Cisco Technology, Inc.

Dantong Liu

Qing Zhao

Ardalan Alizadeh

Gautam Dilip Bhanage

Khashayar Mirfakhraei

Xu Zhang

Matthew Aaron Silverman

John T Chapman

Alon Shlomo Bernstein

Fred Jay Anderson

Zheng Lu

Elias Chavarria Reyes

Learning-based service migration in mobile edge computing may be provided. First, a service migration policy may be created for a network that includes a plurality of edge clouds configured to provide a service to users. Next, a movement of a user receiving the service from a source edge cloud may be detected. The source edge cloud may be associated with a first area and the detected movement may be from the first area to a second area. Then, the service migration policy may be applied to determine whether to migrate the service for the user from the source edge cloud. In response to determining to migrate the service, a target edge cloud may be identified and the service for the user may be migrated from the source edge cloud to the target edge cloud. The service migration policy may then be updated based on a success of the migration.

Learning-based service migration in mobile edge computing

A distributed controller of local radio resources implements a hybrid FFR system. The distributed controller provides user channel information to a central controller of regional radio resources. The distributed controller also obtains an FFR plan from the central controller. The FFR plan designates for an access point associated with the distributed controller, a central frequency band, an edge frequency band, and a distribution of user devices connected to the access point. The distribution indicates whether each user device communicates with the access point via the central frequency band or via the edge frequency band. The distributed controller provisions the access pint to connect to the user devices according to the FFR plan, and adjusts the distribution of user devices locally based on a change in the user channel information.

Dynamic fractional frequency reuse

Predictive scheduling may be provided. First, a first device may identify when a service flow is expected to become active. The first device may estimate an initial traffic profile in response to identifying when the service flow is expected to become active. The first device may then grant allocation based on the initial traffic profile of the service flow. Next, the first device may collect feedback to later update the traffic profile estimate. The first device may then update the traffic profile estimate.

Predictive scheduler

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Mountain View, CA, United States of America

Dantong Liu