Data Placement Optimization Strategy For Social Networks In Cloud Computing

Cloud computing is a model of computing for resource sharing which based on Internet service.Cloud computing platform focus on storage and data processing,which provides resource of storage and computing to user on-demand while sharing massive distributed resources.The online social network platform is a kind of service platform that storing and processing by computer.In online social networks,service providers need to allocate resource with reasonable for each user according to the connection and location between users.With the development of social networking platform in cloud,the requirement of resource scheduling and quality of service constantly improving.Therefore,how to make the allocation scheme of resource more reasonable is an important research direction in cloud.The online social network service is a product that combines the resource of storage and computing in cloud.It is a major task for online social networks,which make the resource allocation reasonable and improve the quality of service.In recent years,the online social networks has achieved development rapidly.As the number of user increases,massive data needs to store.Thus,how to allocate resource of storage for every user is an urgent problem for social network service providers.As the same time,it is also important to store these data reasonable to reduce the user's access time and improve user's social experience.In order to solve the problem,social network service providers can allocate resources reasonable for each user with intelligent algorithms.Due to the small size of early cloud,the problem of social network service provides most concerned is the cost of data storage.Therefore,the target of data placement optimal algorithm is to reduce the cost of data storage in early cloud.With the development of social networks and cloud computing,not only the requirement of social experience get higher,but also the social network service provider turn the object of optimization to reducing the transmission load between multiple servers.Therefore,how to combine the three goals of data storage cost,transmission load and storage load effectively has become the focus of current research.Intelligent optimization algorithm for data placement used in online social networks is genetic algorithm commonly.Genetic algorithm is an evolutionary search starting from an initial population.Due to it has no affected by the specific fitness function,searching the optimal solution in parallel with a certain probability that start from a number of points.The advantage is to avoid getting into a locally optimal solution.Genetic algorithm could select,cross,and mutate the chromosomes at a certain probability.Due to the genetic algorithm can extend the search space to the whole problem space,and it has global optimization performance.Compared with the traditional optimal algorithm,the genetic algorithm has good robustness,adaptability and global optimization.The main work of this article is as follows:1.In the light of the disadvantages of the online social network data storage cost to optimize the traditional data placement strategy by using genetic algorithm.On the premise of ensuring the user experience,reducing the storage cost of social network providers.2.Reducing the transmission load between servers on the premise of optimizing the cost of data storage.Experiments demonstrate that the algorithm can optimize the data storage and data transmission in the online social network.It can provide better data management methods for social network service providers.3.Based on optimizing the data transmission volume between multiple servers,we optimize the load balance of server storage and eliminate the imbalance of storage load between servers.So as to improve the stability of the system.This paper makes an in-depth study of the data placement problem in the current online social network.The intelligent algorithm used to study the more important target of storage cost,data transmission and load balance in social network data placement.Finally,experiments demonstrate that the algorithm can reduce the data storage cost,the data transmission and load balance effectively between multiple servers in the social network.