Computing Systems

A Comparative Research on Open Source Edge
Computing Systems
Jiayue Liang, Fang Liu(&), Shen Li, and Zhenhua Cai
Sun Yat-sen University, Guangzhou 510006, China
liangjy77,lish286,caizhh8@mail2.sysu.edu.cn,
liufang25@mail.sysu.edu.cn
Abstract. With the development of edge computing, open source communities
have put forward several edge computing systems. This paper discussed about
edge computing and its current situation, then presented typical open source
edge computing systems such as EdgeX Foundry, Azure IoT Edge, CORD,
Apache Edgent and Akraino Edge Stack, and gave a comparison about them on
their characteristics. A comparison study on their characteristics were given to
help users to understand these open source edge computing systems and make
choices.
Keywords: Edge computing systems Open source
1 Introduction
Cloud computing is the dominated computing paradigm in the past decade, providing
efficient, centralized computing platform for big data. Recently, with the development
of Internet of Things (IoT) [1] and the popularization of 4G/5G, massive devices are
connected into the network, and a huge amount of data are generated all the time,
which challenges the linearly increasing capability of cloud computing. Meanwhile,
many applications of IoT demand lower response time and improved security, and the
cloud computing cannot meet the requirements. Under this background, edge computing [2] is put forward to solve these problems. Edge computing takes advantage of
the compute resources of edge nodes to perform computation so that the data can be
processed at the edge. Several software systems for edge computing have been
designed to help the popularization of edge computing, such as cloudlet [3] and
Firework [4]. Open source communities began to develop open source systems such as
EdgeX Foundry [5] and Apache Edgent [6]. However, there is no public literature to
help users to select and use these open source edge computing systems. In this paper,
our research analyzes several open source systems for edge computing to provide an
overview of these systems.
The remaining parts of this paper are organized as follows. In Sect. 2, we discuss
the current situation of edge computing. In Sect. 3, we present five open source edge
computing systems. In Sect. 4, we give a comparison about these open source systems.
Finally, Sect. 5 summarizes our conclusions.
© Springer Nature Switzerland AG 2019
X. Sun et al. (Eds.): ICAIS 2019, LNCS 11633, pp. 157–170, 2019.
https://doi.org/10.1007/978-3-030-24265-7_14
2 Edge Computing
Edge computing is a new paradigm and it calls for technologies to enable computation
to be performed at the edge of the network in close proximity to data sources. W. Shi
et al. gave their definition on “edge” as “any computing and network resources along
the path between data sources and cloud data centers” [2]. Edge computing is expected
to solve problems that challenge cloud computing. One problem is the pressure of
bandwidth. Not all the data from the IoT objects are necessary to be sent to the cloud
for processing, and most of them can be processed at the edge. Edge computing
performs computing tasks on edge devices to process part of the data so as to reduce the
data volume sent to the cloud and the pressure of bandwidth. Another problem is the
latency. Centralized processing in the cloud may not meet the need of real-time processing for IoT applications. Edge computing processes data at the edge so as to get a
shorter response time. The third problem is security and privacy [7]. In many cases, the
data are not expected to share due to privacy concern and a local solution is needed.
Edge computing provides support to process data at the edge without sending them to
the cloud. Edge computing has a wide application area such as smart city, smart home,
intelligence manufacturing and lots of other use cases in IoT.
Edge computing has obtained a rapid development. In 2015, the Open Edge
Computing initiative [8] was published by Vodafone, Intel, Huawei and Carnegie
Mellon University. Then the Open Fog Consortium [9] was established by Cisco,
Microsoft, Intel, Dell, ARM and Princeton University. In 2016, the Edge Computing
Consortium (ECC) [10] was established in Beijing by Huawei, Chinese Academy of
Sciences, Intel, ARM, etc. What’s more, software systems developed specifically for
edge computing were launched gradually such as Cloudlet, ParaDrop [11] and Firework. A cloudlet is a small-scale cloud datacenter at the edge of the network, which
provides compute resources to support running interactive mobile applications provided by mobile devices with low latency [3]. ParaDrop, as an edge computing
framework, leverages the computing resource of wireless gateways or access points at
the edge for developers to create and run services. Firework focus on the data sharing
problem among the applications for the Internet of Everything (IOE), and put forward a
computing framework to enable distributed data processing and sharing among multiple stakeholders such as edge nodes or the cloud in the hybrid cloud-edge environment [4]. As for commercial companies, especially cloud service providers, Amazon
published AWS Greengrass [12], which can deploy applications to local devices for
data processing with the help of the AWS Cloud capabilities such as deployment and
management. Microsoft and Alibaba Cloud published solutions namely Azure IoT
Edge [13] and Link IoT Edge [14] respectively, which are similar to AWS Greengrass
with the concept “hybrid cloud-edge analytics”. Open source communities also launched several open source edge computing systems, and we present them in Sect. 3.
