The Analysis of Distributed Computing Systems with Machine Learning

The distributed computing system is a hot research field. Deep learning, the Internet of Things, and other technologies are rapidly advancing, necessitating better levels of computation, storage, and communication efficiency for many jobs and applications. The advanced distributed computing model, represented by cloud computing and edge computing, has attracted the attention of researchers. Firstly, this paper reviews the development of distributed computing systems. Then the critical technologies of the distributed computing system, including MSG-based system, MapReduce-like system, and graph computing system, are introduced. Then, this paper presents two typical frameworks of distributed computing systems, including cloud computing and edge computing, and compares and analyzes the latest progress of cloud computing and edge computing in machine learning. In addition, this paper discusses the development prospect and challenges of the distributed computing system, including increasing the scale of training, developing more complex structure other than MEA and ways to make the cloud more efficient.