Research On Internet-based Resource Sharing Model And Key Technologies

Resource-sharing theory is a hot issue in academic circles currently. With the development of Internet technology and the rich amount of network resources, efficient sharing of these resources based Internet has the potential wide range of applications in education, engineering, social, medical, economic, and management scopes. For example, in management scope, resource sharing technology can be applied to many other aspects such as knowledge acquisition, advertising marketing, decision-making, inter-organizational information exchange and communication. Four factors affecting the performance of network resource sharing systems are analyzed in this thesis, which are resource-sharing model, network topology, data storage and resource scheduling. On this basis, we select the four key issues to study. During the research, much work has been done. For the resource-sharing model, a hierarchical resource-sharing model is presented, which is in line with our organizational model of educational resources and can be used to guide the construction of educational resource sharing systems; for the Internet, RPC(k) and RPn(k) networks are constructed, both of which have many excellent properties and can improve the communication efficiency of resource sharing systems; for the network evolution, a deterministic complex network evolution model was established based on the Sierpinski fractal graph, which has compatible network structural characteristics with a number of practical network on the model degree distribution, clustering coefficient as well as diameter and can be used for the description reference of the research of resource-sharing network theory; for data storage problems, a replica creating strategy is presented, which can effectively solve the frequent replica creation and deletion issues caused by a weak storage ability of network nodes; for the resource scheduling problem, a particle swarm algorithm is proposed based on the reinforcement learning, which can provide method support for the decision-making of resource scheduling.The main achievements of this thesis can be summarized as follows:1. A hierarchical resource-sharing model is presented and the replica creating strategy is designed based on the model. For the resource sharing problem of primary and secondary education, a hierarchical educational resource grid model is proposed, which defines the function of nodes in each layer; by comparison with the European data grid, the characteristics of educational resource grid are analyzed; based on the hierarchical educational resource grid, the factors affecting the performance of replica creating strategies are analyzed, and then two parameters of network bandwidth and file size are introduced, a dynamic replica creating strategy (EDRS) is proposed; using of data grid simulation tool OptorSim to build a virtual environment of the educational resource grid, the performance of EDRS strategy, Caching-lru strategy, Caching-lfu strategy and strategy based on economic models are analyzed and compared; finally, effects of different strategies on grid system performance are analyzed by a comprehensive indicators. The results show that EDRS strategy in educational resource grid has a better system performance.2. Based on the RP(k) network, a resource-sharing network structure is constructed and a series of methods are studied to improve the quality of system services. For the distributed resource sharing issues, ranging from two angles of network topology and communication efficiency to explore methods and measures of reducing the network latency and improving network bandwidth utilization, it designs a structure topology of resource-sharing network system based on RP(k) network, which detailed expound the solution to realize this network structure. On this basis, a series of strategies to improve system quality of services including the node join / leave strategy, agency strategy, distributed resource retrieval strategy, and collaborative strategy for the node data are given. Finally, by theoretical analysis and comparison, the advantages of the RP(k) network and effectiveness of related strategies are confirmed.3. Two kinds of interconnection network model are established and its routing algorithms are discussed. Pertersen diagram has a good performance in parallel computing and distributed computing due to the nature of a short diameter and regularity. Two new extension methods of Pertersen are proposed based on the ring structure, and the RPC(k) and RPn(k) network are constructed. It studies the nature of the two networks, which not only has a regular and good scalability, but also has a smaller network diameter, a better grouped ability as well as a smaller cost of network construction more than the RP(k) network. The conditions of network diameter and grouped ability of RPC(k) and RPn(k) network better than two-dimensional Torus and the RP(k) network are analyzed. Based on the RPC(k) and the RPn(k) network, the routing algorithms are designed, which include point-to-point routing, one-to-all routing, permutation routing, and all-to-all routing. The study finds that their communication efficiency is significantly improved with corresponding algorithms of RP(k) network.4. A deterministic complex network evolution model is constructed, which makes the small-world network and the scale-free network into the same framework. It is discovered that a large number of real networks have shown small-world and scale-free features, such as the P2P resource-sharing networks, thereby complex network evolution model becomes a hot issue in academic circles. Based on the Sierpinski fractal pad, the two deterministic growth complex network model are constructed through the iterative way: small-world network model (S-DSWN), and scale-free network model (S-DSFN). The iterative generation algorithm of deterministic network models is given and their main topological characteristics are analyzed. Results show that the two models are compatible with a number of practical networks on characteristics of degree distribution, clustering coefficient and diameter. Finally, a unified deterministic model (S-DUM) is proposed, which makes the S-DSWN and S-DSFN into a framework. The model not only can be used for the description reference of research on the resource-sharing network theory but also provide a theoretical basis for relevant studies of complex networks. In particular, we find that these network models are Maximal planar graph.5. In order to improve the performance of the decision-making module in resource sharing systems, a particle swarm algorithm based on the reinforcement learning is studied. Modern optimization methods provide support to a number of system decision-making modules, so we study the global optimization evolutionary algorithm: particle swarm optimization (PSO). In the particle swarm algorithm, inertia weight as an important parameter can balance the relationship of the global and local search ability to improve the performance of the algorithm. An adaptive particle swarm optimization (RPSO) based on the reinforcement learning is presented. The algorithm looks different inertia weight adjustment strategies as an action collection of particles, by calculating the Q function values, examines the effect of particle multi-step evolutionary, selects the optimal particle evolutionary strategy, and adjusts the inertia weight dynamic to enhance the capacity of algorithms and find the global optimum. Several test results of classic functions show that: RPSO is able to obtain good performance, especially for multi-peak function.6. A job scheduling simulator of data grid is designed and realized, which provides an effective tool for the performance evaluation of relevant policy. The way to evaluate the performance of an algorithm is a lot of simulation experiments. For the evaluation of job scheduling strategies of data grid, the definition of data grid models and job scheduling process are summarized, the job execution time and costs of data grid are analyzed, and a design scheme of data grid job scheduling simulator is presented based on the simulator GridSim. The architecture, workflow and key technologies of job scheduling simulator are introduced. Finally, experiments show that the job scheduling simulator can satisfy the needs of the grid optimization theory and the purpose of finding optimal scheduling policy.