Open source edge computing systems, supported by multiple companies with latest
technologies, accelerate the development of edge computing. It is meaningful to perform a study on these systems so as to help users to understand these systems. In this
paper, we studied five of these systems in detail.
158 J. Liang et al.
3 Open Source Edge Computing Systems
The Linux Foundation published two projects, EdgeX Foundry in 2017 and Akraino
Edge Statck [15] in 2018. The Open Network Foundation (ONF) launched a project
namely CORD (Central Office Re-architected as a Datacenter) [16]. The Apache
Software Foundation published Apache Edgent. Microsoft published Azure IoT Edge
in 2017 and announced it was available and open source in 2018.
Among these open source edge computing systems, EdgeX Foundry and Apache
Edgent focus on IoT, they try to solve problems which bring difficulties to practical
application of edge computing in IoT. Azure IoT Edge provides with hybrid cloudedge analytics, which helps to migrate cloud solutions to IoT devices. CORD and
Akraino Edge Stack focus on providing edge cloud services.
3.1 EdgeX Foundry
EdgeX Foundry is a standardized interoperability framework for IIoT edge computing,
whose sweet spots are edge nodes such as gateways, hubs, routers [5]. It can connect
with various sensors and devices via different protocols, manage them and collect data
from them, and export the data to a local application at the edge or the cloud for further
processing. IIoT, where a huge amount of data are produced all the time, has urgent
need for combination with edge computing. However, the diversity of hardware,
software and communication protocols among sensors and devices in IIoT brings a
great difficulty on managing them and collecting data from them. EdgeX Foundry is
such a framework to provide interoperability to solve this problem, aiming to simplify
and standardize the foundation of computing architectures in the IIoT market, and
create an ecosystem of interoperable components. EdgeX is designed to be agnostic to
hardware, CPU, operating system, and application environment. It can run natively or
run in docker containers.
Figure 1 [5] shows the architecture of EdgeX Foundry. “South side” at the bottom
of the figure includes “all IoT objects, within the physical realm, and the edge of the
network that communicates directly with those devices, sensors, actuators, and other
IoT objects, and collects the data from them” [5]. Relatively, “north side” at the top of
the figure includes “the Cloud (or Enterprise system) where data are collected, stored,
aggregated, analyzed, and turned into information, and the part of the network that
communicates with the Cloud” [5]. EdgeX Foundry connects these two sides regardless
of the differences of hardware, software and network. EdgeX tries to unify the
manipulation method of the IoT objects from South Side to a common API, so that
those objects can be manipulated in the same way by the applications of North Side.
EdgeX uses a Device Profile to describe a south side object. A Device Profile
defines the type of the object, the format of data that the object provides, the format of
data to be stored in EdgeX and the commands used to manipulate this object. Each
Device Profile involves with a Device Service, which is a service that converts the
format of the data, and translates the commands into instructions that IoT objects know
how to execute. EdgeX provides SDK for developers to create Device Services, so that
it can support for any combination of device interfaces and protocols by programming.
A Comparative Research on Open Source Edge Computing Systems 159
EdgeX consists of a collection of microservices, which allows services to scale up
and down based on device capability. These microservices can be grouped into four
service layers and two underlying augmenting system services as depicted in Fig. 1.
The four layers are Device Services Layer, Core Services Layer, Supporting Services
Layer and Export Services Layer. Two underlying augmenting system services are
System Management and Security. Each layer consists of several components and all of
these components use a common Restful API for configuration.
Device Services Layer. This layer consists of Device Services. According to the
Device Profiles, Device Service Layer converts the format of the data, sends them to
Core Services Layer, and translates the command requests from Core Services Layer.
Core Services Layer. This layer consists of four components Core Data, Command,
Metadata, Registry and Configuration. Core Data is a persistence repository as well as a
management service. It stores and manages the data collected from the south side
objects. Command is a service to offer the API for command requests from north side
to Device Services. Metadata is a repository and management service for metadata
about IoT objects. For example, the Device Profiles are uploaded and stored in
Metadata. Registry and Configuration provides centralized management of configuration and operating parameters for other microservices.
