InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

Seamless mobility with personal servers

We describe the concept and the taxonomy of personal servers, and their implications in seamless mobility. Personal servers could offer electronic services independently of network availability or quality, provide a greater flexibility in the choice of user access device, and support the key concept of continuous user experience. We describe the organization of mobile and remote personal servers, define three relevant communication modes, and discuss means for users to exploit seamless services on the personal server

Condor services for the Global Grid:interoperability between Condor and OGSA

In order for existing grid middleware to remain viable it is important to investigate their potentialfor integration with emerging grid standards and architectural schemes. The Open Grid ServicesArchitecture (OGSA), developed by the Globus Alliance and based on standard XML-based webservices technology, was the first attempt to identify the architectural components required tomigrate towards standardized global grid service delivery. This paper presents an investigation intothe integration of Condor, a widely adopted and sophisticated high-throughput computing softwarepackage, and OGSA; with the aim of bringing Condor in line with advances in Grid computing andprovide the Grid community with a mature suite of high-throughput computing job and resourcemanagement services. This report identifies mappings between elements of the OGSA and Condorinfrastructures, potential areas of conflict, and defines a set of complementary architectural optionsby which individual Condor services can be exposed as OGSA Grid services, in order to achieve aseamless integration of Condor resources in a standardized grid environment

Enabling Personalized Composition and Adaptive Provisioning of Web Services

The proliferation of interconnected computing devices is fostering the emergence of environments where Web services made available to mobile users are a commodity. Unfortunately, inherent limitations of mobile devices still hinder the seamless access to Web services, and their use in supporting complex user activities. In this paper, we describe the design and implementation of a distributed, adaptive, and context-aware framework for personalized service composition and provisioning adapted to mobile users. Users specify their preferences by annotating existing process templates, leading to personalized service-based processes. To cater for the possibility of low bandwidth communication channels and frequent disconnections, an execution model is proposed whereby the responsibility of orchestrating personalized processes is spread across the participating services and user agents. In addition, the execution model is adaptive in the sense that the runtime environment is able to detect exceptions and react to them according to a set of rules

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

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research