Abstract: A method includes determining whether a code update for the service is available at a central repository of the computing environment and, in response to determining that the code update is available, retrieving the code update from the central repository. The method further includes performing a modification of the service in view of the code update.

Abstract: A method includes determining whether a code update for the service is available at a central repository of the computing environment and, in response to determining that the code update is available, retrieving the code update from the central repository. The method further includes performing a modification of the service in view of the code update.

Abstract: A method includes monitoring, during a first time interval, traffic associated with one or more applications executed by a cluster of compute nodes and determining, in view of the traffic associated with the one or more applications during the first time interval, that the traffic is predicted to exceed a capacity threshold of the cluster of compute nodes at an end of a second time interval. The method further includes initiating startup of an additional compute node to be added to the cluster of compute nodes for executing replicas of the one or more applications.

Abstract: One or more metrics associated with performance of a serverless function chain are received. The one or more metrics are used by a reinforcement learning (RL) model to tune the serverless function chain.

Abstract: Events to be executed by one or more serverless functions are received from an events queue. A determination that a first set of the events are to be executed by one or more serverless functions at a local host system and a second set of the events are to be executed at one or more serverless functions at a remote host system is made. The first set of the events are directed to the one or more serverless functions at the local host system and the second set of the events are directed to the one or more serverless functions at the remote host system.

Abstract: A method includes detecting, by a webhook controller, an inference serverless function invocation. The method further includes determining that the inference serverless function can be optimized. The method further includes generating an optimized version of the inference serverless function using a graph compiler, in response to the determining. The method further includes replacing, by a processing device of the webhook controller, a storage volume in an init container of the inference serverless function with a new storage volume comprising the optimized version of the inference serverless function.

Abstract: A method includes determining whether a code update for the service is available at a central repository of the computing environment and, in response to determining that the code update is available, retrieving the code update from the central repository. The method further includes performing a modification of the service in view of the code update.

Abstract: One or more metrics associated with performance of a serverless function chain are received. The one or more metrics are analyzed by a reinforcement learning (RL) model to determine a tuning factor in view of a goal provided to the RL model. The tuning factor is transmitted to a serverless function controller, wherein the serverless function controller is to utilize the tuning factor to tune the serverless function chain.