ThingsMigrate: Platform-Independent Migration of Stateful JavaScript IoT Applications

The Internet of Things (IoT) has gained wide popularity both in academic and industrial contexts. As IoT devices become increasingly powerful, they can run more and more complex applications written in higher-level languages, such as JavaScript. However, by their nature, IoT devices are subject to resource constraints, which require applications to be dynamically migrated between devices (and the cloud). Further, IoT applications are also becoming more stateful, and hence we need to save their state during migration transparently to the programmer. In this paper, we present ThingsMigrate, a middleware providing VM-independent migration of stateful JavaScript applications across IoT devices. ThingsMigrate captures and reconstructs the internal JavaScript program state by instrumenting application code before run time, without modifying the underlying Virtual Machine (VM), thus providing platform and VM-independence. We evaluated ThingsMigrate against standard benchmarks, and over two IoT platforms and a cloud-like environment. We show that it can successfully migrate even highly CPU-intensive applications, with acceptable overheads (about 30%), and supports multiple migrations

A novel distributed architecture for IoT image processing using low-cost devices and open internet standards

Industry 4.0 can be defined as the integration of computers and automation to current industrial processes, with addition of smart and autonomous systems leveraged by machine learning techniques. In this scenario, a compact, dependable and fast controller is desired, featuring low energy consumption, easily programming and maintenance, with no mobile parts. Nowadays, computing power in single board computers, e.g. the Raspberry Pi among others, has been increased at a very important rate. In just three generations, Pi computers offer almost a two-fold speed gain, when compared to first models. Its design, an underlying video driver with general capabilities of regular OSes, makes them quite suitable to build image processing systems at very low cost, with no mobile parts and low energy consumption. However, designing such a system for industrial image processing is a tough challenge, since it implies to integrate cameras, image processing libraries, database servers and application software with graphical user interface, in an already resource constrained device. This work presents a new architecture for this kind of systems, by means of open internet standards, using a self-contained, high performance web server to publish a RESTful API and a set of web pages that use latest HTML5 capabilities to manage USB webcams and system data. This proposal also integrates OpenCV as a compiled script on client-side using the new WASM paradigm, with an optimized storage for images using -industry-standard RDBMS and a modular design that can target Windows and Linux as well.Sociedad Argentina de Informática e Investigación Operativ

ES6 자바스크립트의 스냅샷 기반 마이그레이션

학위논문(석사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 문수묵.최근 웹 플랫폼 및 자바스크립트의 인기와 함께, 자바스크립트로 작성된 프로그램을 위한 앱 마이그레이션 기술이 연구된 바 있다. 이는 이종의 기기 간에 연속적인 워크플로우를 제공해 새로운 사용자 경험을 제공하는 기술을 일컫는다. 여러 선행 연구에서 스냅샷 기반 방법론을 사용해 앱의 런타임 상태를 텍스트 형태로 직렬화 및 복원하는 시도를 했다. 그러나, 기존 연구들은 구 버전 자바스크립트 상에서 진행됐다는 한계가 있다. 이에 비해 ECMAScript2015 (ES6) 업데이트 이후 자바스크립트에 다양한 기능이 도입되었기 때문에, 기존 방법들은 오늘날 real-world 애플리케이션을 마이그레이션하기 어렵다. 본 논문은 [19]에서 소개된 우리의 프레임워크를 소개한다. 우리는 선행 연구에서 다뤄지지 않은 block scope, module, class syntax와 같은 ES6의 주요 기능을 분석했으며 이러한 새로운 기능을 사용하는 앱을 마이그레이션 하기 위한 알고리즘을 제안했다. 또한, 우리는 최신 자바스크립트 엔진에 대한 분석을 통해 실행 중인 자바스크립트 프로그램의 런타임 상태를 scope tree라는 자료구조 상에 직렬화하고, 후처리를 거친 scope tree로부터 스냅샷 코드를 생성했다. 이러한 방법론을 V8 자바스크립트 엔진인 상에 구현했으며, 복잡한 최신 자바스크립트 기능을 사용하는 벤치마크 프로그램에 대해 실험했다. 실험 결과를 통해 이러한 방법이 모바일 기기 간에 5개의 벤치마크 프로그램을 성공적으로 마이그레이션 시킨다는 것을 보였다. 복잡도가 가장 높은 앱 (ML 벤치마크, 소스 코드 크기 213KB)에 대한 실험에서 프레임워크로 인한 시간 부하를 측정한 결과, X86 랩톱에서 200ms 미만, ARM 기반 임베디드 보드에서 800ms 미만이었다. 이러한 결과를 통해 자원이 제한된 IoT 기기 등에 대한 적용 가능성을 검증했으며, 추가적으로 프레임워크의 활용 방안 및 향후 연구 방향에 대해 논의한다.With the growing popularity of the web platform and JavaScript, an interesting user experience called application (app) migration has been proposed for JavaScript programs. To enable a non-breaking workflow across different devices, recent studies have proposed snapshot-based techniques in which an app’s runtime state is serialized into a text form that can be restored back later. A limitation of existing literature, however, is that they are based on old JavaScript specifications. Since major updates introduced by ECMASCript2015 (a.k.a. ES6), JavaScript supports various features that cannot be migrated correctly with existing methods. Some of these features are heavily used in today’s real-world apps and thus greatly reduces the scope of previous works. In this thesis, I will mainly introduce my work presented in [19]. In the paper, we analyzed ES6 features such as block scopes, modules, and class syntax that were previously uncovered in app migration. We presented an algorithm that enables migration of apps implemented with these new features. Based on the standards adopted in modern JavaScript engines, our approach serializes a running program into a scope tree and reorganizes it for snapshot code generation. We implemented our idea on the open source V8 engine and experiment with complex benchmark programs of modern JavaScript. Results showed that our approach correctly migrates 5 target programs between mobile devices. Our framework could migrate the most complex app of source code size 213KB in less than 200ms in a X86 laptop and 800ms in an embedded ARM board, showing feasibility in resource-constrained IoT devices. I will also discuss possible use cases and research directions and conclude.Chapter 1. Introduction 1 1.1. JavaScript App Migration 1 1.2. Purpose of Research 2 Chapter 2. Background 4 2.1. Snapshot-based Approach 4 2.2. Function Closure and Scope Tree 6 2.3. Limitations of Previous Works 6 Chapter 3. Proposed Approach 10 3.1. Module Profiling 10 3.2. Migrating Modified Built-in Objects 11 3.3. Scope Tree Building 11 3.4. Syntax-Aware Tree Re-ordering 12 3.5. Tree Partitioning 13 3.6. Snapshot Code Generation 13 Chapter 4. Evaluation 17 4.1. Implementation and Setup 17 4.2. Scope Tree Analysis 18 4.3. Snapshot Code Sizes 19 4.4. Framework Time Overhead 20 Chapter 5. Discussion 22 5.1. Limitations 22 5.2. Alternative Approach 22 5.3. Potential Use Cases 23 Chapter 6. Conclusion 24 Bibliography 25 Abstract in Korean 27석