Network Abnormality Location Algorithm Based on Greedy Monte Carlo Tree

Abstract

The cloud service providers manage very large data centers all over the world. When an abnormality occurs, various types of alarm information are triggered. The operation engineers need to quickly discover and locate all the abnormalities. To solve the problems of computing-intensive, non-real-time, and inaccurate abnormal detection and location algorithm, we propose to improve the Monte Carlo Tree Search (MCTS) based on the greedy algorithm by: 1) improving the selection of the next node in MCTS by using greedy algorithm and searching the best node with depth-first method; 2) adopting the sparse matrix to store the record of the 5-dimensional log files, then employing the subscript file to record the subscript of the 5-dimensional array, and using the subscript to access the sparse matrix to save memory space and searching time; 3) reducing calculations by pruning some branches based on the observation that the optimal node combination of present layer must belong to the search space of the best layer combination of the previous layer. The experimental results show that GMCTS algorithm reduces 40% computation time than the HotSpot in 5D data, and the correct positioning efficiency is up to 96.1%.