Theoretical Model And Storage Resource Allocating Algorithms For Cloud Download System

File distribution system is an important platform for efficient information dissemination on the Internet. With the rapid development of cloud-computing, cloud download system has emerged and quickly gains focus. The reservation-based file distribution service provided by cloud download system allows users to be offline or even shutdown, instead of keeping online after sending out their requests. Therefore, it could save the users a lot of time. According to users' requests, the cloud download system can rent computing, storage, and bandwidth resource in order to ensure the availability and also the retrieving speed of requested files.The cloud download system combining reservation service and cloud platform, has many novel features such as new interaction patterns between users and system, reusing files according to the statistics of reservation requests, and renting and managing cloud resources for improving efficiency and reducing service costs. However, existing resource allocation strategies in cloud download system have several severe problems, such as waste of storage resources, unfair service, and large overhead in resource provisioning.To address the above issues, this dissertation builds theoretical models and proposes resource assignment strategies by considering the novel features of the cloud download system. The main work and contributions in this dissertation are summarized as follows:(1)Considering interaction between user and system, this dissertation builds an interaction model, which theoretically characterizes the operation mechanism of reservation services in the cloud download system, and propose system response strategy, which decides the time when to notify the user and when to start to get the file. Firstly, based on the analysis of a single user downloading process, the theoretical interaction model is built to quantify all time elements of interaction and their relations. And the user time cost and the system storage time cost can be obtained from the model. Secondly, a multi-objective optimization problem is established. By solving it, response strategies are found to optimize the user experience or minimize system storage cost. Finally, based on the tradeoff between the user time cost and the system storage time cost, we proposed a response strategy to minimum required system storage cost with meeting the quality requirement of services to the needs of the user.(2)To deal with the incentive problem of driving the user and the system cooperation, we design cooperation mechanism to achieve efficient reuse of file in multi-user scenarios. Firstly, the multi-user model is built to achieve service cost when system reuses the file differently. Secondly, the utilities of user and system are analyzed in two cases that altruistic or selfish user. In both cases, we respectively propose mechanisms, in which the system provides difference services according to waiting time and quotation of user. Finally, by simulations and theoretical analysis, it is proved that the mechanism effectively reduces the cost of acquiring and caching file, and ensures the fairness for user.(3)To deal with the issue that the existing cache allocation algorithms did not consider characteristics of reservation service, this dissertation builds the storage capacity model to quantify required cloud cache size (i.e., lower limit of cloud storage requirement). Firstly, through meticulous investigation and statistical analysis of the challenge on designing cloud cache, we firstly put forward that the critical factors affecting cloud cache occupation are the deleting behavior of user and the validity of files. Secondly, through the massive data analysis and mining, we found the cloud cache storage features (such as the relation between the user requests'number and the cached file number, etc.) and the law of user deleting file. Finally, the cloud cache size demand model is established. The model establishes the relation among requests' number, the validity of files and cloud cache size. It provides a theoretical basis of designing cloud cache size and validity of files. Compared with the simulation based on real-trace, results show that the relative error of storage capacity model is less than10%.(4) Considering the cloud storage costs affecting the cloud computing cost, we build model to minimize the system resource cost, and propose the cloud cache management algorithm. The key parameters of the model are studied by a real large-scale commercial cloud download system. Based on the lower limit of cloud storage requirement, the model is designed to rent a cloud cache to minimize the sum of cloud storage and computing resources cost. On the basis of users'retrieving behavior characteristics found through the massive user behavior data analysis in the real-trace, we propose the cloud cache management algorithm, i.e. F-LRU. Finally, the results of simulations driven by massive real-trace prove that both the hit rate and bit-hit rate of F-LRU are higher than the LRU and SIZE algorithm.