CALIFORNIA STATE UNIVERSITY SAN MARCOS THESIS SIGNATURE PAGE THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE MASTER OF SCIENCE IN COMPUTER SCIENCE THESIS TITLE: Edge Computing: An Orchestration of Devices AUTHOR: Emmanuel Ayuyao Castillo DATE OF SUCCESSFUL DEFENSE: TI IE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE. Ali Ahrnadinia /zos: 16 TI IESIS COMMITTEE CHAIR DATE Xin Ye ;z/of/lt TIIESIS COMMITTEE MEMBER DATE TI IESIS COMMITTEE MEMBER SIGNATURE DATE Edge Computing: An Orchestration of Devices Thesis by Emmanuel Ayuyao Castillo In Partial Fulfllment of theRequirements for the Degree of Master of Science in Computer Science CALIFORNIA STATE UNIVERSITY, SAN MARCOS San Marcos, California 2018 Defended December 2018 ii © 2018 Emmanuel Ayuyao Castillo ORCID: 004268444 All rights reserved except where otherwise noted iii ACKNOWLEDGEMENTS I would like to thank California State University, San Marcosfor acceptingme into their Computer Science Master’s program and providing mea rewarding academic experience I would never forget. The professors I work with were very supportive and I have learned a lot in the Computer Science program. I would alsolike to thank my parents and close friends that help me get throughthis journey. It was a diÿcult time for me working and going to school full time with long commutes. There were many cases when I had to sacrifce time and events with my friends and family to take care of my academics. In the end they are still by my side. Lastly, I have the greatest appreciation for my graduate advisor Dr. Ali Ahmadinia. It was my goal to be an Embedded Software Engineer. He introduced me to not only embedded systems both also Artifcial Intelligence, Internet of Things and ComputerVision. I not only gained skills and knowledge in embeddedsystems but also a range of other interesting domains. Overall, withoutDr. Ali AhmadiniaI would have not become a very knowledgeable Software Engineer today. iv ABSTRACT The advancements of Artifcial Intelligence (AI) introduces challenges in integrat- ing its technology into resource limited devices. Many AI technology requires substantial resources to run e˙ectively. Object detection is an example that uses complex neural network models that requires both memory and heavy computation to run eÿciently. With the emergence of the Internet of Things (IoT), this will be a problemfor thoseIoT that are battery powered and resource limited. Fortunately, edge computing has shown promising results in improving the performance of re- source heavy applications. This paper introduces an edge computing architecture, Edge Orchestration Architecture that works with the Cloud. TheEdge Orchestration Architecture orchestrates a set of devices in determining where computation should occur to meet ideal system performance. This architecture is implemented in an object detectionsystem and enforces the potentialsthat edge computing has for this current AIgeneration. v TABLE OFCONTENTS Acknowledgements ............................... iii Abstract ..................................... iv Table of Contents ................................ v List of Illustrations ............................... vi Chapter I: Introduction ............................. 1 Chapter II: Related Work ............................ 3 Chapter III: Architecture ............................ 5 3.1 Node .................................. 6 3.2 Cloud .................................. 6 3.3 Edge .................................. 7 Chapter IV: Object Detection .......................... 12 4.1 You Only Look Once .......................... 13 4.2 Multi-view Object Detection withYOLOV2 . 18 4.3 Edge Computing Object DetectionSystem . 19 Chapter V: Experiment ............................. 23 Chapter VI: Results ............................... 26 6.1 Processing Time ............................ 26 6.2 Data Transfer Size ........................... 29 6.3 Battery Life .............................. 29 Chapter VII: Future Work ............................ 31 Chapter VIII: Conclusion ............................ 32 Bibliography .................................. 33 vi LIST OF ILLUSTRATIONS Number Page 3.1 Edge Orchestration Architecture .................... 5 3.2 Node Component ............................ 6 3.3 Cloud Component ........................... 7 3.4 Initial Edge/Node Set-up ........................ 8 3.5 Edge o˜oading Node work to Cloud . 9 3.6 Node directly o˜oading work to Cloud . 9 3.7 Edge confguresNode to o˜oad work back to the Edge . 9 3.8 Edge re-confguring Node to do its own computation . 10 4.1 Object DetectionModel Comparison [31] . 12 4.2 Feature Extraction in Convolutional Layer [6] . 13 4.3 Leaky Rectifed LinearUnit [36] .................... 