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Metaheuristic Approaches to Task Consolidation Problem in the Cloud
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Author(s): Sambit Kumar Mishra (National Institute of Technology Rourkela, India), Bibhudatta Sahoo (National Institute of Technology Rourkela, India), Kshira Sagar Sahoo (National Institute of Technology Rourkela, India)and Sanjay Kumar Jena (National Institute of Technology Rourkela, India)
Copyright: 2017
Pages: 22
Source title:
Resource Management and Efficiency in Cloud Computing Environments
Source Author(s)/Editor(s): Ashok Kumar Turuk (National Institute of Technology Rourkela, India), Bibhudatta Sahoo (National Institute of Technology Rourkela, India)and Sourav Kanti Addya (National Institute of Technology Rourkela, India)
DOI: 10.4018/978-1-5225-1721-4.ch007
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Abstract
The service (task) allocation problem in the distributed computing is one form of multidimensional knapsack problem which is one of the best examples of the combinatorial optimization problem. Nature-inspired techniques represent powerful mechanisms for addressing a large number of combinatorial optimization problems. Computation of getting an optimal solution for various industrial and scientific problems is usually intractable. The service request allocation problem in distributed computing belongs to a particular group of problems, i.e., NP-hard problem. The major portion of this chapter constitutes a survey of various mechanisms for service allocation problem with the availability of different cloud computing architecture. Here, there is a brief discussion towards the implementation issues of various metaheuristic techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), BAT algorithm, etc. with various environments for the service allocation problem in the cloud.
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