Cloud computing is highly being used for several years for various purpose s. From daily tasks, such as reading e-mails, watching videos to the factory automation and device control, it changed where the data is being processed and how it is accessed. However, increasing number of connected devices brings problems, such as low Quality of Service (QoS) due to infras-tructure resources and high latency because of the bandwidth limitations. The current tendency to solve the problems that the Cloud computing has is performing the computations as close as possible to the device. This paradigm is called Edge Computing. There are several proposed architectures for the Edge Computing, but there is no an accepted standard by the community or the industry. Besides, there is not a common agreement on how the Edge Computing architecture physically looks like. In this paper, we describe the Edge Computing, explain how its architecture looks like, its requirements, and enablers. We also define the major features that one Edge Server should support.
Inhaltsverzeichnis (Table of Contents)
- Abstract
- Introduction
- Architecture Design
- Requirements
- Conclusion and Future Work
- Acknowledgment
- References
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This paper aims to describe the architecture of Edge Computing, its requirements, and enablers, addressing challenges posed by the increasing number of connected IoT devices and the limitations of cloud computing. It explores the need for an extensible architecture to handle the growing demands of real-time data processing and control.
- The challenges of cloud computing in the face of a growing number of connected devices.
- The architecture of Edge Computing and its role as an intermediary between cloud and end devices.
- Key requirements for an effective and scalable Edge Computing architecture.
- The importance of extensibility and adaptability in Edge Computing systems.
- The enabling technologies and functionalities necessary for Edge Computing solutions.
Zusammenfassung der Kapitel (Chapter Summaries)
Abstract: This abstract introduces Edge Computing as a solution to the limitations of cloud computing in handling the increasing number of connected devices. It highlights the lack of a standardized architecture for Edge Computing and outlines the paper's aim to describe its architecture, requirements, and enablers, focusing on features an Edge Server should support.
Introduction: This chapter establishes the context for Edge Computing by illustrating the exponential growth of connected IoT devices and the resulting challenges for cloud computing, such as latency and reduced Quality of Service (QoS). It introduces Edge Computing as a paradigm that moves computing closer to the data source, presenting examples like aircraft and Formula One cars that generate massive amounts of data requiring near-real-time processing and analysis, highlighting the need for a solution like Edge Computing to mitigate these issues.
Architecture Design: This section details the proposed architecture of an extensible Edge Server, emphasizing its modular design. The architecture includes functionalities for real-time and non-real-time control and communication. The core node manages resources and task allocation, while additional modules can be added to extend functionalities. The design emphasizes scalability, enabling servers to share information about their capabilities and resources with neighbors for efficient task distribution based on factors such as deadlines. The figure illustrates a core node with expandable functionalities.
Requirements: This chapter outlines the essential requirements for an Edge Computing architecture, many of which are also pertinent to cloud computing. Key requirements highlighted include interoperability (connecting various devices and servers), scalability (adapting to varying numbers of users and sensors), extensibility (adapting to rapid technological advancements), abstraction (for flexible network topology), time-sensitivity (handling real-time and near-real-time operations), security and privacy, reliability, and intelligence (managing large data volumes from multiple sensors). This section establishes a comprehensive set of requirements to guide the development and implementation of a robust Edge Computing system.
Schlüsselwörter (Keywords)
Edge computing, Extensible Architecture, Internet of Things (IoT), Cloud Computing, Scalability, Real-time processing, QoS, Data aggregation, Modular design.
Frequently Asked Questions: Edge Computing Architecture, Requirements, and Enablers
What is the main topic of this paper?
This paper focuses on the architecture of Edge Computing, its requirements, and enabling technologies. It addresses the challenges posed by the increasing number of connected IoT devices and the limitations of traditional cloud computing in handling real-time data processing and control.
What are the key themes explored in the paper?
The paper explores the challenges of cloud computing in handling a growing number of connected devices, the architecture of Edge Computing as an intermediary between cloud and end devices, key requirements for a scalable Edge Computing architecture, the importance of extensibility and adaptability, and the enabling technologies for Edge Computing solutions.
What are the main components of the proposed Edge Computing architecture?
The proposed architecture features a modular and extensible Edge Server with a core node managing resources and task allocation. Additional modules can be added to extend functionalities. The design prioritizes scalability, allowing servers to share information about their capabilities and resources for efficient task distribution.
What are the key requirements for an effective Edge Computing architecture?
Essential requirements include interoperability (connecting various devices and servers), scalability (adapting to varying numbers of users and sensors), extensibility (adapting to rapid technological advancements), abstraction (for flexible network topology), time-sensitivity (handling real-time and near-real-time operations), security and privacy, reliability, and intelligence (managing large data volumes from multiple sensors).
What are the challenges addressed by Edge Computing?
The paper highlights the challenges of cloud computing in handling the exponential growth of connected IoT devices, specifically latency and reduced Quality of Service (QoS). Edge Computing addresses these by moving computation closer to the data source, enabling near real-time processing and analysis.
What are some examples illustrating the need for Edge Computing?
The paper uses examples such as aircraft and Formula One cars, which generate massive amounts of data requiring near real-time processing and analysis, to illustrate the need for a solution like Edge Computing to mitigate the limitations of cloud computing.
What is the structure of the paper?
The paper is structured into sections including an Abstract, Introduction, Architecture Design, Requirements, Conclusion and Future Work, Acknowledgement, and References. Each section provides a detailed explanation of its respective topic.
What are the key words associated with this paper?
Key words include: Edge computing, Extensible Architecture, Internet of Things (IoT), Cloud Computing, Scalability, Real-time processing, QoS, Data aggregation, and Modular design.
What is the overall goal of this research?
The overall goal is to describe a robust and scalable architecture for Edge Computing, outlining its key requirements and enabling technologies to effectively manage the increasing demands of real-time data processing and control from a growing number of connected IoT devices.
Where can I find more detailed information about the proposed architecture?
The "Architecture Design" chapter provides a detailed explanation of the proposed extensible Edge Server architecture, including its modular design, functionalities for real-time and non-real-time control and communication, and mechanisms for scalable resource allocation.
- Quote paper
- Ajit Singh (Author), 2018, Extensible Edge Computing Architecture, Munich, GRIN Verlag, https://www.grin.com/document/488802
