Research On Strategy Of Data Replica Placement For Geo-distributed Cloud Storage Services

With the rapid development and the widespread use of cloud computing in various applications, as a basis support for cloud computing, the cloud storage has become a research hotspot in this field. The cloud storage technique is responsible for putting the stored resources in the datacenters of clouds for users to access. Cloud storage system has the characteristics of reliability, scalability, and fast data access, and these features of systems are closely related with the management of data replicas. Existing results studied on the optimization methods about data layout in cloud computing environments mostly from different objectives, such as considering energy consumption of systems and quality of service of users.To meet the users’ requirement, most cloud services choose to run on the multiple geographically distributed(geo-distributed) datacenters. In geo-distributed cloud storage service, users choose these clouds to back up their data and selectively share with others. And the data storage is the major monetary expenditure while the ultimate goal is to provide users with a better user experience. Considering regional distribution of the locations of users and explosive growth of users, it’s more efficient for users to replicate data to many distributed clouds to transmission time of accessing data to obtain a better user experience. However, data replicas also brings a problem of higher storage cost. Thus, making a better trade-off between storage cost and user experience is a challenge faced by cloud storage service. We need to propose effective data replica placement strategies, which need to guarantee less cost overheads while taking user experience into account.In recent years, the convenience of the mobile Internet promotes the rapid increase in the number of mobile users. More users will usually backed up their data to the cloud storage center, anytime and anywhere. Research on mobile cloud storage has gradually been concerned. However, mobility also brings new challenges, for instance, the use of mobile cloud storage has promoted the interactions between users for data, flexibility of users’ locations affects the user experience etc. Thus, the system need consider the mobility of cloud storage service when placing data. Because users can be seen as a main part of cloud storage service, which makes it necessary for the system to consider user access features such as users priority and activeness when storing data replicas. How to cope with mobile Internet, and consider the effect of user characteristics on replica placement is a problem to be solved.This article focuses on the geo-distributed cloud storage services. According to the abovementioned questions and based on user behavior characteristics, the following work is as follows:(1). In this paper, we modeled the optimization of data replica placement in geo-distributed cloud storage service mainly from the perspective of cloud service providers. We both take the data storage cost and user experience into account. This paper not only considers the geographical distribution of cloud storage centers, but analyzes the access features of users in detail. Combined with practical application, user files may be shared with other users that can be seen as a link to establish contacts between users. Based on these, the impact of user behavior on the storage of data has been mainly described.(2). We modeled the problem to do a tradeoff between storage and user experience, formulated the optimization objective function of data placement, and then designed a multiple replica adjustment algorithm. Based on the characteristics of users and cloud storage centers, the algorithm can be used to regularly optimize the overall data storage of systems.(3). When placing replicas to clouds, we considered the communications between users and clouds, between users, and analyzed some features of social-aware cloud storage services. We presented a replica placement algorithm(DDRP) for social-aware service, which can store data replica dynamically based on the current information about users’ state and the relationship between users. Leveraging the algorithm, a better replica placement scheme to choose which cloud to place data can be received.Simulation results show that, the RPA algorithm can reduce storage cost about 45% after adjusting replica while not affecting user experience. Compared with other replicas placement method, the proposed DDRP algorithm in our paper can ensure a better user experience performance while reducing storage costs. The overall cost of storing data replica is 40% less than other schemes, which demonstrates its effectiveness.