According to one aspect of the present disclosure, there is provided a computer-implemented method for dynamically allocating edge computing resources. The method can include receiving, in an edge computing center, a request for using the edge computing resources to perform a secondary task for a secondary edge service, allocating the edge computing resources to the secondary edge service if the edge computing resources are available, and in response to an increase in a primary edge services workload, reallocating at least a portion of the edge computing resources from the secondary edge service to the primary edge services if the edge computing resources are insufficient for performing the primary edge services.

A computing system includes a discovery application that identifies a computing device associated with a managed network. The application determines a first command that causes the computing device to invoke a function that provides as output attributes of the computing device. The command includes a parameter that suppresses any textual error messages that the function places in the output. The application also determines a second command that causes the computing device to provide a numerical exit status of the function. The application causes the computing device to execute the first and second commands, and obtains the output and the numerical exit status. Based on the numerical exit status, the application determines that the function did not fully obtain the attributes of the computing device and, in response, (i) modifies the first command, and (ii) causes the computing device to execute the first command as modified and the second command.

Described herein are systems, methods, and software to manage resources in a gateway shared by multiple tenants. In one example, a system may monitor usage of resources by a tenant of the gateway and compare the usage with usage limits associated with the resources. The system may further determine when the usage of a resource exceeds a usage limit associated with the resource and, when the usage of the resource exceeds the usage limit, identify an operation associated with causing the usage limit to be exceeded and blocking the operation.

A computing system includes a discovery application that identifies a computing device associated with a managed network. The application determines a first command that causes the computing device to invoke a function that provides as output attributes of the computing device. The command includes a parameter that suppresses any textual error messages that the function places in the output. The application also determines a second command that causes the computing device to provide a numerical exit status of the function. The application causes the computing device to execute the first and second commands, and obtains the output and the numerical exit status. Based on the numerical exit status, the application determines that the function did not fully obtain the attributes of the computing device and, in response, (i) modifies the first command, and (ii) causes the computing device to execute the first command as modified and the second command.

Embodiments of the present disclosure disclose a method and apparatus for allocating a server resource, an electronic device and a computer readable storage medium, and relate to the technical fields of cloud platform, cloud environment, containerization and resource allocation. A specific implementation of the method comprises: acquiring a container group creation request initiated by a user for creating a target container group; determining a required amount of server resources required by the user and a remaining amount of the server resources according to the container group creation request, the remaining amount comprising at least one of an exclusive server resource or a shared server resource; rating qualities of the remaining amount of server resources in the remaining amount, and selecting a target server resource corresponding to the required amount according to an obtained actual rating; and allocating the target server resource to the user for creating the target container group.

Novel tools and techniques in a telecommunication network are provided for implementing a data link layer control plane that may comply with the Ethernet standard and with sub-millisecond transmission control capabilities across multiple dis-similar technologies and bandwidth links. The data link protocol system may be implemented through a cloud system. Setting and resource registries of the nodes in the network may be displayed at a cloud portal for users to adjust the network. The change in the setting may be communicated through an application programming interface of a compute host to change the setting of the network. Each DLP node includes a slow agent and a fast agent. The slow agent is configured to transmit data messages through Ethernet frames. The fast agent is configured to transmit control messages through DLP frames. Each DLP frame includes a header only without a payload and the header carries a control message.

According to some embodiments, a centralized resource provisioning system may associated with a plurality of end-user applications in a cloud-based computing environment. The centralized resource provisioning system may include a policy decision maker that generates a centralized recommendation for a computing resource of a first end-user application. An application decision maker may be associated with the first end-user application and generate a decentralized recommendation for the computing resource of the first end-user application. A machine controller of the centralized resource provisioning system may then arrange to adjust the computing resource for the first end-user application when both the centralized recommendation and the decentralized recommendation indicate that the adjustment is appropriate.

A method, system and computer program product for handling potential service load interruptions. The utilization of resources, such as servers in a service infrastructure of a SaaS provider, are monitored. If the utilization of a resource exceeds a threshold, then the resource is identified as having an excessive service load leading to a potential service load interruption. When a request is received from a user requesting to access such a resource, one or more action items to be completed by the user are generated and presented to the user. Action items refer to any activity that is required by the user to be performed thereby providing the SaaS provider additional time to address the potential service load interruption in an appropriate manner. Additional action item(s) will be presented to the user until the SaaS provider addresses the potential service load interruption, at which point, the request will be serviced.

The techniques described herein relate to methods, apparatus, and computer readable media configured to select a computing resource from a plurality of computing resources to perform a computing process. A request is received from a remote computing device to perform the computing process. A first set of estimated metrics is accessed that includes an estimated metric for each computing resource and the first remote computing device. The second data is processed using a machine learning algorithm to select a candidate computing resource to perform the process. The machine learning algorithm selects the candidate computing resource based on a second estimated metric between at least one second remote computing device and an associated computing resource from the plurality of computing resources performing a second computing process for the at least one second remote computing device, and a capacity of each computing resource of the plurality of computing resources.

Disclosed herein are methods, systems, and processes to perform granular and selective agent-based throttling of command executions. A polling interval of an agent process executing on a protected host is monitored. If the agent process is active and a current throttle is greater than a desired throttle, the agent process and its children processes are suspended and a run count flag is incremented. However, if the agent process is inactive and the current throttle is less than or equal to the desired throttle, the agent process and its children processes are resumed and a skip count flag is incremented.