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Review on different Meta-Heuristic Techniques for Parallel Computing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 169 - Number 2 |
| Year of Publication: 2017 |
| Authors: Davinderjit Kaur, Amit Chabbra |
10.5120/ijca2017914599
|
Davinderjit Kaur, Amit Chabbra . Review on different Meta-Heuristic Techniques for Parallel Computing. International Journal of Computer Applications. 169, 2 ( Jul 2017), 15-19. DOI=10.5120/ijca2017914599
Abstract
This paper represents the parallel computing is a type of working out through which many data or the execution connected with processes are finished concurrently as well as scheduling along with source of information permitting so that we can optimize efficiency standards within multi-cluster heterogeneous situations is acknowledged for NP-hard problems. Multi-cluster environments are commonly represented as a substitution to high-performance computing regarding resolving large-scale search engine optimization difficulties. The review has shown the various meta heuristic techniques which has proved their usefulness to find the optimum schedule around large-scale allocated circumstances. It also shows the comparison of Meta heuristic techniques which evaluates the real workload trace as well as shows the advantages and disadvantages when it comes to other well-known approaches outlined inside literature.
References
- Dhananjay Thriuvady, et al “Parallel ant colony optimization for resource constrained job scheduling”. Springer (2014).
- Piotr Swistalski, et al “Scheduling parallel batch jobs in grids with evolutionary Metaheuristics”. Springer (2014).
- Makhlouf, Sid Ahmed, and Belabbas Yagoubi. "Resources Co-allocation Strategies in Grid Computing." CIIA. 2011.
- Randall, Marcus, and Andrew Lewis. "A parallel implementation of ant colony optimization." Journal of Parallel and Distributed Computing 62.9 (2002): 1421-1432.
- Huang, Kuo-Chan, and Kuan-Po Lai. "Processor allocation policies for reducing resource fragmentation in multi-cluster grid and cloud environments." Computer Symposium (ICS), 2010 International. IEEE, 2010.
- Sabin, Gerald, et al. "Scheduling of parallel jobs in a heterogeneous multi-site environment." Workshop on Job Scheduling Strategies for Parallel Processing. Springer Berlin Heidelberg, 2003.
- Gabaldon, Eloi, Josep Lluis Lerida, Fernando Guirado, and Jordi Planes. "Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments." The Journal of Supercomputing (2016): 1-16.
- Gabaldon, Eloi, et al. "Particle Swarm Optimization Scheduling for Energy Saving in Cluster Computing Heterogeneous Environments." Future Internet of Things and Cloud Workshops (FiCloudW), IEEE International Conference on. IEEE, 2016.
- Gabaldon, Eloi, et al. "Slowdown-Guided Genetic Algorithm for Job Scheduling in Federated Environments." International Conference on Nature of Computation and Communication. Springer International Publishing, 2014.
- Blanco, H., Guirado, F., Lérida, J. L., & Albornoz, V. M. (2012, August). MIP model scheduling for multi-clusters. In European Conference on Parallel Processing (pp. 196-206). Springer Berlin Heidelberg.
- Bolaji, A. L. A., Khader, A. T., Al-Betar, M. A., & Awadallah, M. A. (2013). Artificial bee colony algorithm, its variants and applications: A survey. Journal of Theoretical & Applied Information Technology, 47(2).
- Acosta, Alejandro, et al. "Dynamic load balancing on heterogeneous multicore/multiGPU systems." High Performance Computing and Simulation (HPCS), 2010 International Conference on. IEEE, 2010.
- Makhlouf, Sid Ahmed, and Belabbas Yagoubi. "Resources Co-allocation Strategies in Grid Computing." CIIA. 2011.
- Huang, Kuo-Chan, and Kuan-Po Lai. "Processor allocation policies for reducing resource fragmentation in multi-cluster grid and cloud environments." Computer Symposium (ICS), 2010 International. IEEE, 2010.
- Sabin, Gerald, et al. "Scheduling of parallel jobs in a heterogeneous multi-site environment." Workshop on Job Scheduling Strategies for Parallel Processing. Springer Berlin Heidelberg, 2003.
- Acosta, Alejandro, et al. "Dynamic load balancing on heterogeneous multicore/multiGPU systems." High Performance Computing and Simulation (HPCS), 2010 International Conference on. IEEE, 2010.
- Ernst, Andreas T., and Gaurav Singh. "Lagrangian particle swarm optimization for a resource constrained machine scheduling problem." 2012 IEEE Congress on Evolutionary Computation. IEEE, 2012.
- Ying, Kuo-Ching, and Shih-Wei Lin. "Unrelated parallel machines scheduling with sequence-and machine-dependent setup times and due date constraints." International Journal of Innovative Computing, Information and Control 8.5 (2012): 3279-3297.
- Blanco, Héctor, et al. "Multiple Job Allocation in Multicluster System⋆."
- Fister Jr, Iztok, Dušan Fister, and Iztok Fister. "A comprehensive review of cuckoo search: variants and hybrids." International Journal of Mathematical Modelling and Numerical Optimisation 4.4 (2013): 387-409.
- Kalra, Mala, and Sarbjeet Singh. "A review of metaheuristic scheduling techniques in cloud computing." Egyptian Informatics Journal 16.3 (2015): 275-295.
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