## Improved Virus Optimization algorithm for two-objective tasks Scheduling in Cloud Environment

### Kadda Beghdad Bey, Sofiane Bouznad, Farid Benhammadi, Hassina Nacer

DOI: http://dx.doi.org/10.15439/2019F63

Citation: Communication Papers of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 20, pages 109–117 (2019)

Abstract. Cloud computing is increasingly recognized as a new way to use on-demand, computing, storage and network services in a transparent and efficient way. The development of applications in cloud environments is faced with the need to efficiently schedule a large number of tasks and resources. However, in the most of the time, the resources in cloud are not efficiently utilized due to inadequate scheduling task algorithm in virtual machines. Therefore, task scheduling is one of the most challenging issues in cloud computing. In this paper, we propose two-objective virus optimization algorithm of the makespan and the cost for mapping tasks to virtual machines in order to meet the needs of cloud service quality and proper assignment of resources. Thus based on genetic algorithm and some parameters of Virus optimization algorithm are redefined to strengthen sorting ability between virus infection strategies. Using CloudSim simulator, our combined methods aims to improve the performance of scheduling algorithms and outperforms the some existing approaches for task scheduling in Cloud computing.

### References

- J. Barbosa and B. Moreira, “Dynamic job scheduling on heterogeneous clusters”, Eighth International Symposium on Parallel and Distributed Computing, 2009.
- O.H. Ibarra, C.E. Kim, Heuristic algorithms for scheduling independent tasks on non-identical processors, Journal of the ACM 24 (2) (1977), pp 280–289.
- H. Izakian, A. Abraham, V. Snasel, “Comparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments”, In: IWHGA ’09: Proceedings of the IEEE International Workshop.
- S. Tareghian, Z. Bornaee, “Algorithm to improve job scheduling problem in cloud computing environment”, 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2015, pp 684-688.
- C.Y. Liu, C.M. Zou and P. Wu, “A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization in Cloud Computing”, International Symposium on Distributed Computing and Applications To Business, Engineering and Science. 2014, pp 68-72.
- F. Tao, Y. Feng, L. Zhang, et al., “CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling”. Journal of Applied Soft Computing, 2014, 19(6), pp 264– 279.
- M. Zhang, Y. Yang, Z. Mi, et al., “An Improved Genetic-Based Approach to Task Scheduling in Inter-cloud Environment[, Ubiquitous Intelligence and Computing”, IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC- ATCScalCom), 2015: 997-1003.
- B. Keshanchi, A. Souri, N.J. Navimipour, “An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing”, Journal of Systems and Software, 2017, 124, pp 1-21.
- K. Beghdad Bey, F. Benhammadi, M. Y Boudaren and S. Khamadja, “Load Balancing Heuristic for Tasks Scheduling in Cloud Environment”, 19th International Conference on Enterprise Information Systems (ICEIS2017), 26-29 April 2017.
- Y.C. Liang and J. R. C. Juarez, “A novel metaheuristic for continuous optimization problems: Virus optimization algorithm”. In: Engineering Optimization 48.1 (2016), pp.73–93.
- S. G. Ahmad, C. S. Liew, E. U. Munir, T. F. Ang, S. U. Khan, “A Hybrid Genetic Algorithm for Optimization of Scheduling Workflow”, Applications in Heterogeneous Computing Systems, Vol. 87, January 2016, pp. 80-90.
- L. Guo, G. Shao, and S. Zhao, “Multi-objective Task Assignment in cloud computing by Particle Swarm Optimization”. In Proceedings of 8th Int. Conf. on Wireless Communications, Networking and Mobile computing, 2012, pp 1-4.
- S. Pandey, L. Wu, S. Guru and R. Buyya, “A particle swarm optimization based heuristic for scheduling workflow applications in cloud computing environments”. 24th IEEE Int’l Conference on Advanced Information Networking and Applications (AINA), Perth, Australia, 2010, pp. 400-407.
- W. Shu, W. Wang, Y. Wang, “A Novel Energy-Efficient Resource Allocation Algorithm Based on Immune Clonal Optimization for Green Cloud Computing”. EURASIP Journal on Wireless Communications and Networking, Vol. 64, December 2014.
- S. Tayal, “Task Scheduling optimization for the Cloud Computing Systems”, International journal of advanced engineering sciences and technologies, Vol No. 5, Issue No. 2, 201, pp. 111-115.
- T. Wang, Z. Liu , Y. Chen, Y. Xu, X. Dai, “Load Balancing Task Scheduling based on Genetic Algorithm in Cloud Computing”, 12th International Conference on Dependable, Autonomic and Secure Computing, IEEE 2014.
- H. Chu, “Service Cost of Resource Scheduling in Cloud Computing based on an Improved Algorithm Combining Support Vector Machine with Genetic Algorithm”, International Journal of Grid and Distributed Computing Vol. 9, No. 6 (2016), pp.51-62.
- S. Kim, J. Byeon, H. Yu and H. Liu, “Biogeography-Based Optimization for Optimal Job Scheduling in Cloud Computing”, Applied Mathematics and Computation, Elsevier, Vol.247, pp. 266-280, 2014.
- A.V Lakra, and D. K Yadav, “Multi-Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization”, International Conference on Intelligent Computing, Communication & Convergence, 2015.
- A. Narwal and S. Dhingra, “Task Scheduling Algorithm Using Multi-Objective Functions for Cloud Computing Environment”, International journal of control theory and applications, Vol. 10(14), pp. 227-238, 2017.
- N. Bansal, A. Maurya, T. Kumar, et al., “Cost performance of QoS Driven task scheduling in cloud computing”. Procedia Computer Science, 57, 2015, pp. 126-130.
- M. Abdullahi, M. A. Ngadi, S. M. Abdulhamid, “Symbiotic Organism Search Optimization Based Task Scheduling in Cloud Computing Environment”. Future Generation Computer Systems, Vol. 56, March 2016, pp. 640-650.
- Y. Sun, J. White, S. Eade, D. C. Schmidt, “ROAR: AQoS-Oriented Modeling Framework for Automated Cloud Resource Allocation and Optimization”, Journal of Systems and Software, 2015.
- D. G. Feitelson and B. Nitzberg, “Job characteristics of a production parallel scientific workload on the NASA Ames iPSC/860”, In: workshop on job scheduling strategies for parallel processing, Springer, 1995, pp. 337–360.