QoS-Aware Efficient Tasks Scheduling in Heterogeneous Cloud Computing Environment

Authors

  • Junaid Hassan University of Engineering and Technology Taxila Pakistan
  • Zeshan Iqbal University of Engineering and Technology Taxila

Keywords:

Cloud Computing, Task Scheduling, Genetic Algorithm, Quality of Service, Bandwidth

Abstract

The management and prioritization of network traffic to ensure the efficient transmission of important data is achieved through Quality of Service (QoS) technologies and techniques. QoS facilitates allocating necessary resources and bandwidth to critical applications and services while de-prioritizing less important traffic. This is accomplished by classifying and marking packets. Task scheduling involves coordinating and managing the execution of tasks in a computer system or network, including allocating resources and determining the order in which tasks are executed. Task scheduling algorithms use priority, resource requirements, and task dependencies to determine the most efficient way to execute tasks. A heterogeneous cloud environment utilizes multiple cloud computing platforms from different vendors such as IaaS, PaaS, and SaaS to deliver services and optimize cost, performance, and scalability. The task scheduling problem in cloud computing involves effectively mapping workloads to virtual resources. The study introduces the genetic-based algorithm BGA to increase makespan and resource consumption, while SGA focuses on convergence speed. These strategies are compared with current meta-heuristic and heuristic techniques.

 

Keywords: Cloud Computing; Task Scheduling; Genetic Algorithm; Quality of Service; Bandwidth

Author Biography

Junaid Hassan, University of Engineering and Technology Taxila Pakistan

Junaid Hassan is a student in MS in Computer Science at the Department of Computer Science, University of Engineering and Technology Taxila. He started his MS in Computer Science in 2018. 

References

J. Logeshwaran, "The control and communication management for ultra dense cloud system using fast Fourier algorithm," ICTACT Journal on Data Science and Machine Learning, vol. 3, no. 2, pp. 281-284, 2022.

C. Surianarayanan and P. R. Chelliah, "Essentials of Cloud Computing." Springer, 2019.

S. Kolb, "On the Portability of Applications in Platform as a Service." University of Bamberg Press, 2019.

M. Abdullahi, M. A. Ngadi, S. I. Dishing, S. i. M. Abdulhamid, and M. J. Usman, "A survey of symbiotic organisms search algorithms and applications," Neural computing and applications, vol. 32, pp. 547-566, 2020.

M. Abdullahi, M. A. Ngadi, S. I. Dishing, and S. i. M. Abdulhamid, "An adaptive symbiotic organisms search for constrained task scheduling in cloud computing," Journal of ambient intelligence and humanized computing, vol. 14, no. 7, pp. 8839-8850, 2023.

Y.-L. Lee, S. N. Arizky, Y.-R. Chen, D. Liang, and W.-J. Wang, "High-availability computing platform with sensor fault resilience," Sensors, vol. 21, no. 2, p. 542, 2021.

J. Hassan and Z. Iqbal, "QoS-Aware Efficient Tasks Scheduling in Heterogeneous Cloud Computing Environment," University of Wah Journal of Computer Science, vol. 5, no. 1, 2023.

A. Hussain, M. Aleem, A. Khan, M. A. Iqbal, and M. A. Islam, "RALBA: a computation-aware load balancing scheduler for cloud computing," Cluster Computing, vol. 21, pp. 1667-1680, 2018.

R. Priyadarshini, M. Alagirisamy, N. Rajendran, A. K. Marandi, and V. V. Patil, "Minimization of Makespan and Energy Consumption in Task Scheduling in Heterogeneous Cloud Environment," International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 2s, pp. 276–280-276–280, 2022.

H. Ning, Y. Li, F. Shi, and L. T. Yang, "Heterogeneous edge computing open platforms and tools for internet of things," Future Generation Computer Systems, vol. 106, pp. 67-76, 2020.

