Abstract: Example methods and systems are provided for simulation-based cross-cloud connectivity checks. One example method may include injecting a connectivity check packet in a first cloud environment, and obtaining first report information associated with a first stage of forwarding the connectivity check packet from one or more first observation points in the first cloud environment. The method may also comprise: based on configuration information associated with one or more second observation points in the second cloud environment, simulating a second stage of forwarding the connectivity check packet towards a second virtualized computing instance via the one or more second observation points. The method may further comprise: generating second report information associated with the simulated second stage to identify a connectivity status between a first virtualized computing instance and the second virtualized computing instance based on the first report information and the second report information.

Abstract: Methods and systems predict parameters in a dataset of an identified piece of (“information technology”) IT equipment. An automated method identifies datasets IT equipment in a same category of IT equipment as a piece of IT equipment identified as having incomplete dataset information. Each dataset of IT equipment parameters is used to construct generalized linear models of different classes of IT equipment within the category of IT equipment. The class of the identified IT equipment is determined. A predicted equipment parameter of incomplete information of the identified piece of IT equipment is computed using the generalized linear model associated with the class. The predicted equipment parameter can be used to complete the dataset of the identified piece of IT equipment.

Abstract: Disclosed are various embodiments for workflow error handling for device driven management. A workflow can be received from a management service by a management agent. The workflow can define a sequence of actions to be implemented by the management agent on a client device and a set of error conditions associated with individual actions in the sequence of actions. The management agent can then process the individual actions in the sequence of actions defined by the workflow. Subsequently, the management agent can monitor the individual actions to determine whether the individual actions trigger an error condition in the set of error conditions. Finally, in response to a determination that the individual actions triggered the error condition in the set of error conditions, the management agent can perform an error response specified by the workflow.

Abstract: Example methods are provided to deploy an Internet of Things (IoT) solution in a hybrid environment. The methods include deploying a first agent application on a first edge gateway of a first vendor by the first edge gateway. The first agent application is configured to collect a first set of information associated with the first edge gateway. The methods include deploying a second agent application on a second edge gateway of a second vendor by the second edge gateway. The second agent application is configured to collect a second set of information associated with the second edge gateway. In response to a determination of a first virtualized computing environment on the first edge gateway or a second virtualized computing environment on the second edge gateway fulfils a first requirement of a template to deploy the IoT solution, the methods include deploying the IoT solution in the first virtualized computing environment, the second virtualized computing environment, or both.

Abstract: Certain aspects of the present disclosure relate to methods and systems for evaluating a first command line interface (CLI) input of a process. The method comprises examining the first CLI input and selecting a first clustering model corresponding to the process, wherein the first clustering model is created based on a first clustering configuration and a first feature type combination. The method further comprises creating a first feature combination for the first CLI input based on the first feature type combination, evaluating the first CLI input using the first clustering model and the first feature combination, wherein the evaluating further comprises determining a similarity score corresponding to a similarity between the first feature combination and the one or more clusters, and determining whether or not the first CLI input corresponds to normal behavior based on the similarity score.

Abstract: Techniques for quantizing training data sets using machine learning (ML) model metadata are provided. In one set of embodiments, a computer system can receive a training data set comprising a plurality of features and a plurality of data instances, where each data instance includes a feature value for each of the plurality of features. The computer system can further train a machine learning (ML) model using the training data set, where the training results in a trained version of the ML model, and can extract metadata from the trained version of the ML model pertaining to the plurality of features. The computer system can then quantize the plurality of data instances based on the extracted metadata, the quantizing resulting in a quantized version of the training data set.

Abstract: Container images are managed in a clustered container host system with a shared storage device. Hosts of the system include a virtualization software layer that supports execution of virtual machines (VMs) in the hosts, and one or more VMs have implemented therein a container engine that supports execution of containers within the respective VMs. Deploying a container in a first VM includes creating a virtual disk in the storage device, storing a container image in the virtual disk, mounting the virtual disk to the first VM, and updating a metadata cache to associate the container image to the virtual disk. Deploying the container in a second VM executed in a host different from a host in which the first VM is executed, includes checking the metadata cache to determine that the container image is stored in the virtual disk, and mounting the virtual disk to the second VM.

Abstract: A rollback can be performed after completing an upgrade to components of a virtualized computing environment. When the upgrade is performed, an upgrade bundle having rollback scripts is provided to edges, hosts, and managers in the virtualized computing environment that are to be upgraded. When a rollback is to be performed, the rollback scripts are executed, and the components are rolled back in a reverse order relative to their upgrade order. Data and configuration checking are performed to validate the results of the rollback.

Abstract: Systems and methods can implement a review process to evaluate changes to target code as part of development cycles for a continuous integration, continuous deployment pipeline for software-based products. The system can aggregate data and determine if the target code has been modified preliminarily and then intelligently determine where further review is needed before the changes are permanently implemented. To do this, a changeset including the preliminarily changed target code can be obtained from the aggregated data. The changeset can be tested with a prediction model based on feature data that characterizes aspects of a coding process carried out to generate the preliminary modification. The prediction model can provide an activation recommendation for the preliminary modification based on a plurality of risk factors determined from the testing. The prediction model can be trained, continuously, with training data that includes a plurality of data artifacts resulting from a code build processes.