Supporting Services Layer. This layer is designed to provide edge analytics and
intelligence [5]. Now the Rules Engine, Alerting and Notification, Scheduling and
Logging microservices are implemented. We can set a target range of data to trigger a
specific device actuation as a rule and Rules Engine helps to realize by monitoring the
incoming data. Alerting and Notifications can send notifications or alerts to another
system or person by email, REST callback or other methods when a urgent actuation or
Fig. 1. Architecture of EdgeX Foundry
160 J. Liang et al.
a service malfunction happens. Scheduling can set up a timer to regularly clean up the
stale data. Logging is used to record the running information of EdgeX.
Export Services Layer. This layer connects EdgeX with North Side and consists of
Client Registration and Export Distribution. Client Registration enables clients like a
specific Cloud or a local application to register as recipients of data from Core Data.
Export Distribution distributes the data to the Clients registered in Client Registration.
System Management and Security. System Management provides with management
operations including installation, upgrade, start, stop and monitoring as EdgeX is
scalable and can be deployed dynamically. Security is designed to protect the data and
command of IoT objects connected with EdgeX Foundry.
EdgeX is designed for use cases which deal with multitudes of sensors or devices,
such as automated factories, machinery systems and lots of other cases in IoT. Now
EdgeX Foundry is in the rapid update phase, more features will be added in future
release. An EdgeX UI is in development as a web-based interface to add and manage
the device.
3.2 Azure IoT Edge
Azure IoT Edge, provided by Microsoft Azure as a cloud service provider, tries to
move cloud analytics to edge devices. These edge devices can be routers, gateways or
other devices which can provide compute resources. The programming model of Azure
IoT Edge is the same as that of other Azure IoT services [17] in the cloud, which
enables user to move their existing application from Azure to the edge devices for
lower latency. The convenience simplifies the development of edge applications. In
addition, Azure services like Azure Functions, Azure Machine Learning and Azure
Stream Analytics can be used to deploy complex tasks on the edge devices such as
machine learning, image recognition and other tasks about artificial intelligence.
Azure IoT Edge consists of three components: IoT Edge modules, IoT Edge runtime and a cloud-based interface as depicted in Fig. 2. The first two components run on
edge devices, the last one is an interface in the cloud. IoT Edge modules are containerized instances running the customer code or Azure services. IoT Edge runtime
manages these modules. The cloud-based interface is used to monitor and manage the
former two components, in other words, monitor and manage the edge devices.
IoT Edge modules are the places that run specific applications as the units of
execution. A module image is a docker image containing the user code. A module
instance, as a docker container, is a unit of computation that running the module image.
If the resources of edge devices supports, these modules can run the same Azure
services or custom application as in the cloud because of the same programing model.
In addition, these modules can be deployed dynamically as Azure IoT Edge is scalable.
IoT Edge runtime acts as a manager on the edge devices. It consists of two
modules, IoT Edge hub and IoT Edge agent. IoT Edge hub acts as a local proxy for IoT
Hub which is a managed service as a central message hub in the cloud, and is
responsible for communication. As a message broker, IoT Edge hub helps modules
communicate with each other, and transport data to IoT Hub. IoT Edge agent is used to
deploy and monitor the IoT Edge modules. It receives the deployment information
A Comparative Research on Open Source Edge Computing Systems 161
about modules from IoT Hub, instantiates these modules, and ensures they are running,
for example, restarts the crashed modules. In addition, it reports the status of the
modules to the IoT hub.
IoT Edge cloud interface is provided for device management. By this interface,
users can create edge applications, then send these applications to the device and
monitor the running status of the device. This monitoring function is useful for use
cases with massive devices, users can deploy applications to devices on a large scale
and monitor these devices.
A simple deployment procedure for applications is that, users choose a Azure
service or write their own code as an application, then build it as an IoT Edge module
image, and deploy this module image to the edge device with the help of the IoT Edge
interface. Then the IoT Edge runtime receives the deployment information, pulls the
module image and instantiates the module instance.
Azure IoT Edge has wide application area, now it has application cases on intelligent manufacturing, irrigation system, drone management system and so on. It’s
worth noting that Azure IoT Edge is open-source but the Azure services charge for fee.