T. S. Alnusairi, A. A. Shahin, and Y. Daadaa, "Binary PSOGSA for load balancing task scheduling in cloud environment," arXiv preprint arXiv:1806.00329, 2018.

F. A. Saif, R. Latip, M. Derahman, and A. A. Alwan, "Hybrid meta-heuristic genetic algorithm: Differential evolution algorithms for scientific workflow scheduling in heterogeneous cloud environment," in Proceedings of the Future Technologies Conference, 2022: Springer, pp. 16-43.

R. Pradhan and S. C. Satapathy, "Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment," 2022.

M. Raushan, A. K. Sebastian, M. Apoorva, and N. Jayapandian, "Advanced load balancing min-min algorithm in grid computing," in Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI-2018), 2020: Springer, pp. 991-997.

S. Supreeth, K. Patil, S. D. Patil, S. Rohith, Y. Vishwanath, and K. Prasad, "An efficient policy-based scheduling and allocation of virtual machines in cloud computing environment," Journal of Electrical and Computer Engineering, vol. 2022, 2022.

R. Pradhan and S. C. Satapathy, "Particle Swarm Optimization-Based Energy-Aware Task Scheduling Algorithm in Heterogeneous Cloud," in Communication, Software and Networks: Proceedings of INDIA 2022: Springer, 2022, pp. 439-450.

Z. Mohamad, A. A. Mahmoud, W. Nik, M. A. Mohamed, and M. M. Deris, "A genetic algorithm for optimal job scheduling and load balancing in cloud computing," International Journal of Engineering & Technology, vol. 7, no. 3, pp. 290-294, 2018.

N. P. Sodinapalli, S. Kulkarni, N. A. Sharief, and P. Venkatareddy, "An efficient resource utilization technique for scheduling scientific workload in cloud computing environment," IAES International Journal of Artificial Intelligence, vol. 11, no. 1, p. 367, 2022.

R. Ghafari, F. H. Kabutarkhani, and N. Mansouri, "Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review," Cluster Computing, vol. 25, no. 2, pp. 1035-1093, 2022.

D. K. Shukla, D. Kumar, and D. S. Kushwaha, "An efficient tasks scheduling algorithm for batch processing heterogeneous cloud environment," International Journal of Advanced Intelligence Paradigms, vol. 23, no. 1-2, pp. 203-216, 2022.

T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, and A. Cosar, "A survey on new generation metaheuristic algorithms," Computers & Industrial Engineering, vol. 137, p. 106040, 2019.

J.-B. Wang et al., "A machine learning framework for resource allocation assisted by cloud computing," IEEE Network, vol. 32, no. 2, pp. 144-151, 2018.

Y. Lu and X. Xu, "Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services," Robotics and Computer-Integrated Manufacturing, vol. 57, pp. 92-102, 2019.

D. A. Shafiq, N. Z. Jhanjhi, A. Abdullah, and M. A. Alzain, "A load balancing algorithm for the data centres to optimize cloud computing applications," IEEE Access, vol. 9, pp. 41731-41744, 2021.

N. Thakkar and R. Nath, "Performance Analysis of Min-Min Max-Min and Artificial Bee Colony Load Balancing Algorithm in Cloud Computing," IJACS, vol. 7, no. 4, 2018.

K. Dubey and S. C. Sharma, "A hybrid multi-faceted task scheduling algorithm for cloud computing environment," International Journal of System Assurance Engineering and Management, vol. 14, no. Suppl 3, pp. 774-788, 2023.

F. Ebadifard and S. M. Babamir, "A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment," Concurrency and Computation: Practice and Experience, vol. 30, no. 12, p. e4368, 2018.

A. Halty, R. Sánchez, V. Vázquez, V. Viana, P. Pineyro, and D. A. Rossit, "Scheduling in cloud manufacturing systems: Recent systematic literature review," 2020.

