Performance Evaluation of an Adopted Model based on Big-Bang Big-Crunch and Artificial Neural Network for Cloud Applications


  • pradeep rawat
  • Punit Gupta Manipal University Jaipur, Jaipur, India
  • Priti Dimri
  • G. P. Saroha



Big-Bang Big-Crunch, Task, Computing, Fitness, Genetic, MIPS, PSO, SJF, SLA,


High-performance computing changes the way of computing. With emerging technologies like grid computing, cloud computing and smart health care application has changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to the requirement for real time applications. Cloud computing comes with pay per use model to pay for only those resources that you have used. Inside cloud there lies many issues related to efficient and cost-effective model to improve cloud performance and complete the client task with least cost and high performance. E-Health care services are one of most computational intensive services in cloud, they require real time computing which can only be achieved if the computational resources can compute it in least time. Cloud can accomplish this using efficient scheduling algorithm. This article focuses on task scheduling policy which aims to improve the performance in real-time with the least execution time, network cost and execution cost-effective at the same time. The proposed model is inspired from Big-Bang Big-Crunch algorithm in astrology. The work has been compared with a genetic algorithm, Particle swarm optimization and TOPSIS algorithm. The result shows that the proposed algorithm performs better than existing approaches taking execution time, network delay and execution cost into considerations. The results are validated using real-time data set and (standard workload file) from workload traces (San Diego Supercomputer Center (SDSC) Blue Horizon logs), and fabricated data sets. The proposed algorithm aims to improve the performance by reducing the scheduling delay, network delay with the least resource cost to complete the task in least cost to the user with high quality of service. The proposed model is to improve the performance of the system in real time and reduce the computing cost for users. The proposed system also aims to provide better quality of services to the user with optimal computing environment and high reliability for large scale application in smart health care.


A. A. Beegom and M. Rajasree, “A particle swarm optimization based pareto optimal task scheduling in cloud computing,” in International Conference in Swarm Intelligence. Springer, 2014, 79–86.

N. A. B. M. Shaari, T. F. Ang, L. Y. Por, and C. S. Liew, “Dynamic pricing scheme for resource allocation in a multi-cloud environment,” Malaysian J. Comput. Sci., vol. 30, no. 1, 1–11, 2017.

A. Liu and Z. Wang, “Grid task scheduling based on adaptive ant colony algorithm,” in Management of e-Commerce and e-Government, 2008. ICMECG’08. International Conference on. IEEE, 2008, 415–418.

C. Zhao, S. Zhang, Q. Liu, J. Xie, and J. Hu, “Independent tasks scheduling based on the genetic algorithm in cloud computing,” in Wireless Communications, Networking, and Mobile Computing, 2009. WiCom’09. 5th International Conference on. IEEE, 2009, 1–4.

A. Suresh and R. Varatharajan, “Competent resource provisioning and distribution techniques for cloud computing environment,” Cluster Comput., 1–8, 2017.

B. Kruekaew and W. Kimpan, “Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony,” vol. 1, 1–5, 2014.

Frincu ME, Craciun C. Multi-objective meta-heuristics for scheduling applications with high availability requirements and cost constraints in multi-cloud environments. In 2011 fourth IEEE international conference on utility and cloud computing 2011 Dec 5, 267-274.

Wickremasinghe B, Calheiros RN, Buyya R. Cloudanalyst: A cloudsim-based visual modeler for analyzing cloud computing environments and applications. In2010 24th IEEE international conference on advanced information networking and applications 2010 Apr 20, 446-452.

C. T. Joseph, K. Chandrasekaran, and R. Cyriac, “A novel family genetic approach for virtual machine allocation,” Procedia Comput. Sci., vol. 46, no. Icict 2014, 558–565.

Zhao C, Zhang S, Liu Q, Xie J, Hu J. Independent tasks scheduling based on genetic algorithm in cloud computing. In2009 5th International Conference on Wireless Communications, Networking and Mobile Computing 2009 Sep 24 , 1-4.

P. Rawat, “A Survey and Analysis with Different Resource Provisioning Strategies in Cloud Environment,” no. 1, 339–345.

C.-W. Chiang, Y.-C. Lee, C.-N. Lee, and T.-Y. Chou, “Ant colony optimization for task matching and scheduling,” IEE Proceedings-Computers and Digital Techniques, vol. 153, no. 6, 373–380.

Sheikh HF, Ahmad I, Fan D. An evolutionary technique for performance-energy-temperature optimized scheduling of parallel tasks on multi-core processors. IEEE Transactions on Parallel and Distributed Systems. 2015 Apr 9;27(3), 668-81.

Mladenow A, Kryvinska N, Strauss C. Towards cloud-centric service environments. Journal of Service Science Research. 2012 Dec 1:4(2), 213-234.

Zhang F, Cao J, Hwang K, Li K, Khan SU. Adaptive workflow scheduling on cloud computing platforms with iterative ordinal optimization. IEEE Transactions on Cloud Computing. 2014 Aug 21;3(2), 156-68.

Venticinque S, Aversa R, Di Martino B, Rak M, Petcu D. A cloud agency for SLA negotiation and management. In European Conference on Parallel Processing 2010 Aug 31, 587-594.

