Edge computing

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Edge computing is a distributed computing model that brings computation and data storage closer to the sources of data, so that a user is likely to be physically closer to a server than if all servers were in one place. This often makes applications faster. [1] More broadly, it refers to any design that pushes computation physically closer to a user, so as to reduce the latency compared to when an application runs on a single data centre. [2]

Contents

The term began being used in the 1990s to describe content delivery networks—these were used to deliver website and video content from servers located near users. [3] In the early 2000s, these systems expanded their scope to hosting other applications, [4] leading to early edge computing services. [5] These services could do things like find dealers, manage shopping carts, gather real-time data, and place ads.

The Internet of Things (IoT), where devices are connected to the internet, is often linked with edge computing. However, it's important to understand that edge computing and IoT are not the same thing. [6]

The edge computing infrastructure Edge computing infrastructure.png
The edge computing infrastructure

Definition

Edge computing involves running computer programs that deliver quick responses close to where requests are made. Karim Arabi, during an IEEE DAC 2014 keynote [7] and later at an MIT MTL Seminar in 2015, described edge computing as computing that occurs outside the cloud, at the network's edge, particularly for applications needing immediate data processing. [8] Unlike data centers , edge computing environments are not always climate-controlled, despite requiring significant processing power. [9]

Edge computing is often equated with fog computing, particularly in smaller setups. [10] However, in larger deployments, such as smart cities, fog computing serves as a distinct layer between edge computing and cloud computing, with each layer having its own responsibilities. [11] [12]

"The State of the Edge" report explains that edge computing focuses on servers located close to the end-users. [13] Alex Reznik, Chair of the ETSI MEC ISG standards committee, defines 'edge' loosely as anything that's not a traditional data center. [14]

In cloud gaming, edge nodes, known as "gamelets," are typically within one or two network hops from the client, ensuring quick response times for real-time games. [15]

Edge computing might use virtualization technology to simplify deploying and managing various applications on edge servers. [16]

Concept

The world's data is expected to grow 61 percent to 175 zettabytes by 2025. [17] According to research firm Gartner, around 10 percent of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, the firm predicts that this figure will reach 75 percent. [18] The increase of IoT devices at the edge of the network is producing a massive amount of data - storing and using all that data in cloud data centers pushes network bandwidth requirements to the limit. [19] Despite the improvements of network technology, data centers cannot guarantee acceptable transfer rates and response times, which often is a critical requirement for many applications. [20] Furthermore, devices at the edge constantly consume data coming from the cloud, forcing companies to decentralize data storage and service provisioning, leveraging physical proximity to the end user.

In a similar way, the aim of edge computing is to move the computation away from data centers towards the edge of the network, exploiting smart objects, mobile phones, or network gateways to perform tasks and provide services on behalf of the cloud. [21] By moving services to the edge, it is possible to provide content caching, service delivery, persistent data storage, and IoT management resulting in better response times and transfer rates. At the same time, distributing the logic to different network nodes introduces new issues and challenges. [22]

Privacy and security

The distributed nature of this paradigm introduces a shift in security schemes used in cloud computing. In edge computing, data may travel between different distributed nodes connected through the Internet and thus requires special encryption mechanisms independent of the cloud. Edge nodes may also be resource-constrained devices, limiting the choice in terms of security methods. Moreover, a shift from centralized top-down infrastructure to a decentralized trust model is required. [23] On the other hand, by keeping and processing data at the edge, it is possible to increase privacy by minimizing the transmission of sensitive information to the cloud. Furthermore, the ownership of collected data shifts from service providers to end-users. [24]

Scalability

Scalability in a distributed network must face different issues. First, it must take into account the heterogeneity of the devices, having different performance and energy constraints, the highly dynamic condition, and the reliability of the connections compared to more robust infrastructure of cloud data centers. Moreover, security requirements may introduce further latency in the communication between nodes, which may slow down the scaling process. [20]

The state-of-the-art scheduling technique can increase the effective utilization of edge resources and scales the edge server by assigning minimum edge resources to each offloaded task. [25]

