Review on different Meta-Heuristic Techniques for Parallel Computing

@article{Kaur2017ReviewOD,
  title={Review on different Meta-Heuristic Techniques for Parallel Computing},
  author={D. Kaur and Amit Chabbra},
  journal={International Journal of Computer Applications},
  year={2017},
  volume={169},
  pages={15-19}
}
  • D. Kaur, Amit Chabbra
  • Published 17 July 2017
  • Computer Science
  • International Journal of Computer Applications
This paper represents the parallel computing as a kind of computation in which many computations or the running of processes are carried out simultaneously as well as scheduling and resource allocation to optimize performance criteria in multi-cluster heterogeneous environments is known for NP-hard problems. Multi-cluster environments are commonly represented as a substitution to high-performance computing for solving large-scale optimization problems. The review has shown the various meta… Expand

References

SHOWING 1-10 OF 32 REFERENCES
MIP Model Scheduling for Multi-Clusters
TLDR
A new MIP model is proposed which determines the best scheduling for all the jobs in the queue, identifying their resource allocation and its execution order to minimize the overall makespan. Expand
A review of metaheuristic scheduling techniques in cloud computing
TLDR
An extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization, Genetic Algorithm and Particle Swarm Optimization and two novel techniques: League Championship Algorithm (LCA) and BAT algorithm. Expand
Parallel ant colony optimization for resource constrained job scheduling
TLDR
This work considers a resource constrained scheduling problem which is motivated in mining supply chains and presents two popular meta-heuristics, ant colony optimization (ACO) and simulated annealing and investigates how best to parallelize these methods on a shared memory architecture consisting of several cores. Expand
Scheduling of Parallel Jobs in a Heterogeneous Multi-site Environement
TLDR
This paper addresses the scheduling of parallel jobs in a heterogeneous multi-site environment, where each site has a homogeneous cluster of processors, but processors at different sites have different speeds. Expand
Scheduling parallel batch jobs in grids with evolutionary metaheuristics
TLDR
The results of the experimental study show that GEO, despite its simplicity, outperforms the GA in a whole range of scheduling instances and is much easier to use. Expand
Slowdown-Guided Genetic Algorithm for Job Scheduling in Federated Environments
TLDR
The GA is provided with knowledge based on slowdown predictions for the application runtime, obtained by considering heterogeneity and bandwidth issues, and the results compared with a range of well-known heuristics in the literature are compared. Expand
Processor allocation policies for reducing resource fragmentation in multi-cluster grid and cloud environments
TLDR
The experimental results indicate that careful selection of processor allocation policies can improve overall system performance greatly and the proposed most-fit policy can outperform other policies in most conditions. Expand
Particle Swarm Optimization Scheduling for Energy Saving in Cluster Computing Heterogeneous Environments
TLDR
A novel approach is presented by using a Particle Swarm Optimization (PSO) based heuristic to generate scheduling decisions that minimize the overall energy consumption in large-scale computing facilities. Expand
Dynamic load balancing on heterogeneous multicore/multiGPU systems
TLDR
A dynamic load balancing library that allows parallel code to be adapted to heterogeneous systems for a wide variety of problems and the overhead introduced by the system is minimal and the cost to the programmer negligible. Expand
Resources Co-allocation Strategies in Grid Computing
TLDR
The proposed Strategies have been verified through an extension of GridSim simulation toolkit and the simulation results confirm that the Strategies allow us to achieve the most of the authors' goals. Expand
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