Abstract: A computing system receives, from a client device, a first request for access to a single page application associated with the computing system. A gateway of the computing system intercepts the first request before it reaches a shell service associated with the single page application. The gateway sends a second request for the single page application to the shell service. Based on the single page application request, the gateway determines that the shell service is experiencing a failure. Responsive to determining that the shell service is experiencing a failure, the gateway retrieves, from a content delivery network, a uniform resource locator (URL) associated with a time-lapse hypertext markup language (HTML) of the single page application. The gateway redirects the client device to the time-lapse HTML of the single page application using the URL.

Abstract: A computing system receives, from a client device, a first request for access to a single page application associated with the computing system. A gateway of the computing system intercepts the first request before it reaches a shell service associated with the single page application. The gateway sends a second request for the single page application to the shell service. Based on the single page application request, the gateway determines that the shell service is experiencing a failure. Responsive to determining that the shell service is experiencing a failure, the gateway retrieves, from a content delivery network, a uniform resource locator (URL) associated with a time-lapse hypertext markup language (HTML) of the single page application. The gateway redirects the client device to the time-lapse HTML of the single page application using the URL.

Abstract: API gateway log data of HTTP response information is collected and aggregated for each API service. The log data is streamed and formed into a feature vector. A model training module trains a neural network model based on the feature vector and model metadata. The model training module also retrains the neural network model based on a new feature vector. Neural network model artifacts are stored and used to detect anomalous patterns and generate alerts of the detection.

Abstract: A computing system receives, from a client device, a first request for access to a single page application associated with the computing system. A gateway of the computing system intercepts the first request before it reaches a shell service associated with the single page application. The gateway sends a second request for the single page application to the shell service. Based on the single page application request, the gateway determines that the shell service is experiencing a failure. Responsive to determining that the shell service is experiencing a failure, the gateway retrieves, from a content delivery network, a uniform resource locator (URL) associated with a time-lapse hypertext markup language (HTML) of the single page application. The gateway redirects the client device to the time-lapse HTML of the single page application using the URL.

Abstract: Systems and methods for routing requests in an application programming interface (API) gateway are disclosed. An example method may include receiving a request from a client at the API gateway, requesting routing information for the request from a routing service associated with the request, receiving the requested routing information from the routing service, the routing information determined based at least in part on the request and a client state associated with the request, and forwarding, from the API gateway, the request to a destination based on the routing information.

Abstract: API gateway log data of HTTP response information is collected and aggregated for each API service. The log data is streamed and formed into a feature vector. A model training module trains a neural network model based on the feature vector and model metadata. The model training module also retrains the neural network model based on a new feature vector. Neural network model artifacts are stored and used to detect anomalous patterns and generate alerts of the detection.