Abstract: An embodiment performs, by an attribute value substitution engine, key-value extraction on infrastructure as code. The embodiment assigns, by the attribute value substitution engine, an attribute in a plurality of attributes to a value in the extracted key-value. The embodiment computes, by the attribute value substitution engine, a plurality of similarities between the plurality of attributes and a cloud resource. The embodiment groups, based on the plurality of similarities, the plurality of attributes into a group of attributes. The embodiment substitutes, by the attribute value substitution engine, the attribute with another attribute, the attribute and the another attribute belonging to the group of attributes.

Abstract: A computer-implemented method performs dynamic filtering within a service mesh. While transferring data from one microservice to another microservice, each microservice evaluates a level of trust with each upstream and downstream microservice based on the level of trust score. Each microservice evaluates which data should be filtered while transferring data to downstream microservice and which data should be filtered while receiving data from upstream microservice.

Abstract: A processor may receive information associated with resources and edges from Infrastructure as Code (IaC). The processor may assemble a knowledge graph. The knowledge graph may be a controlling root graph for infrastructure code that contains the resources and edges from the IaC. The processor may determine, based on global usage statistics of the resources, probabilities of going from one resource to another resource. Going from one resource to another resource may be an edge. The processor may generate a recommendation for a next response.

Abstract: Embodiments of the present invention provide an approach for verifying a security and compliance standard of a multi-cloud environment based on a multi-cloud architecture diagram. Specially, the multi-cloud architecture diagram is parsed to verify the security and compliance standard checks. A list of resources and relationships are extracted from the multi-cloud architecture diagram and compared against an expected security standard compliance level. Any resources attributing non-compliance of the expected compliance level are identified by comparing the list of resources and relationships from the multi-cloud architectural diagram in a recursive and continuous manner. Only when security and compliance standard checks are satisfied/passed are the cloud resources deployed.

Abstract: A method, computer system, and a computer program product are provided for establishing security measures for a content. In one embodiment, the method comprises identifying at least a microservice chain in a network computer. Each microservice chain has more than one microservice linked to one another and each microservice includes a plurality of applications bundled together. Any use profiles associated with the microservice chain are identified and all data entry points into each microservice are determined. Each microservice is analyzed to predict when each microservice in the chain will provide a confidential output. Once any microservice chain is deemed to be providing a confidential output, any predicted exits in the microservice deemed to be generating an output content are upgraded so that all predicted exists provide a confidential output.

Abstract: Embodiments of the present invention provide an approach for dynamically harmonizing a management configuration of a cloud environment in a cloud environment based on policy or best practices updates. Specially, the system and method provide for an automatically evolving Infrastructure as Code (IaC) based on policy, best practices changes, and a recommendation service to assist in updating IaC configuration code in order to prevent a potential service request failure.

Abstract: Described are techniques for reference implementation of cloud computing resources. The techniques include receiving specifications for a computing resource flow to implement a computing solution in a cloud computing environment. The computing resource flow comprises a set of computing resources and computing resource relationships that implement the computing solution. The techniques further include identifying a reference architecture that corresponds to at least a part of the computing resource flow for the computing solution and obtaining a reference template for the reference architecture from a reference code repository. The techniques further include generating a solution template that is based in part on the reference template, and the solution template provisions the computing resource flow in the cloud computing environment to implement the computing solution.

Abstract: A method for automatically selecting a computing device to offload and process a computer task is provided. The method may include automatically identifying an off-loadable computer task to be offloaded from a first computing device to a second computing device. The method may further include determining a scheduled time and an amount of time for processing the identified off-loadable computer task. The method may further include, based on the identified off-loadable computer task, the scheduled time, and the determined amount of time, automatically identifying available computing devices from the plurality of computing devices for processing the identified off-loadable computer task. The method may further include, selecting the computing device from for processing the off-loadable computer task, wherein selecting a moving edge computing device further includes coordinating the scheduled time and the amount of time for processing the identified off-loadable computer task with a determined travel plan.

Abstract: An example method for determining domain name system (DNS) forwarding rules is provided. The method includes training a machine learning model to generate recommended DNS forwarding rules and determine whether a DNS resolver is experiencing an DNS resolver issue. The method further includes receiving a DNS query at the DNS resolver which is one of a plurality of DNS resolvers forming a network of DNS resolvers. The method further includes identifying a chain of DNS resolvers of the network of DNS resolvers based at least in part on the DNS query and a DNS response to the DNS query. The method further includes analyzing the DNS query and the DNS response using the machine learning model to generate a DNS forwarding rule and to identify whether any of the DNS resolvers of the network of DNS resolvers is experiencing the DNS resolver issue.

Abstract: Detecting resource errors in a heterogeneous computing environment is provided. A plurality of individual resource dependency graphs corresponding to a plurality of computing systems that comprise the heterogeneous computing environment is consolidated to form a consolidated resource dependency graph. An analysis of respective nodes representing respective resources of the heterogeneous computing environment in the consolidated resource dependency graph is performed to identify a resource error caused by a new resource being added to a computing system of the plurality of computing systems based on defined rule sets. It is determined whether the new resource causes an error to sibling resources at a same level under a parent resource in the consolidated resource dependency graph based on the analysis. In response to determining that the new resource does not cause an error to the sibling resources, the new resource is deployed in the computing system of the heterogeneous computing environment.

