Package org.cloudsimplus.heuristics

package org.cloudsimplus.heuristics

Provides a set of interfaces and classes to develop heuristics to find sub-optimal solutions for problems, considering some utility function that has to be minimized or maximized. Such a function is also called a fitness function and as higher is the fitness better the found solution is.

Different heuristics include Simulated Annealing, Tabu Search and Ant Colony Optimization.

The first introduced heuristic is the CloudletToVmMappingSimulatedAnnealing that is used by a DatacenterBrokerHeuristic to map Cloudlets to VMs.

Author:

Manoel Campos da Silva Filho

Class	Description
CloudletToVmMappingHeuristic	Provides the methods to be used for implementing a heuristic to get a sub-optimal solution for mapping Cloudlets to Vm's.
CloudletToVmMappingSimulatedAnnealing	A heuristic that uses Simulated Annealing to find a sub-optimal mapping among a set of Cloudlets and VMs in order to reduce the number of idle or overloaded Vm Pe's.
CloudletToVmMappingSolution	A possible solution for mapping a set of Cloudlets to a set of Vm's.
Heuristic<S extends HeuristicSolution<?>>	Provides the methods to be used for implementation of heuristics to find solution for complex problems where the solution space to search is large.
HeuristicAbstract<S extends HeuristicSolution<?>>	An abstract class for Heuristic implementations.
HeuristicSolution<T>	A solution for a complex problem found using a Heuristic implementation.
SimulatedAnnealingAbstract<S extends HeuristicSolution<?>>	An abstract class for implementation of Simulated Annealing algorithms used to find a suboptimal solution for a problem defined by sub-classes of this one.