Reliability

Management of failovers is crucial in order to keep a service alive. If a single node goes down and is unreachable, users should still be able to access a service without interruptions. Moreover, edge computing systems must provide actions to recover from a failure and alert the user about the incident. To this aim, each device must maintain the network topology of the entire distributed system, so that detection of errors and recovery become easily applicable. Other factors that may influence this aspect are the connection technologies in use, which may provide different levels of reliability, and the accuracy of the data produced at the edge that could be unreliable due to particular environment conditions. [20] As an example, an edge computing device, such as a voice assistant, may continue to provide service to local users even during cloud service or internet outages. [24]

Speed

Edge computing brings analytical computational resources close to the end users and therefore can increase the responsiveness and throughput of applications. A well-designed edge platform would significantly outperform a traditional cloud-based system. Some applications rely on short response times, making edge computing a significantly more feasible option than cloud computing. Examples range from IoT to autonomous driving, [26] anything health or human / public safety relevant, [27] or involving human perception such as facial recognition, which typically takes a human between 370-620 ms to perform. [28] Edge computing is more likely to be able to mimic the same perception speed as humans, which is useful in applications such as augmented reality where the headset should preferably recognize who a person is at the same time as the wearer does.

Efficiency

Due to the nearness of the analytical resources to the end users, sophisticated analytical tools and Artificial Intelligence tools can run on the edge of the system. This placement at the edge helps to increase operational efficiency and is responsible for many advantages to the system.

Additionally, the usage of edge computing as an intermediate stage between client devices and the wider internet results in efficiency savings that can be demonstrated in the following example: A client device requires computationally intensive processing on video files to be performed on external servers. By using servers located on a local edge network to perform those computations, the video files only need to be transmitted in the local network. Avoiding transmission over the internet results in significant bandwidth savings and therefore increases efficiency. [28] Another example is voice recognition. If the recognition is performed locally, it is possible to send the recognized text to the cloud rather than audio recordings, significantly reducing the amount of required bandwidth. [24]

Applications

Edge application services reduce the volumes of data that must be moved, the consequent traffic, and the distance that data must travel. That provides lower latency and reduces transmission costs. Computation offloading for real-time applications, such as facial recognition algorithms, showed considerable improvements in response times, as demonstrated in early research. [29] Further research showed that using resource-rich machines called cloudlets or micro data centers near mobile users, which offer services typically found in the cloud, provided improvements in execution time when some of the tasks are offloaded to the edge node. [30] On the other hand, offloading every task may result in a slowdown due to transfer times between device and nodes, so depending on the workload, an optimal configuration can be defined.

IoT-based power grid system enables communication of electricity and data to monitor and control the power grid, [31] which makes energy management more efficient.

Other notable applications include connected cars, autonomous cars, [32] smart cities, [33] Industry 4.0, home automation [34] and satellite systems. [35] The nascent field of edge artificial intelligence (edge AI) implements the artificial intelligence in an edge computing environment, on the device or close to where data is collected. [36]

See also

Related Research Articles

<span class="mw-page-title-main">Client–server model</span> Distributed application structure in computing

The client–server model, also known as client server network architecture, is a distributed application structure that partitions tasks or workloads between the providers of a resource or service, called servers, and service requesters, called clients. Often clients and servers communicate over a computer network on separate hardware, but both client and server may reside in the same system. A server host runs one or more server programs, which share their resources with clients. A client usually does not share any of its resources, but it requests content or service from a server. Clients, therefore, initiate communication sessions with servers, which await incoming requests. Examples of computer applications that use the client–server model are email, network printing, and the World Wide Web.

In telecommunication, provisioning involves the process of preparing and equipping a network to allow it to provide new services to its users. In National Security/Emergency Preparedness telecommunications services, "provisioning" equates to "initiation" and includes altering the state of an existing priority service or capability.

