Patent Document ID: 10127085
Application ID: 14650747
Patent Flag: 1

Claim One:
1. A method for scheduling a task in cloud computing, comprising: parameterizing, by a cloud computing task scheduling system, feature information of the task, wherein the feature information comprises at least one of: Central Processing Unit (CPU), memory, bandwidth, resource cost and reciprocal of a failure rate; classifying, by the cloud computing task scheduling system, the task according to a priority of the task or a judgment index for executing the task, if the task has no priority; performing, by the cloud computing task scheduling system, a computation through a Bacteria Foraging Optimization Algorithm (BFOA) according to a classification result to determine a best working node; and executing the task by the best working node; wherein performing the computation through the BFOA algorithm according to the classification result to determine the best working node comprises: encoding the parameterized feature information of the task according to the classification result to obtain encoded feature information, and recording the encoded feature information of the task to a task information database; selecting N working nodes to form an initial bacterial colony, wherein each node of the N working nodes selected serve as bacterial individual i of the initial bacterial colony, where 1≤i≤N; decoding respectively the encoded feature information corresponding to each bacterial individual according to an algorithm decoder library; computing a fitness value of the each bacterial individual, wherein the fitness value for a current bacterial individual is computed after a taxis operation is performed on the current bacterial individual; performing a taxis operation on the each bacterial individual, wherein performing the taxis operation for a current bacterial individual comprises evaluating a current position of the current bacterial individual based on computing its fitness value, rotating the current bacterium individual to randomly generate an initial step of swimming, determining whether the fitness value of the new position is better, and when it is better, performing a quorum sensing operation on the each bacterial individual of the bacterial colony to allow the bacterial individual to sense the environment around to test whether the bacteria individual is in the best position in the bacterial colony; performing a duplication operation on the each bacterial individual, wherein performing the duplication operation comprises sequencing bacteria individuals according to priority of fitness values, eliminating half of the bacteria individuals of the bacteria colony having poor fitness values, and generating new bacteria individuals by duplicating the remaining bacteria individuals; performing a migration operation on the each bacterial individual in response to a bacterial individual of the bacterial colony satisfying a predefined migration probability, wherein performing migration operation comprises the bacterial individual dying out, and randomly generating a new bacteria individual in any position of a solution space, which may be more close to a global best solution and more favorable for the taxis operation to escape from a local best solution; determining whether a best bacterial individual obtained currently meets a user expectation value; in response to determining that the best bacterial individual obtained currently meets the user expectation value, selecting a working node corresponding to the best bacterial individual obtained currently as the best working node; and in response to determining that the best bacterial individual obtained currently not meeting the user expectation value, returning to a step of computing the fitness value of the each bacterial individual; wherein computing the fitness value of the current bacterial individual after the taxis operation is performed on the current bacterial individual i further comprises: ⁢ J i ⁡ ( j + 1 , k , l ) = J i ⁡ ( j , k , l ) + J cc ⁡ ( θ i ⁡ ( j + 1 , k , l ) , P ⁡ ( j + 1 , k , l ) ) J cc ⁡ ( θ , P ⁡ ( j , k , l ) ) = ∑ i = 1 N ⁢ J cc ⁡ ( θ , θ i ⁡ ( j , k , l ) ) = ∑ i = 1 N ⁢ [ - d attr ⁢ exp ( - w attr ⁢ ∑ m = 1 D ⁢ ( θ m - θ m i ) 2 ] + ∑ i = 1 N ⁢ [ h rep ⁢ exp ( - w rep ⁢ ∑ m = 1 D ⁢ ( θ m - θ m i ) 2 ] ⁢ P ⁢ ( j , k , l ) = { θ i ⁡ ( j , k , l ) | i = 1 , 2 , … ⁢ ⁢ S } where, J i (j,k,l) is a fitness value of a bacteria individual i after a jth taxis operation, a kth duplication operation and Ith migration operation; J cc (θ i (j+1,k,l),P(j+1,k,l)) is an inter-individual sensitivity value of the bacterial individual i; θ i (j+1,k,l is an updated position of the bacteria individual i after a (d−1)th taxis operation; P(j+1,k,l) is an individual position of the bacteria individual i in the bacterial colony after a (d−1)th taxis operation; P(j,k,l) is an individual position of the bacteria individual i in the bacterial colony after a jth taxis operation; d attr and w attr represent a quantity and a releasing velocity of an attractive factor respectively; h rep and w rep represent a quantity and a releasing velocity of a repelling factor respectively; ∑ i = 1 N ⁢ [ - d attr ⁢ exp ( - w attr ⁢ ∑ m = 1 D ⁢ ( θ m - θ m i ) 2 ] represents cybotaxis influence, on the bacterial colony, brought by a position where a ith bacterium individual locates; exp ( - w attr ⁢ ∑ m = 1 D ⁢ ( θ m - θ m i ) 2 represents a coefficient of the cybotaxis influence, on the bacterial colony, brought by the position where the ith bacterium individual locates; ∑ m = 1 D ⁢ ( θ m - θ m i ) 2 represents a variance of the position where the ith bacterium_individual locates; θ m represents a mth dimension of the position where the bacterial colony locates; θ m i represents a mth dimension of the position where the ith bacterium individual locates; D represents the number of dimensions of a bacterial search environment; ∑ i = 1 N ⁢ [ h rep ⁢ exp ( - w rep ⁢ ∑ m = 1 D ⁢ ( θ m - θ m i ) 2 ] represents repelling influence of the position where the ith bacterium individual locates on the bacterial colony; and exp ( - w attr ⁢ ∑ m = 1 D ⁢ ( θ m - θ m i ) 2 represents a coefficient of the repelling influence of the position where the ith_bacterium locates on the bacterial colony.