Abstract: Machine learning-based techniques for representing computing processes as vectors are provided. In one set of embodiments, a computer system can receive a name of a computing process and context information pertaining to the computing process. The computer system can further train a neural network based on the name and the context information, where the training results in determination of weight values for one or more hidden layers of the neural network. The computer system can then generate, based on the weight values, a vector representation of the computing process that encodes the context information and can perform one or more analyses using the vector representation.

Abstract: Examples herein describe systems and methods for concealing internal applications that are accessed over the internet. A user device can select a remote internal application to access using a client. The user device can send an access request to an open listening port of an access server. The access server can be a gateway and proxy to the internal application, which can reside elsewhere. The access server can open a different randomized access port for establishing the connection by proxy to the internal application. The port number for the access port can be identified in the access request at the listening port. The access server can open the access port for a short time interval. The connection can be made through the access port during that time interval. A firewall can then close the access port but maintain an established connection between the user device and the internal application.

Abstract: The present disclosure describes secured interprocess communication (IPC). The operating system traps application-level IPC calls to an IPC agent, which handles the IPC call. The IPC agent executes in a trusted execution environment so that communications between the applications involved in the IPC are secure. Since processing of IPC by the IPC agent bypasses the operating system, IPC remains secure despite any attacks against the operating system code.

Abstract: Various examples for providing neural network models to client devices are described. A management application can cause a training environment to be created for training a neural network using enterprise data authorized by a client device. The management application can cause the client device to send the enterprise data to the training environment. The management application can cause a training application running in the training environment to create a neural network model using the enterprise data. The management application can send a neural network model to the client device.

Abstract: A next generation antivirus (NGAV) security solution in a virtualized computing environment includes a security sensor at a virtual machine that runs on a host and a security engine remote from the host. The integrity of the NGAV security solution is increased, by providing a verification as to whether a verdict issued by the security engine has been successfully enforced by the security sensor to prevent execution of malicious code at the virtual machine.

Abstract: A computerized method for securing an application based on auto-learning and auto-mapping of application services and application programming interfaces (APIs), includes the step of providing a distributed application security mesh system. The method includes the step of implementing a learning mode of the distributed application security mesh system. This includes learning a set of services and a set of APIs that are used by the set of services to interact with each other. The service comprises an application service. This includes creating a record of the set of services. The record comprises a list of services that are currently active in a specified environment of the distributed application security mesh system, and a list of interactions of the services. The method includes the step of implementing an enforcing mode of the distributed application security mesh system.

Abstract: An example method is provided to deploy an application in multiple cloud computing environments. The method may comprise a computing system generating a first request to deploy an application in a first cloud computing environment according to a first deployment plan and a second request to deploy the application in a second cloud computing environment according to a second deployment plan. The method may comprise selecting, from multiple communication components configured on the computing system, a first communication component to communicate with a first orchestration node in the first cloud computing environment and a second communication component to communicate with a second orchestration node in the second cloud computing environment. The method may further comprise sending the first request to the first orchestration node via the first communication component, and the second request to the second orchestration node via the second communication component.

Abstract: Described herein are systems, methods, and software to generate user interfaces to indicate software-defined storage information in view of available hardware resources. In one example, a user interface service may obtain enclosure information associated with enclosures in a computing environment and may determine a storage overview for the plurality of enclosures based on the enclosure information. The user interface service may further determine a function for each storage device in the storage overview in relation to a software-defined storage configuration for the computing environment and generate a user interface to indicate the storage overview with the function for each storage device in the plurality of enclosures.

Abstract: Example methods and systems are provided for network diagnosis. One example method may comprise: detecting an egress packet and determining whether each of multiple network issues is detected for the egress packet or a datapath between a first virtualized computing instance and a second virtualized computing instance. The method may also comprise: generating network diagnosis code information specifying whether each of the multiple network issues is detected or not detected; generating an encapsulated packet by encapsulating the egress packet with an outer header that specifies the network diagnosis code information; and sending the encapsulated packet towards the second virtualized computing instance to cause a second computer system to perform one or more remediation actions based on the network diagnosis code information.

Abstract: Example methods and systems for intent-based network virtualization design are disclosed. One example may comprise: obtaining configuration information and traffic information associated with multiple virtualized computing instances, processing the configuration information and traffic information to identify network connectivity intents and mapping the network connectivity intents to a logical network topology template. Based on a switching intent, a first group may be assigned to a logical network domain and the logical network topology template modified to include a logical switching element. Based on a routing intent, the logical network topology template may be modified to include a logical routing element. A logical network may be configured based on the modified logical network topology template to satisfy the switching intent and routing intent.

Abstract: An asynchronous state machine replication solution in a system of replicas includes executing multiple instances of a consensus protocol, referred to as leader-based views (LBVs) in each replica, where each replica is a leader participant in one of the LBV instances. Each replica drives a decision based on the consensus being reached among the LBV instances, rather than relying the expiration of timers and view changes to drive progress.