14 4.4 Batch Normalization [22] ....................... 14 4.5 Max Pooling Layer [6] ......................... 15 4.6 You Only Look Once [31] ....................... 15 4.7 YOLOV2’s Prediction Output [21] . 16 4.8 YOLOV2’s Confdence Loss: Object Existence [33] . 16 4.9 YOLOV2’s Confdence Loss: No Object Existence [33] . 16 4.10 YOLOV2’s Classifcation Loss [33] . 17 4.11 YOLOV2’s Localization Loss [33] . 17 4.12 Anchor Box [31] ............................ 18 4.13 Point Correspondence using Homography [41] . 19 4.14 Edge Computing Objection DetectionSystem . 19 4.15 YOLOV2 Architecture [31] ...................... 20 4.16 YOLOV2 Tiny Architecture [14] .................... 20 4.17 Object Detectionat the Edge ...................... 21 4.18 Multi-view Object DetectionDesign . 22 5.1 Edge Computing Object Detection Component Specifcations . 23 5.2 Experiment with Raspberry Pis .................... 23 6.1 1 Camera Average Processing Time . 26 6.2 1 Camera Average Data Transfer Size . 26 6.3 1 Camera Average Battery Life .................... 27 vii 6.4 2 Camera Average Processing Time . 27 6.5 2 Camera Average Data Transfer Size . 27 6.6 2 Camera Average Battery Life .................... 28 6.7 3 Camera Average Processing Time . 28 6.8 3 Camera Average Data Transfer Size . 28 6.9 3 Camera Average Battery Life .................... 29 1 C h a p t e r 1 INTRODUCTION Amazon Alexa, Google Home, Smart Refrigerators, and several interesting products are being produced today [12, 17, 19, 30]. Internet of Things (IoT) is the generic term for these products and is also expected to increase in count to approximately 50 billion by 2020 [4, 24, 37, 38, 42]. These are thanks to advances in networking and artifcial intelligence (AI). Althougha milestone for technological advances, it also introduces technological challenges. As more devices connect to a network, the network’s throughput will be limited due to the congestion from incoming and outgoing data from thesedevices. On the otherhand, current advances in AI require signifcant resources in computing and storage resulting in limitation in real-time deadlines [37, 38, 42]. Thus, an overhead will occurin IoT and anything connected to a network to handle wait time or run complex AI algorithms [37, 38, 42]. To top it all o˙, these overheads will require extra power consumption on any battery powered device [37, 38, 42]. This would be a major problem in applications that requires advances in object detection. Object detection’s current advancements uses an AI-based technology, Deep Learning (DL) to provide highly accurate detection of objects in images [9, 16, 20, 26, 27, 31–33, 35]. State-of-the-art object detection uses complex DL models, mainly based on convolutional neural networks (CNN) that requires either time or expensive hardware to run [9, 16, 20, 26, 27, 31–33, 35]. These complex models will need to process images which are naturally large size data. If the processing is done elsewhere it will result in a signifcant decrease in a network’s throughput. The processing that occurs from either running the object detection model or waiting for an object detection result puts a load in power consumption. Hence, object detection requires signifcant resources that integrating it into an IoT product will have its challenges in response time, data transfer size and power consumption. Edge computing is a recent research topic that is being considered as a possible solution in resourceheavy applications[4, 13, 24, 37, 38, 40, 42]. Edge computing’s idea is that a device or devices with powerful hardware can exist in the local area of resource limited devices. By having a powerful device near other less powerful 2 devices, it canbe usedto reduce data transfer overhead that e˙ects both processing time and power consumption. Additionally, by having a device with powerful hardware it can reduce the computational load on resource limited devices which will boost system performance. Therefore, edge computing can be a suitable solution to the resourcerequirements on object detectionsystems by reducing both overhead and load on resource limited devices. 3 C h a p t e r 2 RELATED WORK Object detection is a topic within the image recognition domain. Generally, the challenges in image recognition applications are working with its heavy resource requirements in both running its algorithms and large data dependency[37, 38, 42]. Edge computing and work related to edge computing has shown promising capa- bilities in providing solutions to the challenges in image recognition applications. Primarily, theseworks focus on reducing processing time. DIANNE is an architecture used to improve processing time by distributing a neural network