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
-
Interface Summary Interface Description CloudletToVmMappingHeuristic Provides the methods to be used for implementing a heuristic to get a sub-optimal solution for mapping Cloudlets to 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.HeuristicSolution<T> A solution for a complex problem found using aHeuristic
implementation. -
Class Summary Class Description 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.HeuristicAbstract<S extends HeuristicSolution<?>> A base class forHeuristic
implementations.SimulatedAnnealing<S extends HeuristicSolution<?>> A base class for implementation of Simulated Annealing algorithms used to find a suboptimal solution for a problem defined by sub-classes of this one.