Abstract: Various methods and devices are provided to address the need to mitigate the effects of an overload and/or prevent an overload from cascading. In one method, devices are paged (101) according to a first paging policy regarding paging attempts. It is then determined (102) that a reduced availability condition exists for network equipment supporting the first paging policy. While this reduced availability condition exists, user equipment is paged (103) according to an adapted paging policy which is more limited in paging attempts than the first paging policy.

Abstract: Methods and computer readable mediums are provided for determining the processor occupancy of a cluster of HLR registers. A deterministic-based performance model is utilized to determine the processor occupancy based on a number of nodes in the cluster, a number of call attempts per unit time, a number of autonomous registrations, a processor utilization for processing each call attempt and a processor utilization for processing each autonomous registration. The models may be utilized to determine the processor occupancy of each node in the cluster if a selected number of HLR nodes are utilized, such that an iterative process may be employed to select a number of HLR nodes in order for each HLR node to operate under a node processor occupancy threshold.

Abstract: An exemplary method implements load management for large granularity processes on application processors, APs. First data associated with the primary processes running on each AP is periodically collected, where the first data is proportional to processor occupancy, PO, for the primary processes running on each AP. Second data associated with auxiliary processes running on each AP is periodically collected where the auxiliary processes directly support the primary processes running on the respective AP. The second data is proportional to PO for the auxiliary processes running on each AP. A processor scaling factor and an overhead scaling factor are calculated for each AP based on the first and second data, respectively. The total amount of additional PO a second AP would incur to run a first large granularity process is determined by two aspects.

Abstract: Methods and computer readable mediums are provided for determining the processor occupancy of a cluster of HLR registers. A deterministic-based performance model is utilized to determine the processor occupancy based on a number of nodes in the cluster, a number of call attempts per unit time, a number of autonomous registrations, a processor utilization for processing each call attempt and a processor utilization for processing each autonomous registration. The models may be utilized to determine the processor occupancy of each node in the cluster if a selected number of HLR nodes are utilized, such that an iterative process may be employed to select a number of HLR nodes in order for each HLR node to operate under a node processor occupancy threshold.

Abstract: An exemplary method implements load management for large granularity processes on application processors, APs. First data associated with the primary processes running on each AP is periodically collected, where the first data is proportional to processor occupancy, PO, for the primary processes running on each AP. Second data associated with auxiliary processes running on each AP is periodically collected where the auxiliary processes directly support the primary processes running on the respective AP. The second data is proportional to PO for the auxiliary processes running on each AP. A processor scaling factor and an overhead scaling factor are calculated for each AP based on the first and second data, respectively. The total amount of additional PO a second AP would incur to run a first large granularity process is determined by two aspects.