagan, Dr. N. Satyanarayana, “A Cost Greedy Price Adjustment based Job Scheduling and Load Balancing in Grids”, International Journal of Computing and ICT Research (IJCIR), Vol.10, Issue 1, June,2016

      [16] K. Jairam Naik, Dr. A. Jagan, Dr. N. Satyanarayana, “A Novel Approach for Job Scheduling and Load Balancing in Grid Computing Environment”, Annals. Computer Science Series Journal, Vol. XIII fasc.2, December, 2015


 

View

Download

Article ID: 20236
 
DOI: 10.14419/ijet.v7i4.6.20236




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.