Guo L, Zhao S, Shen S, Jiang C. Task scheduling optimization in cloud computing based on heuristic algorithm. Journal of networks. 2012 Mar 1;7(3),pp :547.

Rawat PS, Dimri P, Saroha GP. Virtual machine allocation to the task using an optimization method in cloud computing environment. International Journal of Information Technology. 2018, 1-9.

M. Kalra and S. Singh, “A review of metaheuristic scheduling techniques in cloud computing,” Egypt. Informatics J., vol. 16, no. 3, 275–295.

Jin X, Zhang F, Wang L, Hu S, Zhou B, Liu Z. Joint optimization of operational cost and performance interference in cloud data centers. IEEE Transactions on Cloud Computing. 2015 Jun 25;5(4), 697-711.

J. Yu and R. Buyya, “Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms,” Scientific Programming, vol. 14, no. 3-4, 217–230.

S. G. Domanal and G. R. M. Reddy, “An efficient cost optimized scheduling for spot instances in a heterogeneous cloud environment,” Future Generation Computer Systems, vol. 84, 11–21, 2018.

J. Gu, J. Hu, T. Zhao, and G. Sun, “A new resource scheduling strategy based on a genetic algorithm in cloud computing environment,” Journal of computers, vol. 7, no. 1, 42–52, 2012.

Domanal SG, Reddy GR. Optimal load balancing in cloud computing by efficient utilization of virtual machines. In2014 Sixth International Conference on Communication Systems and Networks (COMSNETS) 2014 Jan 6, 1-4.

K. Kaur, A. Chhabra, and G. Singh, “Heuristics based genetic algorithm for scheduling static tasks in the homogeneous parallel system,” International Journal of Computer Science and Security (IJCSS), vol. 4, no. 2, 183– 198, 2010.

X. Lu and Z. Gu, “A load-adaptive cloud resource scheduling model based on ant colony algorithm,” in Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on. IEEE, 2011, 296–300.

J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE transactions on evolutionary computation, vol. 10, no. 3, 281–295, 2006.

Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems. 2012 May 1;28(5), 755-68.

Beheshti Z, Shamsuddin SM. A review of population-based meta-heuristic algorithms. Int. J. Adv. Soft Comput. Appl. 2013 Mar 1;5(1), 1-35.

M. A. Rodriguez and R. Buyya, “Scheduling Algorithm for Scientific Workflows on Clouds,” vol. 2, no. 2, 222–235, 2014.

J. Yu and R. Buyya, “Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms,” vol. 14, 217–230, 2006.

D. C. Devi and V. R. Uthariaraj, “Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks,” vol. 2016, 1-8, 2016.

S. H. H. Madni, M. S. A. Latiff, Y. Coulibaly, and S. M. Abdulhamid, “Recent advancements in resource allocation techniques for cloud computing environment: a systematic review,” Cluster Comput., vol. 20, no. 3, 2489–2533, 2017.

O. K. Erol and I. Eksin, “A new optimization method: Big Bang-Big Crunch,” Adv. Eng. Softw., vol. 37, no. 2, 106–111, 2006.

G. M. Jaradat and M. Ayob, “Big Bang-Big Crunch optimization algorithm to solve the course timetabling problem,” Proc. 2010 10th Int. Conf. Intell. Syst. Des. Appl. ISDA’10, 1448–1452, 2010.

Firdhous M, Ghazali O, Hassan S. Modeling of cloud system using Erlang formulas. In The 17th Asia Pacific Conference on Communications 2011 Oct 2, 411-416.

Zhang Y, Wang Y, Wang X. Greenware: Greening cloud-scale data centers to maximize the use of renewable energy. InACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing 2011 Dec 12, 143-164.

Kumar BA, Ravichandran T. Time and cost optimization algorithm for scheduling multiple workflows in hybrid clouds. European Journal of Scientific Research. 2012 Oct; 89(2), 265-75.

R. N. Calheiros, R. Ranjan, A. Beloglazov, and A. F. De Rose, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” no. August 2010, 23–50, 2011.

R. Pragaladan and R. Maheswari, “Improve workflow scheduling technique for novel particle swarm optimization in the cloud environment,” Int. J. Eng. Res. Gen. Sci, vol. 2, no. 5, 1-22, 2014.

L. Guo, S. Zhao, S. Shen, and C. Jiang, “Task scheduling optimization in cloud computing based on heuristic algorithm,” Journal of networks, vol. 7, no. 3, 530- 547, 2012.

Meyer CD, Plemmons RJ, editors. Linear algebra, Markov chains, and queuing models. Springer Science & Business Media; 2012 Dec 6.

P. Y. Zhang and M. C. Zhou, “Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy,” IEEE Trans. Autom. Sci. Eng., vol. 15, no. 2, 772–783, 2018.

Z. H. Zhan, X. F. Liu, Y. J. Gong, J. Zhang, H. S. H. Chung, and Y. Li, “Cloud computing resource scheduling and a survey of its evolutionary approaches,” ACM Comput. Surv., vol. 47, no. 4, 1-22, 2015.