Hyperprofile-based Computation Offloading for Mobile Edge Networks

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.Comment: 5 pages, NSF REU Site publicatio

Intelligence-driven edge computing for visual cloud computing systems

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The explosive growth and diversity of mobile devices such as smart phones and Internet of Thing (IoT) devices has prompted the evolution of network and resource management strategies that seek to mitigate the issues imposed by ever-increasing demands for such computational services. Computation offloading has been shown to be an effective approach for augmenting low-power devices with advanced processing capabilities by offloading computational tasks from users to nearby resources at the edge of the network resulting in better response times and lower resource consumption. The need for proactive cost prediction mechanisms is crucial for low-latency and non-diverging scheduling in dynamic network conditions. In this thesis, we discuss the aspects of developing an intelligence-driven offloading framework for large-scale image analytics in crisis management scenarios. We created several realistic datasets from wireless and wired networks as well as IoT devices and virtualized edge resources capable of running single and distributed state-of-the-art deep learning applications for pedestrian and face recognition services. Our framework uses Machine Learning to predict offloading costs in order to better inform scheduling decisions for multi-edge scales. We investigate several aspects of the learning problem such as feature engineering, model selection, offline data generation using networking testbeds, and the benefits of online learning when training data is limited. Evaluation results show that our learning-based approaches not only outperform traditional static estimation techniques, but also provide real-time training and inference capabilities ideal for scaling to large amounts of users in time-sensitive offloading environments