Abstract: Fog computing systems are provided comprising edge-nodes and middle-nodes between edge-nodes and cloud-node. These nodes form a hierarchical structure with each cloud, middle node having children nodes, and each middle, edge node having a parent-node. Each edge-node receives data from sensors, assigns reception-timestamp to each data indicating when data has been received to produce series of timestamp-ordered data, trains local model through machine-learning based on said series of timestamp-ordered data, and sends said series to parent-node of the edge-node. Each middle-node collects series of timestamp-ordered data from children nodes of the middle-node, trains supra-local model through machine-learning based on said collected series of timestamp-ordered data, and sends said collected series to parent-node of the middle-node. Parent-children structures, edge-nodes and middle-nodes for such fog computing systems are also provided.

Abstract: Techniques regarding anomaly forecasting are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a forecast component that can determine a probability of a computer application executing an anomaly state based on a probabilistic graph that is incrementally updated while the computer application is running.

Abstract: Fog computing systems are provided comprising edge-nodes and middle-nodes between edge-nodes and cloud-node. These nodes form a hierarchical structure with each cloud, middle node having children nodes, and each middle, edge node having a parent-node. Each edge-node receives data from sensors, assigns reception-timestamp to each data indicating when data has been received to produce series of timestamp-ordered data, trains local model through machine-learning based on said series of timestamp-ordered data, and sends said series to parent-node of the edge-node. Each middle-node collects series of timestamp-ordered data from children nodes of the middle-node, trains supra-local model through machine-learning based on said collected series of timestamp-ordered data, and sends said collected series to parent-node of the middle-node. Parent-children structures, edge-nodes and middle-nodes for such fog computing systems are also provided.