Cloud Computing Analysis and Optimization Based on Map-Reduce and Improved Ant Colony Optimization Algorithm

In order to realize rapid processing and accurate interpretation of big data, and to achieve reasonable scheduling and application of cloud computing resources, this paper proposes an integrated artificial intelligence processing and analysis model, combining Map-Reduce and the improved ant colony algorithm based on PSO. Firstly, it uses Map-Reduce to divide cloud computing tasks into several subtasks in a parallel way, and optimizes and reorganizes computing resources. Secondly, it employs PSO to help ant colony optimization get rid of local optimal solutions, thus improving ant colony optimization and obtaining the global optimal solution. Finally, it adopts this model to complete the fast processing and accurate interpretation of big data. Through the experimental simulation results, this integrated innovation model not only reduces the time needed by big data processing, but also improves the precision of big data artificial intelligence analysis.