3.3 CORD
CORD is an open source project of ONF initiated by AT&T and is designed for
network operators. Current network infrastructure is built with closed proprietary
integrated systems provided by network equipment providers. Due to the closed
property, the network capability cannot scale up and down dynamically. And the lack
of flexibility results in inefficient utilization of the compute and networking resources.
CORD plans to reconstruct the edge network infrastructure to build datacenters with
SDN [18], NFV [19] and Cloud technologies. It attempts to slice the compute, storage
and network resources so that these datacenters can act as clouds at the edge, providing
agile services for end-user customers.
Azure IoT edge device
Data
Analystics
Meachine
Learning
Azure
Fuctions
Module
Module
Module
IoT Edge cloud
interface
IoT Edge
agent
instantiate module
ensure running

IoT Edge
hub
data
data
sensors and IoT Edge runtime
devices
Fig. 2. Diagram of Azure IoT Edge
162 J. Liang et al.
CORD is an integrated system built from commodity hardware and open source
software. Figure 3 [16] shows the hardware architecture of CORD. It uses commodity
servers that are interconnected by a Fabric of White-box switches. These commodity
servers provide with compute, storage resources and the fabric of switches is used to
build the network. This switching fabric is organized to a Spine-Leaf topology rather
than traditional three-tier network topology, because it can provide scalable throughput
for greater East-to-West network traffic. In addition, specialized access hardware is
required to connect subscribers. The subscribers can divided into three categories for
different use cases, mobile subscribers, enterprise subscribers and residential subscribers. Each category demands different access hardware due to different access
technology. In terms of software, Fig. 4 [16] shows the software architecture of CORD.
Based on the servers and the fabric of switches, OpenStack provides with IaaS capability
for CORD, it manages the compute, storage and networking resources as well as creating virtual machines and virtual networks. Docker is used to run services in containers
for isolation. ONOS (Open Network Operating System) is a network operating system
which is used to manage network components like the switching fabric and provide
communication services to end-users. XOS provides a control plane to assemble and
compose services. Other software projects provide with component capabilities, for
example, vRouter (Virtual Router) provides with virtual routing functionality.
The edge of the operator network is a sweet spot for edge computing because it
connects customers with operators and is close to customers’ applications as data
sources. CORD takes edge computing into consideration and moves to support edge
computing as a platform to provide edge cloud services from the 4.1 release version.
CORD can be deployed into three solution, M-CORD (Mobile CORD), R-CORD
(Residential CORD) and E-CORD (Enterprise CORD) for different use cases. M-CORD
focus on mobile network, especially 5G network, it plans to disaggregate and virtualize
cellular network functions to enable services be created and scaled dynamically.
Fig. 3. Hardware architecture of CORD
A Comparative Research on Open Source Edge Computing Systems 163
This agility helps to provide multi-access edge services for mobile applications. For
those use cases like driverless cars or drones, users can rent the edge service to run their
edge applications. Similarly, R-CORD and E-CORD are designed to be agile service
delivery platforms but for different users, residential and enterprise user relatively.
So far, deployment of CORD is still in test among network operators, and it needs
more researches to combine CORD with edge applications.
3.4 Apache Edgent
Apache Edgent, which was known as Apache Quarks previously, is an Apache Incubator project at present. It is an open source programming model and lightweight
runtime for data analytics, used in small devices such as routers and gateways at the
edge. Apache Edgent focuses on data analytics at the edge, aiming to accelerate the
development of data analysis.
As is a programming model, Edgent provides API to build edge applications.
Figure 5 illustrates the model of the Edgent applications. Edgent uses a topology as a
graph to represent the processing transformation of streams of data which are abstracted
to a Tstream class. A connector is used to get streams of data from external entities
such as sensors and devices in physical world, or to send streams of data to back-end
systems like a cloud. The primary API of Edgent is responsible for data analysis. The
streams of data can be filtered, split, transformed or processed by other operations in a
topology. Edgent use a provider to act as a factory to create and execute topologies. To
build an Edgent applications, user should firstly get a provider, then create a topology
and add the processing flow to deal with the streams of data, and finally submit the
topology. The deployment environments of Edgent are Java 8, Java 7 and Android.
Edgent provides API for sending data to back-end systems and now supports
MQTT, IBM Watson IoT Platform, Apache Kafka and custom message hubs. Edgent
applications analyze the data from sensors and devices, and send the essential data to
the back-end system for further analysis. For IoT use cases, Edgent helps to reduce the
cost of transmitting data and provide local feedback.