Abstract: Detecting resource errors in a heterogeneous computing environment is provided. A plurality of individual resource dependency graphs corresponding to a plurality of computing systems that comprise the heterogeneous computing environment is consolidated to form a consolidated resource dependency graph. An analysis of respective nodes representing respective resources of the heterogeneous computing environment in the consolidated resource dependency graph is performed to identify a resource error caused by a new resource being added to a computing system of the plurality of computing systems based on defined rule sets. It is determined whether the new resource causes an error to sibling resources at a same level under a parent resource in the consolidated resource dependency graph based on the analysis. In response to determining that the new resource does not cause an error to the sibling resources, the new resource is deployed in the computing system of the heterogeneous computing environment.

Abstract: A system and method compare performance of virtual assistants. A user selects metrics for evaluating two or more virtual assistants, and these metrics may be weighted by the user. One or more chat sessions from each virtual assistant are then analyzed using the weighted metrics to generate a score for each chat session. The scores of chat sessions of different virtual assistants are then compared according to the selected weighted metrics, and a recommendation of a virtual assistant may be made based on the score comparison. The evaluation of multiple virtual assistants allows comparing these virtual assistants to determine which provides the better customer service according to the selected weighted metrics.

Abstract: An approach is provided for changing a language for a replay of a session of a user. Request and response data in the session is captured. If a preferred language for the replay does not match an initial language of content presented to the user in the session, the captured data is translated into the preferred language and the content is presented in the preferred language and not in the initial language. If the preferred language matches the initial language, the content is presented in the initial language, without translating the captured data. An event in the captured data indicates a change in a displayed image. Responsive to searching Hypertext Markup Language (HTML) documents generated from a resource file, the HTML documents are determined to not include an HTML document that specifies the change. The HTML document is generated and employed in the replay of the session.

Abstract: An approach is provided for changing a language for a replay of a session of a user. Request and response data in the session is captured. If a preferred language for the replay does not match an initial language of content presented to the user in the session, the captured data is translated into the preferred language and the content is presented in the preferred language and not in the initial language. If the preferred language matches the initial language, the content is presented in the initial language, without translating the captured data. An event in the captured data indicates a change in a displayed image. Responsive to searching Hypertext Markup Language (HTML) documents generated from a resource file, the HTML documents are determined to not include an HTML document that specifies the change. The HTML document is generated and employed in the replay of the session.

Abstract: An approach is provided for changing a language for a replay of a session of a user. If a preferred language for the replay does not match an initial language of content presented to the user, data captured from requests and responses is translated from the initial into the preferred language, and the session replay presents the content in the preferred language; otherwise, the content is presented in the initial language. If a next event in the data indicates a change in an image displayed on a computing device utilized by the user during the session, a Hypertext Markup Language (HTML) document specifying the change is found in a search of HTML documents generated from a resource file from which image data for the session is reproduced, and the session replay employs the HTML document; otherwise, scripts that apply the next event to the HTML documents are generated.

Abstract: An approach is provided for changing a language for a replay of a session of a user. If a preferred language for the replay does not match an initial language of content presented to the user, data captured from requests and responses is translated from the initial into the preferred language, and the session replay presents the content in the preferred language; otherwise, the content is presented in the initial language. If a next event in the data indicates a change in an image displayed on a computing device utilized by the user during the session, a Hypertext Markup Language (HTML) document specifying the change is found in a search of HTML documents generated from a resource file from which image data for the session is reproduced, and the session replay employs the HTML document; otherwise, scripts that apply the next event to the HTML documents are generated.

Abstract: An approach is provided for changing a language for a replay of a session of a user. Data from requests and responses of the session is captured. Based on the captured data, an initial language of content presented to the user in the session is determined. A selection by an analyst of a preferred language for the replay of the session is obtained. It is determined whether the preferred language matches the initial language. If the preferred language does not match the initial language, the captured data is translated from the initial language into the preferred language and the session is replayed by presenting the content in the preferred language and not in the initial language. The replayed session is viewed by the analyst. If the preferred language matches the initial language, the session is replayed by presenting the content in the initial language, without translating the captured data.

Abstract: Systems, methods and tools for providing customers of web sites, applications and online shopping programs opportunities to compare different fabrics of products with previously purchased items. Each item in the website's inventory may be assigned a fabric index score describing fabric properties such as the drapability, the GSM of fabric, capillarity, breathability, absorbance, slipperiness, thickness, softness, scratchiness, fabric content, coloration, vulnerability to discoloration, strands/gaps between threads, vulnerability to wrinkling, and suppleness. Disclosed embodiments leverage computing systems and data analytics to build an index of fabric containing products, classify parameters describing the fabric and analyzing the similarities of products viewed by customers with previously purchased products.

Abstract: An approach is provided for changing a language for a replay of a session of a user. Data from requests and responses of the session is captured. Based on the captured data, an initial language of content presented to the user in the session is determined. A selection by an analyst of a preferred language for the replay of the session is obtained. It is determined whether the preferred language matches the initial language. If the preferred language does not match the initial language, the captured data is translated from the initial language into the preferred language and the session is replayed by presenting the content in the preferred language and not in the initial language. The replayed session is viewed by the analyst. If the preferred language matches the initial language, the session is replayed by presenting the content in the initial language, without translating the captured data.