G. F. Da Silva, F. Brasileiro, R. Lopes, F. Morais, M. Carvalho, and D. Turull, "QoS-driven scheduling in the cloud," Journal of Internet Services and Applications, vol. 11, pp. 1-36, 2020.

S. K. Mishra, B. Sahoo, and P. P. Parida, "Load balancing in cloud computing: a big picture," Journal of King Saud University-Computer and Information Sciences, vol. 32, no. 2, pp. 149-158, 2020.

R. Gulbaz, A. B. Siddiqui, N. Anjum, A. A. Alotaibi, T. Althobaiti, and N. Ramzan, "Balancer genetic algorithm—A novel task scheduling optimization approach in cloud computing," Applied Sciences, vol. 11, no. 14, p. 6244, 2021.

T. Prem Jacob and K. Pradeep, "A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization," Wireless Personal Communications, vol. 109, pp. 315-331, 2019.

T. Wang, P. Zhang, J. Liu, and M. Zhang, "Many-objective cloud manufacturing service selection and scheduling with an evolutionary algorithm based on adaptive environment selection strategy," Applied Soft Computing, vol. 112, p. 107737, 2021.

K. Maryam, M. Sardaraz, and M. Tahir, "Evolutionary algorithms in cloud computing from the perspective of energy consumption: A review," in 2018 14th international conference on emerging technologies (ICET), 2018: IEEE, pp. 1-6.

M. Abd Elaziz, S. Xiong, K. Jayasena, and L. Li, "Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution," Knowledge-Based Systems, vol. 169, pp. 39-52, 2019.

D. Gabi, A. S. Ismail, A. Zainal, Z. Zakaria, and A. Abraham, "Orthogonal Taguchi-based cat algorithm for solving task scheduling problem in cloud computing," Neural Computing and Applications, vol. 30, pp. 1845-1863, 2018.

M. Baghel, S. Agrawal, and S. Silakari, "Survey of metaheuristic algorithms for combinatorial optimization," International Journal of Computer Applications, vol. 58, no. 19, 2012.

M. Ali, S. U. Khan, and A. V. Vasilakos, "Security in cloud computing: Opportunities and challenges," Information sciences, vol. 305, pp. 357-383, 2015.

A. Hameed et al., "A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems," Computing, vol. 98, pp. 751-774, 2016.

K. Etminani and M. Naghibzadeh, "A min-min max-min selective algorihtm for grid task scheduling," in 2007 3rd IEEE/IFIP International Conference in Central Asia on Internet, 2007: IEEE, pp. 1-7.

R. NoorianTalouki, M. H. Shirvani, and H. Motameni, "A heuristic-based task scheduling algorithm for scientific workflows in heterogeneous cloud computing platforms," Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 8, pp. 4902-4913, 2022.

M. Abdullahi and M. A. Ngadi, "Symbiotic organism search optimization based task scheduling in cloud computing environment," Future Generation Computer Systems, vol. 56, pp. 640-650, 2016.

A. Hussain, M. Aleem, M. A. Islam, and M. A. Iqbal, "A rigorous evaluation of state-of-the-art scheduling algorithms for cloud computing," IEEE Access, vol. 6, pp. 75033-75047, 2018.

X. Wu, M. Deng, R. Zhang, B. Zeng, and S. Zhou, "A task scheduling algorithm based on QoS-driven in cloud computing," Procedia Computer Science, vol. 17, pp. 1162-1169, 2013.

E. S. Alkayal, N. R. Jennings, and M. F. Abulkhair, "Efficient task scheduling multi-objective particle swarm optimization in cloud computing," in 2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops), 2016: IEEE, pp. 17-24.

Published

2023-06-30 — Updated on 2023-12-29

How to Cite

Hassan, J., & Iqbal, Z. (2023). QoS-Aware Efficient Tasks Scheduling in Heterogeneous Cloud Computing Environment. University of Wah Journal of Computer Science, 5, 1–21. Retrieved from http://uwjcs.org.pk/index.php/ojs/article/view/61