Edgent is suitable for use cases in IoT such as intelligent transportation, automated
factories and so on. In addition, the data in Edgent applications are not limited to sensor

Fig. 4. Software architecture of CORD
164 J. Liang et al.
readings, they can also be files or logs. Therefore, Edgent can be applied to other use
cases. For example, it can perform local data analysis when embedded in application
servers, where it can analyze error logs without impacting network traffic [6].
3.5 Akraino Edge Stack
Akraino Edge Stack, initiated by AT&T and now hosted by Linux Foundation, is a
project to develop a holistic solution for edge infrastructure so as to support highavailability edge cloud service [15]. An open source software stack, as the software part
of this solution, is developed for carrier to facilitate optimal networking and workload
orchestration for underlying infrastructure in order to meet the need of edge computing
such as low latency, high performance, higher availability, scalability and so on.
To provide a holistic solution, Akraino Edge Stack has a wide scope from infrastructure layer to application layer. Figure 6 [15] shows the scope with three layers. In the
application layer, Akraino Edge Stack wants to create an app/VNF ecosystem and calls
for edge applications. The second layer consists of middleware which supports applications in the top layer. In this layer, Akraino Edge Stack plans to develop Edge API and
framework for interoperability with 3rd party Edge projects such as EdgeX Foundry. At
the bottom layer, Akraino Edge Stack intends to develop an open source software stack
for the edge infrastructure in collaboration with upstream communities. It interfaces with
and maximize the use of existing open source projects such as Kubernetes, OpenStack
and so on. Akraino Edge Stack provides different edge use cases with blueprints, which
are declarative configurations of entire stack including hardware, software, point of
delivery, etc. [15] The application domains of these blueprints start from Telco industry,
will expend to more domains like Enterprise and industrial IoT. Now Akraino Edge
Stack has put forward several blueprints such as Micro-MEC and Edge Media
Sensors or devices
data
Edgent applicaƟon on edge devices
Connector Connector

Tstream
The Cloud
data
control
Topology
A
Topology B
…
Provider
Fig. 5. Model of the Edgent applications
A Comparative Research on Open Source Edge Computing Systems 165
Processing. Micro-MEC intends to develop a new service infrastructure for smart cities,
which enables developing services for smart city and has high data capacity for citizens.
Edge Media Processing intends to develop a network cloud to enable real-time media
processing and edge media AI analytics with low latency.
As an emerging project, Akraino Edge Stack moved to execution since August
2018, thus more researches need to be done with the development of this project.
4 Comparative Study
In this section, we summarize the features of the systems discussed above in Table 1.
Then compare these open source edge computing systems under different aspects as
shown in Table 2. These aspects includes: the main purpose of the systems, the
application area, target user, the virtualization technology, system characteristic and
limitation. We believe they are of importance on giving a better understanding of these
systems. Finally, we discuss some use scenarios to help to choose a suitable system for
researchers.
4.1 Main Purpose
The main purpose shows the target problem that a system tries to fix. It is a key factor
for us to choose a suitable system to run edge applications. Therefore, we choose main
purpose as the first aspect we use to compare.
As an interoperability framework, EdgeX Foundry aims to communicate with any
sensor or device in IoT. And this ability is necessary for edge applications with data
from various sensors and devices. Azure IoT Edge offers an efficient solution to move
Fig. 6. Akraino Edge Stack’s scope
166 J. Liang et al.
the existing applications from cloud to edge, and to develop edge applications in the
same way with the cloud applications. Apache Edgent helps to accelerate the development process of data analysis in IoT use cases. CORD aims to reconstruct current
edge network infrastructure to build datacenters so as to provide agile network services
for end-user customers. From the view of edge computing, CORD provides with multiaccess edge services. Akraino Edge Stack provides an open source software stack to
support high-availability edge clouds.
Table 1. Features of open edge systems

Table 2. Comparison of open edge system characteristics

A Comparative Research on Open Source Edge Computing Systems 167
4.2 Application Area
This subsection discusses about the suitable application area of these systems, considering that some systems are created to meet the need of specific application area.
