1 INTRODUCTION

We are pleased to present a special issue that focuses on state-of-the-art research on Elastic Computing from Edge to the Cloud Environments. Today, a huge amount of data is being generated by the Internet of Things (IoT) devices such as smartphones, sensors, cameras, cars, and robots.¹ In order to process the generated data, there exist Big Data platforms (such as Hadoop and Spark). Conventionally, they are deployed in centralized Data Centers, which, however, fall short of addressing time-critical requirements of the applications due to high latency between the Edge, where the data are generated and the Data Centers where they are processed.² The emerging Edge and Fog computing paradigms promise to solve this problem by seamlessly integrating hardware and software resources across multiple computing tiers, from the Edge to the Data Center/Cloud. Since computing resources at the Edge may be power and capacity constrained, it is necessary to invent new lightweight platforms and techniques that seamlessly interact, sense, execute and produce results with very low latency, while at the same time address other high-level requirements of applications, such as security and privacy. Regarding these problems, there are many challenges that must be addressed with the invention of new architectures, methods, algorithms, and solutions.

This special issue addresses various problems, challenges, new approaches and technologies to shape future directions for research, foster the exchange of ideas. In this special issue, six research articles are carefully selected after multiple rounds of peer-review to address some new and upcoming avenues of next-generation clouds. They include:

• Integrate and process data from underlying IoT platforms and services
• Improve the energy-efficient management of resources and tasks processing
• Address the Quality of Service (QoS) and time-critical aspects of smart applications
• Facilitate intelligent integration of information arising from various sources
• Address the requirements of very dynamic Big Data pipelines (e.g., moving smartphones, sensors, cars, robots with dynamically changing requirements for processing)
• Provide orchestration methods and scheduling policies that address dependability, reliability, availability and other high-level application requirements
• Adequately address the inherent variability of resources from the Edge to the Data Centers
• Provide new architectures which use the powerful computing resources of Data Centers, while at the same time providing optimal QoS to applications
• Implement distributed Artificial Intelligence methods from the Edge to the Data Centre/Cloud

中文翻译：

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特刊：从边缘到云环境的弹性计算

1 介绍

我们很高兴推出一期特刊，重点关注从边缘到云环境的弹性计算的最新研究。如今，智能手机、传感器、相机、汽车和机器人等物联网 (IoT) 设备正在生成大量数据。¹为了处理生成的数据，存在大数据平台（如Hadoop和Spark）。传统上，它们部署在集中式数据中心，然而，由于生成数据的边缘和处理数据的数据中心之间的高延迟，无法满足应用程序的时间关键要求。²新兴的边缘和雾计算范式有望通过跨多个计算层（从边缘到数据中心/云）无缝集成硬件和软件资源来解决这个问题。由于边缘的计算资源可能受到功率和容量的限制，因此有必要发明新的轻量级平台和技术，以极低的延迟无缝交互、感知、执行和产生结果，同时满足其他高级别的要求应用程序，例如安全和隐私。关于这些问题，必须通过新架构、方法、算法和解决方案的发明来解决许多挑战。

本期特刊解决了各种问题、挑战、新方法和技术，以塑造未来的研究方向，促进思想交流。在本期特刊中，经过多轮同行评审后精心挑选了六篇研究文章，以探讨下一代云的一些新途径和即将到来的途径。它们包括：

• 集成和处理来自底层物联网平台和服务的数据
• 改进资源和任务处理的节能管理
• 解决智能应用程序的服务质量 (QoS) 和时间关键问题
• 促进各种来源信息的智能整合
• 满足动态大数据管道的需求（例如，移动智能手机、传感器、汽车、具有动态变化处理需求的机器人）
• 提供满足可靠性、可靠性、可用性和其他高级应用程序需求的编排方法和调度策略
• 充分解决从边缘到数据中心的资源固有可变性
• 提供新的架构，利用数据中心强大的计算资源，同时为应用程序提供最佳的 QoS
• 实施从边缘到数据中心/云的分布式人工智能方法