A computer network optimization methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature selection analysis performed thereon. Provided the feature selection analysis determines that features of the components are in frequent communication and generating network latency. Provided the feature selection analysis determines that features of the components are not well defined, a similarity analysis of the features is performed. Results of the feature selection methodology are generated, and the components involved in the network traffic latency are reassigned to migrate the latency.

A system for monitoring a plurality of machines of a facility is disclosed. The system comprises a plurality of data network devices configured to communicate with one another via at least one network. The data network devices are configured to collect machine data from the plurality of machines and distribute the machine data via the at least one network. The system further comprises data display devices configured to provide a graphical user interface that enables a user to view and analyze the machine data that is collected and distributed by the data network devices.

Embodiments are directed to managing communication over one or more networks. A monitoring engine may be instantiated to perform actions including receiving network traffic from a physical network that may be associated with network addresses of the physical network. The monitoring engine may analyze the network traffic to associate activity with gateway identifiers (GIDs) associated with gateway computers in an overlay network such that the GIDs are separate from the network addresses. The monitoring engine may be arranged to monitor the network traffic based on monitoring rules. The monitoring engine may provide metrics associated with the gateway computers based on the monitoring of the network traffic. The monitoring engine may compare the metrics to event rules. The monitoring engine may generate events based on affirmative results of the comparison. The events may be mapped to actions based on characteristics of the events and executed.

Embodiments are directed to managing communication over one or more networks. A monitoring engine may be instantiated to perform actions including receiving network traffic from a physical network that may be associated with network addresses of the physical network. The monitoring engine may analyze the network traffic to associate activity with gateway identifiers (GIDs) associated with gateway computers in an overlay network such that the GIDs are separate from the network addresses. The monitoring engine may be arranged to monitor the network traffic based on monitoring rules. The monitoring engine may provide metrics associated with the gateway computers based on the monitoring of the network traffic. The monitoring engine may compare the metrics to event rules. The monitoring engine may generate events based on affirmative results of the comparison. The events may be mapped to actions based on characteristics of the events and executed.

Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Provided herein are systems and methods for providing insights or metrics in connection with provisioning applications and/or desktop sessions to end-users. Network devices (e.g., appliances, intermediary devices, gateways, proxy devices or middle-boxes) can gather insights such as network-level statistics. Additional insights (e.g., metadata and metrics) associated with virtual applications and virtual desktops can be gathered to provide administrators with comprehensive end-to-end real-time and/or historical reports of performance and end-user experience (UX) insights. Insights relating to an application or desktop session can be used to determine and/or improve the overall health of the infrastructure of the session, Citrix Virtual Apps and Desktops, the applications (e.g., remote desktop application) being delivered using the infrastructure, and/or the corresponding user experience.

Provided herein are systems and methods for providing insights or metrics in connection with provisioning applications and/or desktop sessions to end-users. Network devices (e.g., appliances, intermediary devices, gateways, proxy devices or middle-boxes) can gather insights such as network-level statistics. Additional insights (e.g., metadata and metrics) associated with virtual applications and virtual desktops can be gathered to provide administrators with comprehensive end-to-end real-time and/or historical reports of performance and end-user experience (UX) insights. Insights relating to an application or desktop session can be used to determine and/or improve the overall health of the infrastructure of the session, Citrix Virtual Apps and Desktops, the applications (e.g., remote desktop application) being delivered using the infrastructure, and/or the corresponding user experience.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.