Abstract: Systems, methods, and other embodiments that manage load in request processing environments are described. In one embodiment, a method includes receiving, at a backend of a request processing environment, requests transmitted by frontends. The backend is controlled to process the requests to create responses that are transmitted back to the frontends. Load of the backend processing the requests is monitored. In response to the load exceeding a threshold, a retry interval is calculated as a function of the load. In response to receiving a subsequent request from a frontend, a command is transmitted to the frontend. The command modifies operation of the frontend to wait the retry interval before re-transmitting the subsequent request as a retry request and to avoid generating an error message.

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

Abstract: Systems, methods, and other embodiments that manage load in request processing environments are described. In one embodiment, a method includes receiving, at a backend of a request processing environment, requests transmitted by frontends. The backend is controlled to process the requests to create responses that are transmitted back to the frontends. Load of the backend processing the requests is monitored. In response to the load exceeding a threshold, a retry interval is calculated as a function of the load. In response to receiving a subsequent request from a frontend, a command is transmitted to the frontend. The command modifies operation of the frontend to wait the retry interval before re-transmitting the subsequent request as a retry request and to avoid generating an error message.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: In accordance with an embodiment, described herein is a system and method for two-tier adaptive heap management (AHM) in a virtual machine environment, such as a Java virtual machine (JVM). In accordance with an embodiment, a two-tier AHM approach recognizes that more virtual machines can be run on a particular host, or the same number of virtual machines can support higher load while minimizing out-of-memory occurrences, swapping, and long old garbage collection pauses, if the heap is divided into tiers, so that a garbage collection policy that minimizes pause time can be used in a first (normal) tier, and a garbage collection policy that favors heap compaction and release of free memory to the host can be used in another (high-heap) tier.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: In accordance with an embodiment, described herein is a system and method for two-tier adaptive heap management (AHM) in a virtual machine environment, such as a Java virtual machine (JVM). In accordance with an embodiment, a two-tier AHM approach recognizes that more virtual machines can be run on a particular host, or the same number of virtual machines can support higher load while minimizing out-of-memory occurrences, swapping, and long old garbage collection pauses, if the heap is divided into tiers, so that a garbage collection policy that minimizes pause time can be used in a first (normal) tier, and a garbage collection policy that favors heap compaction and release of free memory to the host can be used in another (high-heap) tier.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: Apparatus, methods, and computer program products are disclosed that determine an actor context of an actor as well as an access environment for an attempted operation responsive to the actor context and a necessary condition. The method also evaluates whether the access environment satisfies the necessary condition and activates a principal responsive to the evaluation and authenticates the actor against the principal.

Abstract: Apparatus, methods, and computer program products are disclosed that present a delegated-right to a delegation system by a service-application provisioned with the delegation system. The delegated-right enables the service-application to perform an operation/access on behalf of a delegator-user. The method then attempts to perform the operation/access.

Abstract: An intelligent client agent and a method for using the client agent to operate a hybrid online/offline client application. A mobile client device is configured with a client agent comprising a dispatcher for receiving and responding to page requests from a client browser, a cache for storing the presentation formats of pages, a database for storing data for the pages, a voice engine for interaction with the application user in audio, and a script engine for assembling a page to be presented graphically or aurally. Instead of storing each page (e.g., of an application) as a static composition, the presentation format of the page is stored separate from content (e.g., data). At the time of assembly, the desired content is retrieved and bound to the presentation format and provided to the user.

Abstract: Apparatus, methods, and computer program products are disclosed that determine Rights to an entity. The disclosed technology maintains data structures representing a set of entities. These entities include protected-entities and sensitivity-entities. Each of the sensitivity-entities is associated with a respective sensitivity access-control-list. The sensitivity-entities include a first sensitivity-entity that is associated with a first sensitivity-access-control-list. A first protected-entity being one of one or more of the protected-entities associated with the first sensitivity-entity. The technology evaluates Rights to the first protected-entity with respect to the first sensitivity-access-control-list and enables access to the first protected-entity responsive to the Rights evaluation and presents the first protected-entity when access is enabled.