=Paper=
{{Paper
|id=Vol-1684/paper20
|storemode=property
|title=Digital Enterprise Architecture with Micro-granular Systems and Services
|pdfUrl=https://ceur-ws.org/Vol-1684/paper20.pdf
|volume=Vol-1684
|authors=Alfred Zimmermann,Justus Bogner,Rainer Schmidt,Dierk Jugel,Christian Schweda,Michael Möhring
|dblpUrl=https://dblp.org/rec/conf/bir/ZimmermannBSJSM16
}}
==Digital Enterprise Architecture with Micro-granular Systems and Services==
https://ceur-ws.org/Vol-1684/paper20.pdf
Digital Enterprise Architecture with
Micro-granular Systems and Services
Alfred Zimmermann 1 , Justus Bogner1,2 , Rainer Schmidt 3 ,
Dierk Jugel1,4 , Christian Schweda 1 , and Michael Möhring 3
1
Reutlingen University, Faculty of Informatics, Germany
2
Hewlett Packard Enterprise, Boeblingen, Germany
3
M unich University, Faculty of Computer Science and M athematics, Germany
4
University of Rostock, Faculty of Computer Science and Engineering, Germany
{alfred.zimmermann, dierk.jugel, christian.schweda}@reutlingen-university.de
justus.bogner@hpe.com, rainer.schmidt@hm.edu, michael.moehring@hm.edu
Abstract. The digitization of our society changes the way we live, work, learn,
communicate, and collaborate. This disruptive change interacts with all
information processes and systems that are important business enablers for the
context of digitization since years. Our aim is to support flexibility and agile
transformations for both business domains and related information technology
with more flexible enterprise information systems through adaptation and
evolution of digital enterprise architectures. The present research paper
investigates the continuous bottom-up integration of micro-granular
architectures for a huge amount of dynamically growing systems and services,
like M icroservices and the Internet of Things, as part of a new digital enterprise
architecture. To integrate micro-granular architecture models to living
architectural model versions we are extending more traditional enterprise
architecture reference models with state of art elements for agile architectural
engineering to support the digitization of products, services, and processes.
Keywords: M icroservices, Internet of Things, Digital Enterprise Architecture,
Architectural Integration, Adaptable Architecture
1 Introduction
Digitization is the collaboration of human beings and autonomous objects beyond
their local context using digital technologies. Digitization [16] further increases the
importance of information, data, and knowledge as fundamental concepts of our
everyday activities. By exchanging information human beings and intelligent objects
are able to make decisions in a broader context and with higher quality. Social
networks, smart portable devices, and intelligent cars represent only a few instances
of a pervasive, information-driven vision [14] for the next wave of digital economy
with digital products, services, and processes. Major trends for the digitization are
investigated by [19] itemizing the digitization of products and services, context-
sensitive value creation, consumerization of IT, digitization of work, and the
digitization of business models. Microservices and the Internet of Things are
emerging to support next intelligent systems. They will shape future trends of
business innovation and the next wave of information and communication technology.
Biological metaphors of living and adaptable eco systems [17] provide the logical
foundation for self-optimizing and resilient run-time environments.
The technological and business architectural impact of digitization has multiple
aspects, which directly affect adaptable digital enterprise architectures and their
supported systems. Smart companies are extending their capabilities to continuously
manage their changing business operating model by developing and maintaining
Enterprise Architectures as the architectural part of a changing IT Governance [20].
Enterprise Architecture Management [7] with Services Computing [22] is the
approach of choice to organize, build, utilize, and distribute capabilities for the digital
enterprise architectures [23]. They provide flexibility and agility in business and IT
systems. The development of such applications integrates Web and REST Services,
Microservices, Internet of Things, Cloud Computing and Big Data management,
among other frameworks and methods for semantic support.
Digitization of products and services requires the close alignment of business
models and digital technologies for creative digital strategies and solutions, as well as
for their digital transformation. Unfortunately, the current state of art and practice of
enterprise architecture lacks an integral understanding and support of integrating a
huge amount of micro-granular systems and services, like Microservices and Internet
of Things, and the process of architectural adaptation for enterprise transformation.
Our main motivation and the current presented work is to extend previous approaches
of quiet static enterprise architecture to fit for flexible and adaptive digitization of
new products and services and by introducing suitable mechanisms for collaborative
architectural engineering and integration of micro-granular architectures. We report
about our research to provide an adaptable digital enterprise architecture framework
by continuously integrating relevant micro-granular information resources and their
architectures for a fast growing number of digital products, services, and processes.
Our current paper is investigating the following research questions:
RQ1: What are architectural properties of Microservices and what are implication
for integrating them into a digital enterprise architecture?
RQ2: How can we architect the Internet of Things and what is the resulting
architectural composition context for the digitization of products and processes ?
