Abstract: This disclosure describes techniques and mechanisms for enabling a user and third party applications to dynamically partition and place heavy deep learning workloads on standard edge networks to optimize the overall inference throughput of the network while meeting Service Level Objective(s) (SLOs). The techniques may include profiling, partitioning, and splitting of the deep learning workloads, which may be hidden from the user and/or third party application. The user may user interact with a pre-deployed service through a simple SDK that resembles those used for hardware acceleration, such that the current techniques may be easily inserted into their code.

Abstract: This disclosure describes techniques and mechanisms for enabling a user to run heavy deep learning workloads on standard edge networks without off-loading computation to a cloud, leveraging the available edge computing resources, and efficiently partitioning and distributing a Deep Neural Network (DNN) over a network. The techniques enable the user to split a workload into multiple parts and process the workload on a set of smaller, less capable compute nodes in a distributed manner, without compromising on performance, and while meeting a Service Level Objective (SLO).

Abstract: Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta-averages for the counters/features around the time of the state change may be determined. The delta-averages may be utilized to determine which counters/features are most descriptive for a particular state change. Determining which counters/features are most descriptive may also include determining which counters/features are most relevant, i.e., counters/features that contribute most to preserving the manifold structure of the original data or counters/features with the highest or lowest correlation with the other counters/features in the data set.

Abstract: Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta averages for the features around the time of the state change may be determined. The delta averages may be utilized to determine which counters/features are most descriptive for a particular state change. The counter/features that are the most descriptive for a particular state change is as important as the change detection itself. This is especially true since in a case of an event/state change occurring, a large amount of counters/features may react to the state change or event. Thus, the techniques described herein provide for an approach to distill which counters/features contribute the most to a particular state change from a data driven perspective.

Abstract: Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta-averages for the counters/features around the time of the state change may be determined. The delta-averages may be utilized to determine which counters/features are most descriptive for a particular state change. Determining which counters/features are most descriptive may also include determining which counters/features are most relevant, i.e., counters/features that contribute most to preserving the manifold structure of the original data or counters/features with the highest or lowest correlation with the other counters/features in the data set.

Abstract: Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta averages for the features around the time of the state change may be determined. The delta averages may be utilized to determine which counters/features are most descriptive for a particular state change. The counter/features that are the most descriptive for a particular state change is as important as the change detection itself. This is especially true since in a case of an event/state change occurring, a large amount of counters/features may react to the state change or event. Thus, the techniques described herein provide for an approach to distill which counters/features contribute the most to a particular state change from a data driven perspective.