Abstract: A customer blockchain data store is provided. An exemplary method comprises obtaining a blockchain associated with a given customer of an enterprise having multiple customer communication channels, wherein the blockchain comprises transaction data for the given customer with the customer communication channels; obtaining new transaction data for the given customer for a given one of the customer communication channels; providing the new transaction data for the given customer to additional customer communication channels; receiving a validation of the new transaction data from the additional customer communication channels based on one or more predefined validation criteria; and storing the validated new transaction data for the given customer in the blockchain associated with the given customer.

Abstract: One example method includes identifying a changed microservice whose code has been changed, mapping, for each microservice in a group of microservices that includes the changed microservice, microservice dependencies associated with the changed microservice, determining, for each microservice in the group, a relative risk that the microservice will be adversely affected by the change to the code of the changed microservice, and based on the respective relative risks, generating a test order indicating an order in which the microservices in the group will be tested.

Abstract: One example method includes collecting telemetry data for each of a group of virtual machines (VM), and each of the VMs is associated with a user, collecting usage data for each of the VMs, creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user, creating, for each user, a user profile that is based on the user profile definition for that user, clustering the users based on similarity of their respective user profiles, and generating a recommended VM hardware configuration for a VM of one of the users.

Abstract: Techniques are provided for data-driven virtual machine restoration. In an example, a VM crashes and is to be restored. There can be multiple restoration paths that can be used to recover the VM (e.g., various source locations where a recovery image is stored, various recovery images, and various target locations where a VM can be restored to). A trained prediction model can analyze these various restoration paths and predict which restoration path will have a quickest time to recovery, to minimize a time that the VM is unavailable.

Abstract: Techniques are provided for data-driven organization adjustment. In an example, a service mesh of microservices of a platform can be determined. An organizational chart of workers in an organization that maintain the microservices can be determined. Which workers manage which microservices can be determined from a repository in which code for the microservices is maintained. Then, the organizational chart can be analyzed to determine whether workers are organized in a way that meets policies based on which microservices they manage. For example, a policy can be that all workers who manage a given microservice should report to the same manager in the organization.

Abstract: One example method includes collecting telemetry data for each of a group of virtual machines (VM), and each of the VMs is associated with a user, collecting usage data for each of the VMs, creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user, creating, for each user, a user profile that is based on the user profile definition for that user, clustering the users based on similarity of their respective user profiles, and generating a recommended VM hardware configuration for a VM of one of the users.

Abstract: Techniques are provided for generating workspace recommendations based on prior user ratings of selected workspaces, as well as other similar selections. One method comprises obtaining user workspace ratings provided by a user; calculating a first workspace recommendation score for workspaces that the user previously rated based on the obtained user workspaces ratings; calculating a second workspace recommendation score for additional workspaces that are: (i) similar to workspaces previously rated by the user based on a predefined workspace similarity metric, and/or (ii) selected by similar users, based on a predefined user similarity metric; and recommending workspaces for the user based on the first workspace recommendation score and the second workspace recommendation score.

Abstract: Techniques are provided for chaos engineering in microservices using a service mesh. In an example, a plurality of microservices can operate together as part of a software as a service product. A graph of the service mesh of the plurality of microservices can be determined. From that graph, weight can be assigned to the respective nodes. Those weights can be used to determine a probability of where chaos is introduced in the corresponding microservice architecture as part of chaos testing.

Abstract: Systems and methods for detecting cost anomalies in a data protection system. Data is collected for assets of a data protection system operating in a cloud. The data often relates to cost and may constitute time series. The time series are then analyzed by performing a fitting competition using multiple models. The best fitting model is selected and the residuals are analyzes to find outliers and produce a normal zone for the signal. The outliers can identify cost anomalies that may reflect the health of the data protection system.

Abstract: Techniques are provided for facial recognition using a high probability group database. One method comprises maintaining (i) a first database of facial images of individuals, and (ii) a second database of facial images comprising a subset of the individuals from the first database based on a probability of individuals appearing in sequences of image frames at a given time; applying a face detection algorithm to sequences of image frames to identify one or more faces in the sequences of images; and applying a facial recognition to at least one sequence of image frames using at least the second database to identify one or more individuals in the at least one sequence of image frames. The second database is comprised of facial images of: (i) individuals from multiple angles; (ii) individuals that appeared in prior image frames; and/or (iii) individuals that appeared in an image frame generated by a plurality of cameras.

Abstract: Techniques are provided for optimizing hybrid cloud usage. In an example, a cloud spot manager can manage spot virtual machine instances for on-premises systems for multiple different customers. Where a customer requires more resources on its system, the cloud spot manager can terminate another customer's spot virtual machine on that system. Where a customer needs more resources than can be provided by its system, the cloud spot manager can determine another customer system to locate the first customer's spot virtual machine, and instantiate that virtual machine there.

Abstract: One example method includes receiving a quote for provision of goods and/or services, and the quote concerns a particular account, receiving information concerning characteristics of the account identified in the quote, receiving information concerning characteristics of the goods and/or services specified in the quote, generating a probability that the quote will be approved by the account, and the probability is generated based on the characteristics of the account and the characteristics of the goods and/or services specified in the quote, when the probability is below a threshold, generating an adjusted quote based on input received, and generating an updated probability based on the adjusted quote.

Abstract: Techniques are provided for chaos engineering in microservices using a service mesh. In an example, a plurality of microservices can operate together as part of a software as a service product. A graph of the service mesh of the plurality of microservices can be determined. From that graph, weight can be assigned to the respective nodes. Those weights can be used to determine a probability of where chaos is introduced in the corresponding microservice architecture as part of chaos testing.

Abstract: Data protection based on cybersecurity feeds is described. A system receives cybersecurity feed content from a cybersecurity feed. If the cybersecurity feed content is relevant to data associated with an organization, the system evaluates a cybersecurity threat based on the cybersecurity feed content. The system selects at least one data protection policy, from multiple data protection policies, which corresponds to the evaluated cybersecurity threat. The system implements the selected at least one data protection policy.

Abstract: Embodiments for dynamically allocating journal space for Do streams across multiple applications. A shared Do stream process has a dynamic block allocation component that provides a certain amount of buffering of a data flush for an application, using space that would normally be allocated for, but unused by, other applications, thus preventing the need for one or more of the applications to move to fast-forward mode when possible. Certain machine learning techniques are used in order to predict the required Do stream for each application according to past experience with the application, and this prediction is used to intelligently allocate Do Streams between the different applications.

Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: Recommending applications is disclosed. An application graph is disclosed that represents applications. Each node of the graph corresponds to an application and edges relate applications that can handle the same file type. When an input application is identified, the graph can be used to recommend other applications that may be a suitable replacement for the input application. The recommendation is based on the graph and its links and on characteristics of the organization.

Abstract: Described is a system for creating an intelligent testing queue for improved quality assurance (QA) testing of services (or microservices). The system may perform a graphical analysis of interactions between services to derive testing constraints. For example, the system may monitor services to identify interactions (e.g. API accesses) between the services, and store the interactions as a directed graph representation. The system may traverse the directed graph (e.g. via a breadth-first search) to determine service dependencies. Based on the probability of failure for the testing operations and the service dependencies, the system create a specialized testing queue. By performing testing operations according to the specialized queue, the system may improve certain metrics associated with QA processes such as mean time to failure (MTTF) and mean time to diagnose (MTTD).