It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clu...It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clustering tasks according to spatio-temporal attributes,the clustered groups are linked into task sub-chains according to similarity.Then,based on the correlation between clusters,the child chains are connected to form a task chain.Therefore,the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension.When a sudden task occurs,a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks.Through the above improvements,the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks.In order to reflect the efficiency and applicability of the algorithm,a task allocation model for the unmanned aerial vehicle(UAV)group is constructed,and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed.Task assignment has been constructed.The study uses the self-adjusting characteristics of the bee colony to achieve task allocation.Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance.展开更多
The microservices architecture has been proposed to overcome the drawbacks of the traditional monolithic architecture.Scalability is one of the most attractive features of microservices.Scaling in the microservices ar...The microservices architecture has been proposed to overcome the drawbacks of the traditional monolithic architecture.Scalability is one of the most attractive features of microservices.Scaling in the microservices architecture requires the scaling of specified services only,rather than the entire application.Scaling services can be achieved by deploying the same service multiple times on different physical machines.However,problems with load balancing may arise.Most existing solutions of microservices load balancing focus on individual tasks and ignore dependencies between these tasks.In the present paper,we propose TCLBM,a task chainbased load balancing algorithm for microservices.When an Application Programming Interface(API)request is received,TCLBM chooses target services for all tasks of this API call and achieves load balancing by evaluating the system resource usage of each service instance.TCLBM reduces the API response time by reducing data transmissions between physical machines.We use three heuristic algorithms,namely,Particle Swarm Optimization(PSO),Simulated Annealing(SA),and Genetic Algorithm(GA),to implement TCLBM,and comparison results reveal that GA performs best.Our findings show that TCLBM achieves load balancing among service instances and reduces API response times by up to 10%compared with existing methods.展开更多
基金This work was supported by the National Natural Science and Technology Innovation 2030 Major Project of Ministry of Science and Technology of China(2018AAA0101200)the National Natural Science Foundation of China(61502522,61502534)+4 种基金the Equipment Pre-Research Field Fund(JZX7Y20190253036101)the Equipment Pre-Research Ministry of Education Joint Fund(6141A02033703)Shaanxi Provincial Natural Science Foundation(2020JQ-493)the Military Science Project of the National Social Science Fund(WJ2019-SKJJ-C-092)the Theoretical Research Foundation of Armed Police Engineering University(WJY202148).
文摘It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clustering tasks according to spatio-temporal attributes,the clustered groups are linked into task sub-chains according to similarity.Then,based on the correlation between clusters,the child chains are connected to form a task chain.Therefore,the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension.When a sudden task occurs,a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks.Through the above improvements,the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks.In order to reflect the efficiency and applicability of the algorithm,a task allocation model for the unmanned aerial vehicle(UAV)group is constructed,and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed.Task assignment has been constructed.The study uses the self-adjusting characteristics of the bee colony to achieve task allocation.Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance.
文摘The microservices architecture has been proposed to overcome the drawbacks of the traditional monolithic architecture.Scalability is one of the most attractive features of microservices.Scaling in the microservices architecture requires the scaling of specified services only,rather than the entire application.Scaling services can be achieved by deploying the same service multiple times on different physical machines.However,problems with load balancing may arise.Most existing solutions of microservices load balancing focus on individual tasks and ignore dependencies between these tasks.In the present paper,we propose TCLBM,a task chainbased load balancing algorithm for microservices.When an Application Programming Interface(API)request is received,TCLBM chooses target services for all tasks of this API call and achieves load balancing by evaluating the system resource usage of each service instance.TCLBM reduces the API response time by reducing data transmissions between physical machines.We use three heuristic algorithms,namely,Particle Swarm Optimization(PSO),Simulated Annealing(SA),and Genetic Algorithm(GA),to implement TCLBM,and comparison results reveal that GA performs best.Our findings show that TCLBM achieves load balancing among service instances and reduces API response times by up to 10%compared with existing methods.
