The proper selection of parameters, kernel parameter g, penalty factor c, non-sensitive coefficient p of Support Vector Regression (SVR) model can optimize SVR's performance. The most commonly used approach is grid search. However, when the data set is large, a terribly long time will be introduced. Thus, we propose an improved grid algorithm to reduce searching time by reduce the number of doing cross-validation test. Firstly, the penalty factor c could be calculated by an empirical formula. Then the best kernel parameter g could be found by general grid search algorithm with the achieved c and a p-value selected randomly within a range. According to the achieved c and p, the grid search algorithm is used again to search the best non-sensitive coefficient p. Experiments on 5 benchmark datasets illustrate that the improved algorithm can reduce training time markedly in a good prediction accuracy.

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Today telecom network are growing complex. Although the amount of network performance data increased dramatically, telecom network operators require better performance on network performance data collection and analysis. Database is the important component in modern network management model. Since main memory database (MMDB) store data in main physical memory and provide very high-speed access, MMDB can suffice the requirements on data intensive and real time response in network performance management system. This paper presents a novel lightweight design on MMDB for network performance data persistence. This design improves data access performance in following aspects.  The data persistence mechanism employs user mode memory map provided by UNIX OS. To reduce the cost of data copy and data interpretation, the data storage format is designed as consistent with binary format in application memory. The database is provided as program library and the application can access data in shared memory to avoid the cost on inter-process communication. Once data is updated in memory, query application can get updated data without disk I/O cost. The data access methods adopt multi-level RB-Tree structure. In best case, the algorithm complexity is O(N). In worst case, the algorithm complexity is O(N*lgN). In real performance data distribution scenarios, the complexity is nearly O(N). The system approach has been tested in laboratory using benchmark data. The result shows the performances of the application fully meet the requirements of the product index. The CPU and memory consumption are also lower than network management system requirements

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