VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared

Computation Offloading in Multi-access Edge Computing using Deep Sequential Model based on Reinforcement Learning

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Multi-access Edge Computing (MEC) is an emerging paradigm which utilizes computing resources at the network edge to deploy heterogeneous applications and services. In the MEC system, mobile users and enterprises can offload computation-intensive tasks to nearby computing resources to reduce latency and save energy. When users make offloading decisions, the task dependency needs to be considered. Due to the NP-hardness of the offloading problem, the existing solutions are mainly heuristic, and therefore have difficulties in adapting to the increasingly complex and dynamic applications. To address the challenges of task dependency and adapting to dynamic scenarios, we propose a new Deep Reinforcement Learning (DRL) based offloading framework, which can efficiently learn the offloading policy uniquely represented by a specially designed Sequence-to-Sequence (S2S) neural network. The proposed DRL solution can automatically discover the common patterns behind various applications so as to infer an optimal offloading policy in different scenarios. Simulation experiments were conducted to evaluate the performance of the proposed DRL-based method with different data transmission rates and task numbers. The results show that our method outperforms two heuristic baselines and achieves nearly optimal performance.Engineering and Physical Sciences Research Council (EPSRC

CRIME: Input-Dependent Collaborative Inference for Recurrent Neural Networks

The excellent accuracy of Recurrent Neural Networks (RNNs) for time-series and natural language processing comes at the cost of computational complexity. Therefore, the choice between edge and cloud computing for RNN inference, with the goal of minimizing response time or energy consumption, is not trivial. An edge approach must deal with the aforementioned complexity, while a cloud solution pays large time and energy costs for data transmission. Collaborative inference is a technique that tries to obtain the best of both worlds, by splitting the inference task among a network of collaborating devices. While already investigated for other types of neural networks, collaborative inference for RNNs poses completely new challenges, such as the strong influence of input length on processing time and energy, and is greatly unexplored.In this paper, we introduce a Collaborative RNN Inference Mapping Engine(CRIME), which automatically selects the best inference device for each input. CRIME is flexible with respect to the connection topology among collaborating devices, and adapts to changes in the connections statuses and in the devices loads. With experiments on several RNNs and datasets, we show that CRIME can reduce the execution time (or end-node energy) by more than 25% compared to any single-device approach

