Research On Solving Methods Of Multi-Objective Service Selection Problems For Large Scale Service Request

With the rapid development of service computing and cloud computing,a large number of service resources have been deployed on the network.Because of the convenience of network service access,more and more users use various kinds of service resources through the network to complete their business applications.The rapid growth of service resources has led to an increasing number of services offering similar functions.The frequent access of service resources makes the number of service requests rapidly increase at a certain time interval.This makes the service selection problem not only consider the quality of similar services,but also consider the change of service quality under large-scale requests.However,most of the current service selection solutions do not consider the change of service quality and the influence of service selection on large scale requests.Therefore,this dissertation aims at the current existing problems in this field.This dissertation studies the efficiency enhancement and effectiveness assurance of service selection based on mass service resources.In the process of service composition,in order to optimize the operation quality of the composite services at the execution stage,the process is often divided into two stages:one is the process design phase,two is the process instantiation stage based on service selection.In the process design stage,the abstract business process is designed according to the business logic.business processes include a set of tasks and their relationships.The service that implements the task function constitutes a service class.In the process instantiation stage based on service selection,the process of instantiation is to select a better service from the service class for each task in the abstract business process,and construct the combined service instance to meet the user's quality requirements.When composite service instance is facing large amount of user access,the requests have instantaneous high concurrency.At the same time,users have different quality requirements for service requests due to personalized requirements.This leads to the diversity of service requests.When a composite service instance is faced with a large number of users accessing continuously,the service requests forms a request flow.The request flow is dynamic.How to ensure the stability and maximization of the revenue of a composite service instance in the face of dynamically changing request flows is a problem worth studying.Therefore,this dissertation studies the four problems of high concurrency,diversity,flow characteristics and composite service profitability,and puts forward the corresponding solution methods.(1)In this dissertation,a graph based composite service selection model is constructed,and a multi-objective service selection method based on ant colony algorithm is proposed to solve the problem that one composite service instance is difficult to meet the needs of the users when composite services are facing large scale and high concurrent service requests.The method uses ant colony algorithm to search in graph,selects multiple composite service instances at one time,and achieves request distribution by load balancing to ensure the effective execution of large-scale and highly concurrent service requests.In order to improve the efficiency of the algorithm,the Pareto model is applied and the ant colony algorithm is used to solve multiple optimal solutions,i.e.Pareto solution set,at one time.On this basis,resource conflict detection is carried out to ensure the concurrency to meet the requirements.Experiments show that the efficiency of the proposed algorithm is improved effectively facing high concurrency of service requests.At the same time,it can ensure the effective execution of large-scale and highly concurrent service requests.(2)When composite services face large scale service requests,personalized service requests result in various personalized service requests should be satisfied at the same time.As a result,the selection process needs to be carried out many times and the efficiency of selection is reduced.Aiming at this problem,in this dissertation,a graph based parallel composite service optimization selection model is constructed,and on this basis a multi-objective service selection method based on parallel ant colony algorithm is proposed.This method first uses the clustering algorithm to cluster users'personalized requests,and forms multiple request categories to reduce the number of personalized requests.Then the parallel ant colony algorithm is applied to select services for all the request categories.Each ant colony is used to solve for a request category.Multiple ant colonies work concurrently.The conflict between the optimal solutions of different categories is solved through parallel communication strategy.Finally,a group of optimal solutions without conflicts are formed.Experiments show that this method can effectively improve the efficiency of solution and that at the same time,an effective balance between the scale and individuation of service requests is achieved.(3)Aiming at the quality optimization problem of service request flow,which is composed of continuous user requests,in this dissertation,a probability based solution structure is constructed,and on this basis,a multi-objective service selection method based on artificial bee colony algorithm is proposed.In this method,the quality constraint description method of service request flow is given,and a new solution structure is defined.The solution is composed of multiple composite service instances.Each combined service instance has a distribution probability.When the request comes,the request is distributed according to the distribution probability.According to this solution structure,the evaluation method satisfied by the request flow constraint is defined.Based on the evaluation of solutions,the constraint satisfaction problem is converted to the problem of composite service selection.Then,an artificial bee colony algorithm is used to give multiple composite service instances and their allocation probabilities at one time.The experiment shows that the method can effectively guarantee the constraint of the request flow,and the solution finding speed will not change with the size of the solution space,and it is basically suitable for large-scale problem solving.(4)In order to solve the problem of continuous optimization of composite service revenue for composite service providers in service request flow,in this dissertation,a selection model of composite services based on Markov decision process is built.On this basis,a multi-objective service selection method based on Lyapunov optimization theory is proposed.Firstly,the continuous optimization selection process is modeled as a Markov decision process.In this process,the request flow is fragmented by time slot and a buffer queue is set up for each service under each task in the composite service.When the request comes,the request under the current time slot is dispatched according to the current queuing condition of the composite service system.The dispatchment ensures that the buffer queue is not unlimited growth and the cost is minimal,and the user request can respond in a limited time.In this way,the continuous optimization selection problem of composite services is converted to request allocation problem.However,due to the large number of services,the problem of state space explosion is faced in the process of service request allocation.Therefore,applying Lyapunov queue stability theory,LBO algorithm is proposed.In the dispatching process,the time average operating cost is minimized on the premise of ensuring the stability of the buffer queue,the profit is maximized,and state space explosion problem is avoided.The experiment proves that the method effectively guarantees under the continuous request flow,maximizing the revenue of the composite service,shortening the average waiting time of the service request,and solving the problem of state space explosion.