Abstract: In some examples, a system may include a first computing device communicatively coupled to a second computing device. Additionally, the first computing device is configured to obtain, from the second computing device, check-in data indicating an arrival of the user of the second computing device at a first location, and in response to obtaining the check-in data, determine current wait times. Moreover, the first computing device is configured to determine a first number of customers waiting for service, determine a first number of associates available to assist the first number of customers, and determine an expected wait time for the user operating the second computing device based at least on the current wait times. In some examples, the first number of customers waiting for service, and the first number of associates available. Further, the first computing device is configured to transmit the expected wait time to the second computing device.

Abstract: A system that permits or otherwise facilitates assessment of operational state of a computing component in a computing environment. In one example, this disclosure describes a method that includes collecting, by a server device that is executing within a server device cluster, metric information indicative of an operational state of the server device, wherein the metric information is associated with a plurality of virtual computing instances executing on the server device; analyzing, by the server device and based on the metric information, whether a first condition associated with a first virtual computing instance is satisfied; analyzing, by the server device and based on the metric information, whether a second condition associated with a second virtual computing instance is satisfied; and updating control information characterizing the operational state of the server device executing within the server device cluster.

Abstract: The present invention addresses the need for improved virtualized cloud infrastructure policy implementation and management in order allow real-time monitoring and optimization of virtualized resources. It provides systems and methods for real-time cloud infrastructure policy implementation and management that include a plurality of host devices, a plurality of real-time probe agents associated with the plurality of host devices operating on each of the plurality of host devices, and a policy engine communicatively coupled to the plurality of host devices and containing a policy associated with an application program deployed in at least one of the plurality of host devices. The policy engine is programmed to monitor in real time changes in deployment of the application program across the plurality of host devices and to push the policy to the real-time probe agent operating on each host device on which the application program is deployed.

Abstract: This disclosure describes techniques for analyzing information generated as a result of monitoring resources within computing environments. In one example, this disclosure describes a method that includes observing a plurality of alerts generated in response to monitoring resources associated with a plurality of connected elements within a network; determining a plurality of occurrence counts; determining a plurality of concurrent occurrence counts, wherein each of the plurality of concurrent occurrence counts represents a count of concurrent occurrences of two or more of the alerts in the set of historical alerts; and identifying one or more root cause alerts, wherein the one or more root cause alerts are determined based on the plurality of occurrence counts and the plurality of the concurrent occurrence counts, and wherein the one or more root cause alerts are a subset of the plurality of alerts.

Abstract: The present invention addresses the need for improved virtualized cloud infrastructure policy implementation and management in order allow real-time monitoring and optimization of virtualized resources. It provides systems and methods for real-time cloud infrastructure policy implementation and management that include a plurality of host devices, a plurality of real-time probe agents associated with the plurality of host devices operating on each of the plurality of host devices, and a policy engine communicatively coupled to the plurality of host devices and containing a policy associated with an application program deployed in at least one of the plurality of host devices. The policy engine is programmed to monitor in real time changes in deployment of the application program across the plurality of host devices and to push the policy to the real-time probe agent operating on each host device on which the application program is deployed.

Abstract: The present invention addresses the need for improved virtualized cloud infrastructure policy implementation and management in order allow real-time monitoring and optimization of virtualized resources. It provides systems and methods for real-time cloud infrastructure policy implementation and management that include a plurality of host devices, a plurality of real-time probe agents associated with the plurality of host devices operating on each of the plurality of host devices, and a policy engine communicatively coupled to the plurality of host devices and containing a policy associated with an application program deployed in at least one of the plurality of host devices. The policy engine is programmed to monitor in real time changes in deployment of the application program across the plurality of host devices and to push the policy to the real-time probe agent operating on each host device on which the application program is deployed.

Abstract: The present invention addresses the need for improved virtualized cloud infrastructure policy implementation and management in order allow real-time monitoring and optimization of virtualized resources. It provides systems and methods for real-time cloud infrastructure policy implementation and management that include a plurality of host devices, a plurality of real-time probe agents associated with the plurality of host devices operating on each of the plurality of host devices, and a policy engine communicatively coupled to the plurality of host devices and containing a policy associated with an application program deployed in at least one of the plurality of host devices. The policy engine is programmed to monitor in real time changes in deployment of the application program across the plurality of host devices and to push the policy to the real-time probe agent operating on each host device on which the application program is deployed.

Abstract: This disclosure describes techniques for analyzing information generated as a result of monitoring resources within computing environments. In one example, this disclosure describes a method that includes observing a plurality of alerts generated in response to monitoring resources associated with a plurality of connected elements within a network; determining a plurality of occurrence counts; determining a plurality of concurrent occurrence counts, wherein each of the plurality of concurrent occurrence counts represents a count of concurrent occurrences of two or more of the alerts in the set of historical alerts; and identifying one or more root cause alerts, wherein the one or more root cause alerts are determined based on the plurality of occurrence counts and the plurality of the concurrent occurrence counts, and wherein the one or more root cause alerts are a subset of the plurality of alerts.

Abstract: A system that permits or otherwise facilitates assessment of operational state of a computing component in a computing environment. In one example, this disclosure describes a method that includes collecting, by a server device that is executing within a server device cluster, metric information indicative of an operational state of the server device, wherein the metric information is associated with a plurality of virtual computing instances executing on the server device; analyzing, by the server device and based on the metric information, whether a first condition associated with a first virtual computing instance is satisfied; analyzing, by the server device and based on the metric information, whether a second condition associated with a second virtual computing instance is satisfied; and updating control information characterizing the operational state of the server device executing within the server device cluster.

