Abstract: A method includes receiving, by a computing device, metrics identifying vulnerabilities in an application; collecting, by the computing device, information related to the vulnerabilities; assigning, by the computing device, weights to the metrics using collected information; applying, by the computing device, a machine learning model on the weighted metrics; and generating, by the computing device, a predictive score for the vulnerabilities using the machine learning model.

Abstract: A processor may identify one or more pieces of code in a container environment. The one or more pieces of code may adhere to respective agreements. The processor may generate respective digital twins associated with the respective agreements. The processor may analyze the digital twins for multifarious obligations. The processor may provide the one or more pieces of code to one or more specific containers. The providing of the one or more pieces of code may adhere to the multifarious obligations.

Abstract: Techniques are disclosed for initializing an application composed of multiple software containers, where one or more of the software containers are associated with dependent software containers. A request is received to initialize the application. A dependency manager determines, from a map identifying associations between each of the software containers, whether a software container is associated with any dependent software containers. Upon determining that the software container is associated with dependent software containers, the dependency manager enables a notification setting in the software container. The dependency initializes the software container.

Abstract: Techniques are disclosed for initializing an application composed of multiple software containers, where one or more of the software containers are associated with dependent software containers. A request is received to initialize the application. A dependency manager determines, from a map identifying associations between each of the software containers, whether a software container is associated with any dependent software containers. Upon determining that the software container is associated with dependent software containers, the dependency manager enables a notification setting in the software container. The dependency initializes the software container.

Abstract: A method for a messaging engine to control messaging within a virtual machine having a heap includes creating a list of destinations associated with a messaging engine and receiving parameters associated with the heap. The message production rates are controlled using the parameters and heap usage. For example, the parameters may include a lower limit that indicates that message production should be decreased (i.e., throttled back) and a step-down value. Using the lower limit and the step-down value, intermediate throttle-back thresholds are created, and when the heap usage transitions from below one of the intermediate throttle-back thresholds to above the intermediate throttle-back threshold, the message production rate is decreased. Further, other parameters may include an upper limit and a step-up value, which are used to determine intermediate throttle-up thresholds that indicate when the message production rate may be increased (i.e., throttled up).

Abstract: A method for a messaging engine to control messaging within a virtual machine having a heap is disclosed. The messaging engine creates a list of destinations associated with a messaging engine and receives parameters associated with the heap. The messaging engine determines total heap usage and based on the parameters associated with the heap and the total heap usage, controls message production rates of producers associated with the destinations.