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

엣지 클라우드 환경을 위한 연산 오프로딩 시스템

학위논문(박사)--서울대학교 대학원 :공과대학 전기·정보공학부,2020. 2. 문수묵.The purpose of my dissertation is to build lightweight edge computing systems which provide seamless offloading services even when users move across multiple edge servers. I focused on two specific application domains: 1) web applications and 2) DNN applications. I propose an edge computing system which offload computations from web-supported devices to edge servers. The proposed system exploits the portability of web apps, i.e., distributed as source code and runnable without installation, when migrating the execution state of web apps. This significantly reduces the complexity of state migration, allowing a web app to migrate within a few seconds. Also, the proposed system supports offloading of webassembly, a standard low-level instruction format for web apps, having achieved up to 8.4x speedup compared to offloading of pure JavaScript codes. I also propose incremental offloading of neural network (IONN), which simultaneously offloads DNN execution while deploying a DNN model, thus reducing the overhead of DNN model deployment. Also, I extended IONN to support large-scale edge server environments by proactively migrating DNN layers to edge servers where mobile users are predicted to visit. Simulation with open-source mobility dataset showed that the proposed system could significantly reduce the overhead of deploying a DNN model.본 논문의 목적은 사용자가 이동하는 동안에도 원활한 연산 오프로딩 서비스를 제공하는 경량 엣지 컴퓨팅 시스템을 구축하는 것입니다. 웹 어플리케이션과 인공신경망 (DNN: Deep Neural Network) 이라는 두 가지 어플리케이션 도메인에서 연구를 진행했습니다. 첫째, 웹 지원 장치에서 엣지 서버로 연산을 오프로드하는 엣지 컴퓨팅 시스템을 제안합니다. 제안된 시스템은 웹 앱의 실행 상태를 마이그레이션 할 때 웹 앱의 높은 이식성(소스 코드로 배포되고 설치하지 않고 실행할 수 있음)을 활용합니다. 이를 통해 상태 마이그레이션의 복잡성이 크게 줄여서 웹 앱이 몇 초 내에 마이그레이션 될 수 있습니다. 또한, 제안된 시스템은 웹 어플리케이션을 위한 표준 저수준 인스트럭션인 웹 어셈블리 오프로드를 지원하여 순수한 JavaScript 코드 오프로드와 비교하여 최대 8.4 배의 속도 향상을 달성했습니다. 둘째, DNN 어플리케이션을 엣지 서버에 배포할 때, DNN 모델을 전송하는 동안 DNN 연산을 오프로드 하여 빠르게 성능향상을 달성할 수 있는 점진적 오프로드 방식을 제안합니다. 또한, 모바일 사용자가 방문 할 것으로 예상되는 엣지 서버로 DNN 레이어를 사전에 마이그레이션하여 콜드 스타트 성능을 향상시키는 방식을 제안 합니다. 오픈 소스 모빌리티 데이터셋을 이용한 시뮬레이션에서, DNN 모델을 배포하면서 발생하는 성능 저하를 제안 하는 방식이 크게 줄일 수 있음을 확인하였습니다.Chapter 1. Introduction 1 1.1 Offloading Web App Computations to Edge Servers 1 1.2 Offloading DNN Computations to Edge Servers 3 Chapter 2. Seamless Offloading of Web App Computations 7 2.1 Motivation: Computation-Intensive Web Apps 7 2.2 Mobile Web Worker System 10 2.2.1 Review of HTML5 Web Worker 10 2.2.2 Mobile Web Worker System 11 2.3 Migrating Web Worker 14 2.3.1 Runtime State of Web Worker 15 2.3.2 Snapshot of Mobile Web Worker 16 2.3.3 End-to-End Migration Process 21 2.4 Evaluation 22 2.4.1 Experimental Environment 22 2.4.2 Migration Performance 24 2.4.3 Application Execution Performance 27 Chapter 3. IONN: Incremental Offloading of Neural Network Computations 30 3.1 Motivation: Overhead of Deploying DNN Model 30 3.2 Background 32 3.2.1 Deep Neural Network 33 3.2.2 Offloading of DNN Computations 33 3.3 IONN For DNN Edge Computing 35 3.4 DNN Partitioning 37 3.4.1 Neural Network (NN) Execution Graph 38 3.4.2 Partitioning Algorithm 40 3.4.3 Handling DNNs with Multiple Paths. 43 3.5 Evaluation 45 3.5.1 Experimental Environment 45 3.5.2 DNN Query Performance 46 3.5.3 Accuracy of Prediction Functions 48 3.5.4 Energy Consumption. 49 Chapter 4. PerDNN: Offloading DNN Computations to Pervasive Edge Servers 51 4.1 Motivation: Cold Start Issue 51 4.2 Proposed Offloading System: PerDNN 52 4.2.1 Edge Server Environment 53 4.2.2 Overall Architecture 54 4.2.3 GPU-aware DNN Partitioning 56 4.2.4 Mobility Prediction 59 4.3 Evaluation 63 4.3.1 Performance Gain of Single Client 64 4.3.2 Large-Scale Simulation 65 Chapter 5. RelatedWorks 73 Chapter 6. Conclusion. 78 Chapter 5. RelatedWorks 73 Chapter 6. Conclusion 78 Bibliography 80Docto

How to Place Your Apps in the Fog -- State of the Art and Open Challenges

Fog computing aims at extending the Cloud towards the IoT so to achieve improved QoS and to empower latency-sensitive and bandwidth-hungry applications. The Fog calls for novel models and algorithms to distribute multi-service applications in such a way that data processing occurs wherever it is best-placed, based on both functional and non-functional requirements. This survey reviews the existing methodologies to solve the application placement problem in the Fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open-source prototypes, and on the size of test use cases. Second, it classifies the literature based on the application and Fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimised metrics. Finally, it identifies some open challenges in application placement in the Fog

A programmable structure for pervasive computing

This exstended abstract presents an asymmetric and programmable (extensible) approach to pervasive computing. The idea is to off-load computations from light portable clients into a back-bone of seamlessly integrated servers. This way, a user can extend and personalize his pervasive computational environment by installing computations following his trajectory throughout the day. Focus on this extended abstract is on structural issues related to the back-end servers running mobile code off-loaded from the mobile user