Abstract: An assessment environment is provided to generate real-time or nearly real-time events and/or alarms based at least on operational state of a host device. An agent module executing in the host device can monitor some or all of the performance metrics that are available in the host device and can analyze the monitored information in order to generate operational information and/or intelligence associated with an operational state of the host device and/or a computing component (e.g., an application, a virtual machine, or a container) associated therewith. The monitoring and analysis can be performed locally at the host device in real-time or nearly real-time. Analysis of the monitored information can be utilized to update first control information indicative of occurrence of an event and/or second control information indicative of presence or absence of an alarm condition. The control information can be sent to a remote device.

Abstract: For multiple multi-core nodes in a cluster, the filtered statistics clients contacts the aggregator on a master node of the cluster, referred to as the cluster configuration owner (“CCO”) or cluster coordinator and expects the stats aggregated from all the cluster nodes. The aggregator on the CCO nodes relay the client request to packet engines on the CCO node and to an aggregator on each of the other nodes in the cluster. Then the CCO node aggregator gets responses from other cores on the node and responses from all other cluster node aggregators. The CCO node aggregator aggregates the responses and sends back the aggregated response to the clients. Communication between nodes is via a static authenticated communication channel.

Abstract: In various example embodiments, a system and method are presented for a bandwidth (BW) management system. The BW management system includes a BW manager module to manage bandwidth of a collection of flows by traffic classes using bandwidth limits assigned to the traffic classes arranged in a hierarchical bandwidth tree (HBT). The BW management system includes a quality of service (QOS) manager module to manage bandwidth for leaf traffic subclasses in the HBT based on application priority classifications. The bandwidth management system including a window manager (WM) module to manage bandwidth for individual flows in the collection of flows using a sliding window protocol to control the rate at which the first host transmits data packets to the second host. The QOS manager module is in communication with the WM module and the BW manager module while the bandwidth management system is actively managing the bandwidth of the collection of flows.

Abstract: In various example embodiments, a system and method are presented for a bandwidth (BW) management system. The BW management system accepts, on behalf of a receiving host, data packets for a first individual flow at a flow rate sent by a sending host. The bandwidth management system manages bandwidth by traffic classes representing collections of flows associated with nodes in a hierarchical bandwidth tree (HBT). The first individual flow is included within the collection of individual flows associated with one or more of the traffic classes. The BW management system controls the rate at which the sending host is transmitting the data packets for the first individual flow using a sliding window protocol by managing the bandwidth utilization of the nodes to conform to bandwidth limits assigned to the nodes. The BW management system receives the data packets for the first individual flow at an adjusted flow rate based on the sliding window protocol.

Abstract: An assessment environment is provided to generate real-time or nearly real-time events and/or alarms based at least on operational state of a host device. An agent module executing in the host device can monitor some or all of the performance metrics that are available in the host device and can analyze the monitored information in order to generate operational information and/or intelligence associated with an operational state of the host device and/or a computing component (e.g., an application, a virtual machine, or a container) associated therewith. The monitoring and analysis can be performed locally at the host device in real-time or nearly real-time. Analysis of the monitored information can be utilized to update first control information indicative of occurrence of an event and/or second control information indicative of presence or absence of an alarm condition. The control information can be sent to a remote device.

Abstract: The present invention addresses the need for improved virtualized cloud infrastructure policy implementation and management in order allow real-time monitoring and optimization of virtualized resources. It provides systems and methods for real-time cloud infrastructure policy implementation and management that include a plurality of host devices, a plurality of real-time probe agents associated with the plurality of host devices operating on each of the plurality of host devices, and a policy engine communicatively coupled to the plurality of host devices and containing a policy associated with an application program deployed in at least one of the plurality of host devices. The policy engine is programmed to monitor in real time changes in deployment of the application program across the plurality of host devices and to push the policy to the real-time probe agent operating on each host device on which the application program is deployed.

Abstract: In various example embodiments, a system and method are presented for a bandwidth (BW) management system. The BW management system accepts, on behalf of a receiving host, data packets for a first individual flow at a flow rate sent by a sending host. The bandwidth management system manages bandwidth by traffic classes representing collections of flows associated with nodes in a hierarchical bandwidth (HBT). The first individual flow is included within the collection of individual flows associated with one or more of the traffic classes. The BW management system controls the rate at which the sending host is transmitting the data packets for the first individual flow using a sliding window protocol by managing the bandwidth utilization of the nodes to conform to bandwidth limits assigned to the nodes. The BW management system receives the data packets for the first individual flow at an adjusted flow rate based on the sliding window protocol.

Abstract: In various example embodiments, a system and method are presented for a bandwidth (BW) management system. The BW management system includes a BW manager module to manage bandwidth of a collection of flows by traffic classes using bandwidth limits assigned to the traffic classes arranged in a hierarchical bandwidth tree (HBT). The BW management system includes a quality of service (QOS) manager module to manage bandwidth for leaf traffic subclasses in the HBT based on application priority classifications. The bandwidth management system including a window manager (WM) module to manage bandwidth for individual flows in the collection of flows using a sliding window protocol to control the rate at which the first host transmits data packets to the second host. The QOS manager module is in communication with the WM module and the BW manager module while the bandwidth management system is actively managing the bandwidth of the collection of flows.

Abstract: For multiple multi-core nodes in a cluster, the filtered statistics clients contacts the aggregator on a master node of the cluster, referred to as the cluster configuration owner (“CCO”) or cluster coordinator and expects the stats aggregated from all the cluster nodes. The aggregator on the CCO nodes relay the client request to packet engines on the CCO node and to an aggregator on each of the other nodes in the cluster. Then the CCO node aggregator gets responses from other cores on the node and responses from all other cluster node aggregators. The CCO node aggregator aggregates the responses and sends back the aggregated response to the clients. Communication between nodes is via a static authenticated communication channel.