Conceptes and Methods for Edge Computing

快速增长的物联网(IoT)渗透到行业和社会。未来的智能医院、生产工厂或整个城市可能包含数千、甚至数百万个传感器和智能对象。为了提供被认为是智能的功能，必须在越来越多的实时约束下高效地处理越来越大量的数据。在广泛使用的两层结构中(参见移动云计算)、计算密集型或多设备/用户任务被从资源受限的设备卸载到云，其严重缺点如下。(1)在实时条件下，无法容忍无限的通信时延波动和间歇性连接。(2)数据预处理不充分，浪费网络资源，加剧拥塞。(3)隐私和安全问题比在数据源的前提或附近更难解决。该项目的关键目标是进一步推广最近推广的边缘计算范例，该范例将两层设备云设置进一步推向三层设备边缘云计算设置。虽然它有潜力消除上述缺点，但仍然存在相当大的研究挑战。我们将开发和改进算法、协议和机制，以及统一模型，作为集成开发和运行时支持的基础。关键目标分为四个分目标；这些分目标和计划贡献的缩写如下。(1)虚拟化：用于虚拟化的新型轻量级容器的方法，支持将应用程序模块映射到计算资源及其迁移，尽管功能差异很大，为边缘计算提供高度动态的运行时支持。(2)控制：基于软件定义网络(SDN)和网络功能虚拟化(NFV)的最新进展构建了新的配置和控制协议和算法，支持灵活管理边缘环境中的异类设备，并可扩展部署设备边缘云应用。(3)优化：将(支持虚拟化的)应用程序模块高效地在线映射到满足多个优化目标的资源上，并动态适应位置、负载和资源成本的变化。(4)通信：通信层，具有统一的通信框架和高效的协议和机制，根据相关的内容类(可执行文件、媒体流、复杂事件流)、跳数类(边缘内、设备边缘、边缘云)、服务质量要求和网络接入技术进行调整。项目成果将帮助开发人员通过统一模型从边缘计算的技术问题中抽象出来，并将以其新颖的算法、协议和机制以运行时自适应的方式实现对这些问题的复杂自动处理。我们认为它对培育大规模智能物联网应用具有开创性意义。

The fast-growing Internet-of-Things (IoT) penetrates industry and society. Future smart hospitals, production plants or entire cities may comprise thousands, even millions of sensors and smart objects. Ever larger amounts of data must be processed efficiently, increasingly under real time constraints, to provide the functionality deemed smart. In the widely used two-tier setup (cf. mobile-cloud computing), computationally intensive or multi-device/user tasks are offloaded from resource-constraint devices to the cloud, with serious shortcomings as follows. (1) Unbounded communication delay fluctuations and intermittent connectivity are intolerable under real-time conditions. (2) Insufficient pre-processing of data wastes network resources and aggravates congestion. (3) Privacy and security issues are harder to solve than on premise or in proximity of the data source. Key goal of the project is the furthering of the recently propagated edge computing paradigm which furthers the two-tier device-cloud setup towards a three-tier device-edge-cloud computing setup. While it has the potential to remove the above shortcomings, considerable research challenges remain. We will develop and advance algorithms, protocols and mechanisms, as well as unifying models as a basis for integrated development and runtime support. The key goal is divided into four sub-goals; the short-form of these sub-goals and planned contributions are as follows. (1) Virtualization: approaches to novel lightweight containers for virtualization that enable the mapping of application modules onto computing resources and their migration despite highly varying capabilities, providing highly dynamic runtime support for edge computing. (2) Control: new configuration and control protocols and algorithms built upon recent advancements in Software Defined Networking (SDN) and Network Function Virtualization (NFV), enabling flexible management of heterogeneous devices in the edge environment and scalable deployment of device-edge-cloud applications. (3) Optimization: efficient online graph based mapping of (virtualization-enabled) application modules onto resources subject to multiple optimization objectives, with dynamic adaptation to location, load, and resource cost changes. (4) Communication: a communication layer with a unified communication framework and efficient protocols and mechanisms, adjusting to relevant content classes (executables, media streams, complex event streams), hop classes (intra-edge, device-edge, edge-cloud), quality-of-service requirements, and network access technologies. The project outcome will help developers abstract from technical issues of edge computing by means of the unifying model, and it will enable the sophisticated automatic handling of these issues in a runtime-adaptive manner with its novel algorithms, protocols and mechanisms. We deem it seminal for fostering large-scale smart IoT applications.