Latency in edge computing
AssigneeInternational Business Machines Corporation
InventorsHui Lin, Jun Yang, Jing James Xu, Yue Wang
Life of this patent
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Abstract
Deep reinforcement learning is applied to self-orchestration in edge device computing for offloading within a spatial network community to reduce latency and bandwidth issues. A revised online policy gradient training algorithm based on importance sampling in addition to the use of DRL-based offloading provides for continued use of original sample training data. A request for help scheme supports edge-device cooperation among neighboring devices of the spatial network community by sharing edge device state information (EDSI) for governing task assignments.
Claims20
A computer-implemented method for edge device self-orchestration in a spatial network community, the method comprising:
- initializing a first deep neural network (DNN) for a local deep reinforcement learning (DRL) network of a first local cluster of edge devices including a first edge device;
- receiving, by the first edge device in the spatial network community, a first task into a local task queue of tasks to be performed by the first edge device;
- assigning, by the first edge device, a first class to the first task based on deadline requirements;
- determining, by the local DRL network, to execute the first task by remote execution based on constraints represented by the assigned first class;
- responsive to determining to execute the first task by remote execution, sending, by the first edge device, a request for help signal to a sub-set of edge devices in the spatial network community, the sub-set of edge devices having edge device profiles demonstrating characteristics aligned with the constraints represented by the assigned first class;
- and
- offloading, by the first edge device, the first task to a selected edge device based on a dataset returned by the selected edge device responsive to the request for help signal.
A computer program product for edge device self-orchestration in a spatial network community, the computer program product comprising a non-transitory computer readable storage medium having a set of instructions stored therein which, when executed by a processor, causes the processor to perform a method comprising:
- initializing a first deep neural network (DNN) for a local deep reinforcement learning (DRL) network of a first local cluster of edge devices including a first edge device;
- receiving, by the first edge device in the spatial network community, a first task into a local task queue of tasks to be performed by the first edge device;
- assigning, by the first edge device, a first class to the first task based on deadline requirements;
- determining, by the local DRL network, to execute the first task by remote execution based on constraints represented by the assigned first class;
- responsive to determining to execute the first task by remote execution, sending, by the first edge device, a request for help signal to a sub-set of edge devices in the spatial network community, the sub-set of edge devices having edge device profiles demonstrating characteristics aligned with the constraints represented by the assigned first class;
- and
- offloading, by the first edge device, the first task to a selected edge device based on a dataset returned by the selected edge device responsive to the request for help signal.
A computer system for edge self-orchestration in a spatial network community, the computer system comprising:
- a processor set;
- and
- a computer readable storage medium;
- wherein:
- the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium;
- and
- the program instructions which, when executed by the processor set, cause the processor set to perform a method comprising:
- initializing a first deep neural network (DNN) for a local deep reinforcement learning (DRL) network of a first local cluster of edge devices including a first edge device;
- receiving, by the first edge device in the spatial network community, a first task into a local task queue of tasks to be performed by the first edge device;
- assigning, by the first edge device, a first class to the first task based on deadline requirements;
- determining, by the local DRL network, to execute the first task by remote execution based on constraints represented by the assigned first class;
- responsive to determining to execute the first task by remote execution, sending, by the first edge device, a request for help signal to a sub-set of edge devices in the spatial network community, the sub-set of edge devices having edge device profiles demonstrating characteristics aligned with the constraints represented by the assigned first class;
- and
- offloading, by the first edge device, the first task to a selected edge device based on a dataset returned by the selected edge device responsive to the request for help signal.
Dependencies are inferred from the claim text.