Research Of Cloud Service Composition Optimization Based On Improved Bacterial Foraging Algorithm

With the characteristics of high reliability,low price and on-demand service,cloud service is more and more popular among users,so it has gained explosive development in recent years.However,the functions provided by a single service are single and limited,It takes a lot of effort and time to develop services with complex functions to meet the needs of users.Therefore,it is necessary to combine services with a single function to protect the needs of users.How to quickly and accurately select a service combination that meets the needs of users from a large set of services has become an urgent problem to be solved.Most of the current service composition research only focuses on functional attributes,the current cloud service combination research pays more attention to functional attributes than non-functional attributes.And most of people focus on response time,cost,etc.,while it is easily ignored for memory,CPU,bandwidth and other influencing factors.The methods adopted for the service composition model are all intelligent optimization algorithms.It is necessary for improving service quality and reducing development costs to to select the cloud service combination with the best service quality that meets the user's non-functional requirements from a large number of cloud service candidates.First,analyze the response time,cost,availability,and CPU,bandwidth,memory and other occupancy rates of the cloud service composition optimization in the web service composition optimization,and perform modeling calculations on it.Then the four combined processes of sequence,selection,parallel,and loop are analyzed and modeled.Finally,a cloud service composition optimization calculation model for obtaining the optimal value is given.Second,analyze the bacterial foraging optimization algorithm,and improve the randomness of its optimization and the weak global search ability,we use chaotic mapping method to improve population generation,improve the chemotaxis process in combination with the global optimal bacteria,improve the replication process with genetic algorithm,make it no longer rely on the replication of excellent bacteria to achieve the replication process,add the influence of health and fitness to the migration process.Finally,the benchmark function was used to verify the improved effect.Third,for the given cloud service composition optimization model,the bacterial foraging optimization algorithm is used to solve the problem,and the bacterial location is coded to correspond to the service number in the cloud service combination path,in order to solve the combination of the best service quality.In the end,simulation was used to test the effect of the algorithm,and the improved bacterial foraging optimization algorithm to solve the cloud service combination result was compared with evolutionary algorithms to solve the cloud service composition optimization result,through convergence,robustness,effectiveness and algorithm execution Time to analyze the effect of the algorithm.