Hyperprofile-based Computation Offloading for Mobile Edge Networks
This work addresses resource optimization for edge computing, but it appears incremental as it builds on existing offloading frameworks with a novel method.
The paper tackles the problem of selecting edge nodes for computation offloading to reduce resource consumption in mobile edge networks, proposing a hyperprofile-based method that uses machine learning to predict offloading costs and k-Nearest Neighbor queries, with results showing accurate modeling of network metrics and favorable conditions for using Euclidean distance.
In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption. We propose a unique solution to this problem which incorporates elements from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical data. Each server instance can be represented in a multidimensional feature space where each dimension corresponds to a predicted metric. We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task. We then perform a k-Nearest Neighbor (kNN) query within the hyperprofile to select nodes for offloading computation. This paper formalizes our hyperprofile-based solution and explores the viability of using machine learning (ML) techniques to predict metrics useful for computation offloading. We also investigate the effects of using different distance metrics for the queries. Our results show various network metrics can be modeled accurately with regression, and there are circumstances where kNN queries using Euclidean distance as opposed to rectilinear distance is more favorable.