Abstract: A method for monitoring and management of a cloud-based computing system is provided. The method includes sending a first data stream to a first pod of a first worker node of a cloud-based computing system. First logs are received from the first pod of the first worker node, which are generated by the first pod while processing the first data stream. A first age of the first pod is determined. In response to the first age being less than a first age threshold, a first failure chance and a first failure timeline are determined for the first pod based on the first logs. In response to the first failure chance being greater than a first failure threshold, a first report is sent to a primary node of the cloud-based computing system, which includes the first failure chance, the first failure timeline, and a first template for the first pod.

Abstract: A system accesses a disk image of a first software container and collected sensor data for a computer server. The system sequentially analyzes the sequence of layers of the disk image, and generates, based on the sequential analysis of the sequence of layers of the disk image, an auto coding sequence. The auto coding sequence includes a sequence of instructions for creating a new disk image. The system determines, based on the collected sensor data and the sequential analysis of the sequence of layers of the disk image, a sequential list of software needed for the computer server. The system determines, using the sequential list of software needed for the computer server, a plurality of infra requirements for a new software container. The system generates the new software container and the new disk image using the auto coding sequence and the plurality of infra requirements.

Abstract: A memory stores a log that includes first, second, and third datasets each containing server information collected at different times. The processor forms vectors from the datasets, calculates distance vectors between them and uses the distance vectors and pairwise angles between distance vectors to predict a dataset for a first future time. When the first future time arrives, the processor collects the server information, stores it in a fourth dataset, and calculates a variance between that dataset and the predicted dataset. Using the variance, distance vectors, and angles, the processor determines a second predicted dataset corresponding to a second future time, from which it determines a server usage prediction. If the server usage prediction is below a decommission threshold, the processor decommissions the server. If the server usage prediction is above a capacity threshold, the processor generates a report advising an administrator to upgrade or remove projects from the server.

Abstract: A memory stores a log that includes first, second, and third datasets each containing server information collected at different times. The processor forms vectors from the datasets, calculates distance vectors between them and uses the distance vectors and pairwise angles between distance vectors to predict a dataset for a first future time. When the first future time arrives, the processor collects the server information, stores it in a fourth dataset, and calculates a variance between that dataset and the predicted dataset. Using the variance, distance vectors, and angles, the processor determines a second predicted dataset corresponding to a second future time, from which it determines a server usage prediction. If the server usage prediction is below a decommission threshold, the processor decommissions the server. If the server usage prediction is above a capacity threshold, the processor generates a report advising an administrator to upgrade or remove projects from the server.