The week's pick
Reseach Article
Deadline Constrained Workflow Scheduling Optimization by Initial Seeding with ANT Colony Optimization
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 155 - Number 14 |
| Year of Publication: 2016 |
| Authors: Neel Sinha, Vishesh Srivastav, Waquar Ahmad |
10.5120/ijca2016912409
|
Neel Sinha, Vishesh Srivastav, Waquar Ahmad . Deadline Constrained Workflow Scheduling Optimization by Initial Seeding with ANT Colony Optimization. International Journal of Computer Applications. 155, 14 ( Dec 2016), 24-29. DOI=10.5120/ijca2016912409
Abstract
Cloud computing is a new model of service provisioning in distributed systems. It encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in workflow scheduling in cloud environment is its quality of service, which minimizes the cost of computation of workflows. In this paper, we use the Predicted Earliest finish time (PEFT) for initial seeding to Ant Colony optimization technique (ACO). As we know ACO is a very powerful technique appropriate for optimization.. The increasing complexity of the workflow applications is forcing researchers to explore hybrid approaches to solve the workflow scheduling problem. In this paper we proposed PEFT with ACO algorithm which reduces the initialization complexity and converge ACO algorithm.
References
- Q. Zhang, L. Cheng and R. Boutaba, "Cloud computing: state-of- theart and research challenges",J. Internet Services and Applications, vol. 1, no. 1, 2010
- R. Ranjan, L. Zhao, X. Wu, A. Liu, A. Quiroz and M. Parashar, &ldquo,Peer-to- Peer Cloud Provisioning: Service Discovery and Load-Balancing,&rdquo, Cloud Computing, Computer Communications and Networks, N. Antonopoulos and L. Gillam, eds., pp. 195-217, Springer, 2010.
- G. Aceto, A. Botta, W. de Donato and A. Pescapè, "Cloud monitoring: A survey", Computer Networks, vol. 57, no. 9, pp. 2093-2115, 2013.
- K. Nuaimi, "A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms", in Network Cloud Computing and Applications (NCCA), 2012, 2016.
- I. FisterJr, X.-S. Yang, I. Fister, J. Brest, and D. Fister.A brief review of nature-inspired algorithms for optimization.Elektrotehniskivestnik, 80(3):116-122, 2013.
- E. García-Gonzalo and J. Fernández-Martínez, "A Brief Historical Review of Particle Swarm Optimization (PSO)", j bioinformintelli control, vol. 1, no. 1, pp. 3-16, 2012.
- E. Duman, M. Uysal and A. Alkaya, "Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem", Information Sciences, vol. 217, pp. 65-77, 2012.
- Jianyong Sun, J. Garibaldi and C. Hodgman, "Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review", IEEE/ACM Trans. Comput. Biol. and Bioinf., vol. 9, no. 1, pp. 185-202, 2012.
- J. Liu, X. Luo, X. Zhang, F. Zhang and B. Li, "Job Scheduling Model for Cloud Computing Based on Multi-Objective Genetic Algorithm", IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 3, January 2013.
- A. Soni, G. Vishwakarma and Y. Kumar Jain, "A Bee Colony based Multi-Objective Load Balancing Technique for Cloud Computing Environment", International Journal of Computer Applications, vol. 114, no. 4, pp. 19-25, 2015.
- M. Abdullah and M.Othman, "Cost-based Multi-QoS Job Scheduling Using Divisible Load Theory in Cloud Computing", Procedia Computer Science, vol. 18, pp. 928-935, 2013.
- D. Santos, A. de Sousa and F. Alvelos, "A hybrid column generation with GRASP and path relinking for the network load balancing problem", Computers & Operations Research, vol. 40, no. 12, pp. 3147-3158, 2013.
- Y. Jiang, Z. Shao, Y. Guo, H. Zhang and K. Niu, "DRSCRO: A Metaheuristic Algorithm for Task Scheduling on Heterogeneous Systems", Mathematical Problems in Engineering, vol. 2015, pp. 1-20, 2015.
- F. Zhang, J. Cao, K. Li, S. Khan and K. Hwang, "Multi-objective scheduling of many tasks in cloud platforms", Future Generation Computer Systems, vol. 37, pp. 309-320, 2014.
- A. Beloglazov, J. Abawajy and R. Buyya, "Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing", Future Generation Computer Systems, vol. 28, no. 5, pp. 755-768, 2012.
- T. Keskinturk, M. Yildirim and M. Barut, "An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup times", Computers & Operations Research, vol. 39, no. 6, pp. 1225-1235, 2012.
- M. Yakhchi, S. Ghafari and S. Yakhchi, "Proposing a load balancing method based on Cuckoo Optimization Algorithm nergyanagement in cloud computing infrastructures", in 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), Istanbul, 2015.
- J. Adhikari and S. Patil, "Double threshold energy aware load balancing in cloud computing",in Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference, Tiruchengode, 2013, pp. 1 - 6.
- J. Doyle, R. Shorten and D. O'Mahony, "Stratus: Load Balancing the Cloud for Carbon Emissions Control", IEEE Transactions on Cloud Computing, vol. 1, no. 1, pp. 1-13, 2013.
- S. Ahmad, C. Liew, E. Munir, T. Ang and S. Khan, "A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems", Journal of Parallel and Distributed Computing, vol. 87, pp. 80-90, 2016.
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