Abstract: Systems, methods, and non-transitory computer readable media are configured to receive a uniform resource locator. A time and one or more features associated with the uniform resource locator can be provided to a first machine learning model. A prediction relating to a quantity of views the uniform resource locator achieves by the time can be received from the first machine learning model.

Abstract: Systems, methods, and non-transitory computer readable media are configured to receive a uniform resource locator. A time and one or more features associated with the uniform resource locator can be provided to a first machine learning model. A prediction relating to a quantity of views the uniform resource locator achieves by the time can be received from the first machine learning model.

Abstract: The disclosed computer-implemented method may include (1) for each tenant in a plurality of tenants within a multi-tenant service system, assigning a probability factor to the tenant that indicates a likelihood that the tenant will be selected when a resource of the multi-tenant service system is available, (2) detecting that the resource of the multi-tenant service system is available, (3) probabilistically selecting a tenant from the plurality of tenants by using the probability factors assigned to the tenants in the plurality of tenants, and (4) directing the multi-tenant service system to allocate the resource to the selected tenant for execution of a work item received from the selected tenant. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include (1) for each tenant in a plurality of tenants within a multi-tenant service system, assigning a probability factor to the tenant that indicates a likelihood that the tenant will be selected when a resource of the multi-tenant service system is available, (2) detecting that the resource of the multi-tenant service system is available, (3) probabilistically selecting a tenant from the plurality of tenants by using the probability factors assigned to the tenants in the plurality of tenants, and (4) directing the multi-tenant service system to allocate the resource to the selected tenant for execution of a work item received from the selected tenant. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Some embodiments include a back-end routing engine. The engine can receive traffic data characterizes amount of service requests from front-end servers to a server group of one or more back-end servers that corresponds to a geographical tier in a server group hierarchy. The engine can receive metric measurements in a performance metric dimension for the server group and a performance threshold corresponding to the performance metric dimension and the geographical tier. The engine can estimate a linear derivative between variable traffic data and variable performance metric in the performance metric dimension based on collected sample points respectively representing the traffic data and the metric measurement. The engine can then compute, based on the linear derivative and the performance threshold, a threshold traffic capacity of the server group. The engine can then generate a routing table based on the threshold traffic capacity.

Abstract: The disclosed computer-implemented method may include (1) for each tenant in a plurality of tenants within a multi-tenant service system, assigning a probability factor to the tenant that indicates a likelihood that the tenant will be selected when a resource of the multi-tenant service system is available, (2) detecting that the resource of the multi-tenant service system is available, (3) probabilistically selecting a tenant from the plurality of tenants by using the probability factors assigned to the tenants in the plurality of tenants, and (4) directing the multi-tenant service system to allocate the resource to the selected tenant for execution of a work item received from the selected tenant. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Systems, methods, and non-transitory computer readable media are configured to receive a uniform resource locator. A time and one or more features associated with the uniform resource locator can be provided to a first machine learning model. A prediction relating to a quantity of views the uniform resource locator achieves by the time can be received from the first machine learning model.

Abstract: Some embodiments include a back-end routing engine. The engine can receive traffic data characterizes amount of service requests from front-end servers to a server group of one or more back-end servers that corresponds to a geographical tier in a server group hierarchy. The engine can receive metric measurements in a performance metric dimension for the server group and a performance threshold corresponding to the performance metric dimension and the geographical tier. The engine can estimate a linear derivative between variable traffic data and variable performance metric in the performance metric dimension based on collected sample points respectively representing the traffic data and the metric measurement. The engine can then compute, based on the linear derivative and the performance threshold, a threshold traffic capacity of the server group. The engine can then generate a routing table based on the threshold traffic capacity.