<span class="mw-page-title-main">Content delivery network</span> Layer in the internet ecosystem addressing bottlenecks

A content delivery network or content distribution network (CDN) is a geographically distributed network of proxy servers and their data centers. The goal is to provide high availability and performance by distributing the service spatially relative to end users. CDNs came into existence in the late 1990s as a means for alleviating the performance bottlenecks of the Internet as the Internet was starting to become a mission-critical medium for people and enterprises. Since then, CDNs have grown to serve a large portion of the Internet content today, including web objects, downloadable objects, applications, live streaming media, on-demand streaming media, and social media sites.

Mutual authentication or two-way authentication refers to two parties authenticating each other at the same time in an authentication protocol. It is a default mode of authentication in some protocols and optional in others (TLS).

Vehicular ad hoc networks (VANETs) are created by applying the principles of mobile ad hoc networks (MANETs) – the spontaneous creation of a wireless network of mobile devices – to the domain of vehicles. VANETs were first mentioned and introduced in 2001 under "car-to-car ad-hoc mobile communication and networking" applications, where networks can be formed and information can be relayed among cars. It was shown that vehicle-to-vehicle and vehicle-to-roadside communications architectures will co-exist in VANETs to provide road safety, navigation, and other roadside services. VANETs are a key part of the intelligent transportation systems (ITS) framework. Sometimes, VANETs are referred as Intelligent Transportation Networks. They are understood as having evolved into a broader "Internet of vehicles". which itself is expected to ultimately evolve into an "Internet of autonomous vehicles".

The Internet of things (IoT) describes devices with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. The Internet of things encompasses electronics, communication, and computer science engineering. "Internet of things" has been considered a misnomer because devices do not need to be connected to the public internet; they only need to be connected to a network and be individually addressable.

Cyber–Physical System (CPS) are integrations of computation with physical processes. In cyber–physical systems, physical and software components are deeply intertwined, able to operate on different spatial and temporal scales, exhibit multiple and distinct behavioral modalities, and interact with each other in ways that change with context. CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science. The process control is often referred to as embedded systems. In embedded systems, the emphasis tends to be more on the computational elements, and less on an intense link between the computational and physical elements. CPS is also similar to the Internet of Things (IoT), sharing the same basic architecture; nevertheless, CPS presents a higher combination and coordination between physical and computational elements.

<span class="mw-page-title-main">Cloud computing</span> Form of shared Internet-based computing

Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center. Cloud computing relies on sharing of resources to achieve coherence and typically uses a pay-as-you-go model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users.

Mobile Cloud Computing (MCC) is the combination of cloud computing and mobile computing to bring rich computational resources to mobile users, network operators, as well as cloud computing providers. The ultimate goal of MCC is to enable execution of rich mobile applications on a plethora of mobile devices, with a rich user experience. MCC provides business opportunities for mobile network operators as well as cloud providers. More comprehensively, MCC can be defined as "a rich mobile computing technology that leverages unified elastic resources of varied clouds and network technologies toward unrestricted functionality, storage, and mobility to serve a multitude of mobile devices anywhere, anytime through the channel of Ethernet or Internet regardless of heterogeneous environments and platforms based on the pay-as-you-use principle."

Software-defined networking (SDN) is an approach to network management that enables dynamic and programmatically efficient network configuration to improve network performance and monitoring in a manner more akin to cloud computing than to traditional network management. SDN is meant to improve the static architecture of traditional networks and may be employed to centralize network intelligence in one network component by disassociating the forwarding process of network packets from the routing process. The control plane consists of one or more controllers, which are considered the brains of the SDN network, where the whole intelligence is incorporated. However, centralization has certain drawbacks related to security, scalability and elasticity.

A distributed file system for cloud is a file system that allows many clients to have access to data and supports operations on that data. Each data file may be partitioned into several parts called chunks. Each chunk may be stored on different remote machines, facilitating the parallel execution of applications. Typically, data is stored in files in a hierarchical tree, where the nodes represent directories. There are several ways to share files in a distributed architecture: each solution must be suitable for a certain type of application, depending on how complex the application is. Meanwhile, the security of the system must be ensured. Confidentiality, availability and integrity are the main keys for a secure system.