EdgeX Foundry and Apache Edgent both focus on IoT edge, and EdgeX Foundry is
good at communication with various sensors and devices, while Edgent is good at data
analysis. They are suitable for intelligent manufacturing, intelligent transportation and
smart city where various sensors and devices generate data all the time. Azure IoT
Edge can be thought as the expansion of Azure Cloud. It have an extensive application
area but depends on the compute resources of edge devices. Besides, it is very convenient to deploy edge applications about artificial intelligence such as machine
learning and image recognition to Azure IoT Edge with the help of Azure services.
CORD and Akraino Edge Stack support edge cloud services, which has no restriction
on application area. If the edge devices of users don’t have sufficient computing
capability, these two systems are suitable for users to run resource-intensive and
interactive applications in connection with operator network.
4.3 Target User
Though these open source systems focus on edge computing, but their target user are
not the same. EdgeX Foundry, Azure IoT Edge and Apache Edgent have no restriction
on target users. Therefore, every developer can deploy them into local edge devices like
gateways, routers and hubs. Differently, CORD and Akraino Edge Stack are created for
network operators because they focus on edge infrastructure.
4.4 Virtualization Technology
Virtualization technologies are widely used nowadays [20]. Virtual machine technology can provide better management and higher utilization of resources, stability,
scalability and other advantages. Container technology can provide services with
isolation and agility but with negligible overhead, which can be used in edge devices
[21]. Using OpenStack and Docker as software components, CORD and Akraino Edge
Stack use both of these two technologies to support edge cloud.
Different edge devices may have different hardware and software environment. For
those edge systems which are deployed on edge devices, container is a good technology for services to keep independence in different environment. Therefore, EdgeX
Foundry and Azure IoT Edge choose to run as docker containers. As for Edgent,
Edgent applications run on JVM.
4.5 System Characteristic
System characteristics show the unique features of the system, which may help user to
develop, deploy or monitor their edge applications. It will save lots of workload and
time if making good use of these characteristics. EdgeX Foundry provides with a
common API to manage the devices, and this brings great convenience to deploying
and monitoring edge applications in large scale. Azure IoT Edge provides powerful
168 J. Liang et al.
Azure services to accelerate the development of edge applications. Apache Edgent
provides a series of functional APIs for data analytics, which lowers the difficulty and
reduces the time on developing edge analytic applications. CORD and Akraino Edge
Stack provide with multi-access edge services on edge cloud, we only need to keep
connection with operator network, then we can apply for these services without the
need to deploy an edge computing system on edge devices by ourselves.
4.6 Limitation
This subsection discusses the limitation of the latest version of them to deploy edge
applications. The latest version of EdgeX Foundry has not provided a programmable
interface in its architecture for developers to write their own application. Although
EdgeX allows us to add custom implementations, but it demands more workload and
time. As for Azure IoT Edge, though it is open-source and free, but Azure services are
chargeable as commercial software. For Apache Edgent, it is lightweight and it focuses
on only data analytics. As for CORD and Akraino Edge Stack, these two systems
demand stable network between data sources and the operators because the edge
applications are running on the edge of operator network rather than local devices.
4.7 Scenarios
Based on the differences of aspects discussed above, now we can discuss where and
when each system should be chosen from the view of edge application.
In the first scenario, suppose we want to run edge applications on local area
network, and use local enterprise system as back-end system with no need for thirdparty clouds. In this case, we can choose EdgeX Foundry or Apache Edgent. Further,
suppose we want a good device management ability with various and devices added in
this system, we should choose EdgeX Foundry because it provides APIs to manage and
control the devices. If we focus on data analysis, a best approach would be to use
Apache Edgent because it helps to accelerate the development of edge analytic
applications.
Then suppose we want to build edge applications about artificial intelligence. In
this case, Azure IoT Edge can reduce the difficulty of development by providing
powerful Azure services like Azure Machine Learning, as well as commercial support.
At last, suppose we want to run mobile edge applications on drones or autonomous
cars. In this case, we should choose edge cloud services with wireless access, so CORD
or Akraino Edge Stack are the best choice.
5 Conclusions
In this paper, we presented five open source edge computing systems, EdgeX Foundry,
Azure IoT Edge, CORD, Apache Edgent and Akraino Edge Stack. We also listed
features of them, gave a comparative study about them and described user scenarios.
We hope this paper can give the readers a better understanding of their characteristic
and help readers to choose an appropriate systems according to their requirement.
A Comparative Research on Open Source Edge Computing Systems 169
Acknowledgments. This work is supported by the National Key Research and Development
Program of China under Grant No. 2016YFB1000302 and the National Natural Science Foundation of China under Grant Nos. 61832020.
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