Computation offloading is the transfer of resource intensive computational tasks to a separate processor, such as a hardware accelerator, or an external platform, such as a cluster, grid, or a cloud. Offloading to a coprocessor can be used to accelerate applications including: image rendering and mathematical calculations. Offloading computing to an external platform over a network can provide computing power and overcome hardware limitations of a device, such as limited computational power, storage, and energy.

Fog computing or fog networking, also known as fogging, is an architecture that uses edge devices to carry out a substantial amount of computation, storage, and communication locally and routed over the Internet backbone.

Multi-access edge computing (MEC), formerly mobile edge computing, is an ETSI-defined network architecture concept that enables cloud computing capabilities and an IT service environment at the edge of the cellular network and, more in general at the edge of any network. The basic idea behind MEC is that by running applications and performing related processing tasks closer to the cellular customer, network congestion is reduced and applications perform better. MEC technology is designed to be implemented at the cellular base stations or other edge nodes, and enables flexible and rapid deployment of new applications and services for customers. Combining elements of information technology and telecommunications networking, MEC also allows cellular operators to open their radio access network (RAN) to authorized third parties, such as application developers and content providers.

A cloudlet is a mobility-enhanced small-scale cloud datacenter that is located at the edge of the Internet. The main purpose of the cloudlet is supporting resource-intensive and interactive mobile applications by providing powerful computing resources to mobile devices with lower latency. It is a new architectural element that extends today's cloud computing infrastructure. It represents the middle tier of a 3-tier hierarchy: mobile device - cloudlet - cloud. A cloudlet can be viewed as a data center in a box whose goal is to bring the cloud closer. The cloudlet term was first coined by M. Satyanarayanan, Victor Bahl, Ramón Cáceres, and Nigel Davies, and a prototype implementation is developed by Carnegie Mellon University as a research project. The concept of cloudlet is also known as follow me cloud, and mobile micro-cloud.

The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.

Fog robotics can be defined as an architecture which consists of storage, networking functions, control with fog computing closer to robots.

Dew computing is an information technology (IT) paradigm that combines the core concept of cloud computing with the capabilities of end devices. It is used to enhance the experience for the end user in comparison to only using cloud computing. Dew computing attempts to solve major problems related to cloud computing technology, such as reliance on internet access. Dropbox is an example of the dew computing paradigm, as it provides access to the files and folders in the cloud in addition to keeping copies on local devices. This allows the user to access files during times without an internet connection; when a connection is established again, files and folders are synchronized back to the cloud server.

<span class="mw-page-title-main">IoT forensics</span> Branch of digital forensics

IoT forensics is a branch of digital forensics that has the goal of identifying and extracting digital information from devices belonging to the Internet of things field, using a forensically sound and legally acceptable process.

The Internet of Musical Things is a research area that aims to bring Internet of Things connectivity to musical and artistic practices. Moreover, it encompasses concepts coming from music computing, ubiquitous music, human-computer interaction, artificial intelligence, augmented reality, virtual reality, gaming, participative art, and new interfaces for musical expression. From a computational perspective, IoMusT refers to local or remote networks embedded with devices capable of generating and/or playing musical content.

References

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  8. ↑ MIT MTL Seminar: Trends, Opportunities and Challenges Driving Architecture and Design of Next Generation Mobile Computing and IoT Devices
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  24. 1 2 3 3 Advantages of Edge Computing. Aron Brand. Medium.com. Sep 20, 2019
  25. ↑ Babar, Mohammad; Sohail Khan, Muhammad (July 2021). "ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems". International Journal of Distributed Sensor Networks. 17 (7): 155014772110353. doi: 10.1177/15501477211035332 . ISSN   1550-1477. S2CID   236917011.
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  28. 1 2 Satyanarayanan, Mahadev (January 2017). "The Emergence of Edge Computing". Computer. 50 (1): 30–39. doi:10.1109/MC.2017.9. ISSN   1558-0814. S2CID   12563598.
  29. ↑ Yi, S.; Hao, Z.; Qin, Z.; Li, Q. (November 2015). "Fog Computing: Platform and Applications". 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb). pp. 73–78. doi:10.1109/HotWeb.2015.22. ISBN   978-1-4673-9688-2. S2CID   6753944.
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