RQ3: How should the digital enterprise architecture be holistically tailored to
integrate a huge amount of Microservices and Internet of Things architectures,
considering the hypotheses that these micro-granular structures can be integrated in t o
a consistent view of a digital enterprise architecture in a similar way ?
The following Section 2 sets the fundamental context for digital enterprise
architectures using Microservices. Section 3 focusses on architecting the Internet of
Things for supporting the digital transformation. Section 4 presents with digital
enterprise architecture our collaborative architectural reference and transformation
approach and links it with specific architectural integration mechanisms for micro-
granular systems and services . Finally, we summarize in Section 5 our research
findings, our ongoing work in academic and practical environments and our future
research plans.
2 Microservices Architecture
The term Microservices became popular in the last years and refers to a fine-grained
style of service-oriented architecture (SOA) applications combined with several
DevOps elements. James Lewis and Martin Fowler define a Microservice
Architecture [8] as an approach for developing a single application from a suite of
small services, each running in its own process and communicating with lightweight
mechanisms, like HTTP. Microservices may additionally access NoSQL databases
from on premise and optional Cloud environments.
These services are built around business capabilities and are independently
deployable by an automated deployment pipeline. Typically, there is a bare minimum
of centralized management of these services . Microservices may be written in
different programming languages and can use different data storage technologies. As
opposed to big monolithic applications, a single Microservice tries to represent a unit
of functionality that is as small and coherent as possible. This unit of functionality or
business capability is often referred to as a bounded context, a term that originates
from Domain-Driven Design (DDD) [2].
However, Microservices also come with the need for a strong DevOps culture
[1] to handle the increased distribution level and deployment frequenc y. Moreover,
while each single Microservice may be of reasonably low complexity compared to a
monolithic application, the overall complexity of the system has not been reduced at
all. Gary Olliffe [9] distinguishes between the inner architecture and the outer
architecture of Microservices (Fig. 1).
Fig. 1. M icroservices Inner and Outer Architecture, based on [9]
By splitting up a big monolith into more fine-grained independent services, you
shift most of the hindering complexity from the inner architecture to the outer
architecture, where inter-service communication, service discovery, or operational
capabilities have to be handled. The greatest benefits that come with Microservices
are the possibility to use the best-fitting technology for each bounded context. Typical
examples are: increased application resilience (if one Microservice fails, the others
may not be affected, at least if there is no chaining), independent and efficient
scalability instead of replicating the complete monolith, and faster and easier
deployment [1]. Especially the last advantage is an important step towards agility of
business and IT systems.
Enabling technological heterogeneity is usually considered an advantage of
Microservices [8] that allows the selection of the best tool for the job, reduces the
possibility of lock-ins for outdated technology, and supports a culture of innovation
and experimentation. However, Microservices also come with some risks for the
organization. An explosion of technological diversity can quickly become
overwhelming and unmanageable. Moreover, you are dependent on employees with
the corresponding skills to handle these technologies and programming languages.
This is why most organizations that use Microservice Architecture either provide
some very basic standardization without limiting their teams’ choices too much or
encourage the use of only a certain technology subset by offering comfortable tooling
and infrastructure support for selected languages. Both approaches work reasonably
well and prevent the existence of e.g. three different versions of Java or the use of six
different web servers. This difficulty of keeping a healthy amount of governance and
standardization while still allowing enough technological heterogeneity to not hinder
innovation and agility can be addressed by Enterprise Architecture Management.
However, classical approaches to enterprise architectures are often not flexible
enough for the kind of diversity and distribution present in a Microservice
Architecture.
3 Internet of Things Architecture
The Internet of Things (IoT) fundamentally revolutionizes today’s digital strategies
with disruptive business operating models [19], and holistic governance models [20]
for both business and IT. With the huge diversity of Internet of Things technologie s
and products organizations have to leverage and extend previous enterprise
architecture efforts to enable business value by integrating the Internet of Things into
existing business and computational environments. Reasons for strategic changes
resulting from the Internet of Things [4] are:
Information of everything – enables information about what customers really
demand,
Shift from the thing to the composition – the power of the IoT results from the
unique composition of things in an always -on,
Always-connected information-rich environments,
Convergence – integrates people, things, places, and information,
Next-level business – the Internet of Things is changing existing business
capabilities by providing a deeper way to interact, measure, operate and
analyze business and IT.
The Internet of Things enables a large number of physical devices to connect each
other to perform wireless data communication and interaction, by using the Internet as
a global communication environment. The Internet of Things is the result of a
convergence of visions [13]: Things-oriented vision, an Internet-oriented vision, and a
Semantic-oriented vision. A cloud centric vision for architectural thinking of a
ubiquitous sensing environment is provided by [4]. The typical configuration of the
Internet of Things includes besides many communicating devices a cloud-based
server architecture, which is required to interact and perform remote data
management and calculations. In this way Internet of Things directly includes
software and services into structures of digitized value chains.
Sensors, actuators, devices as well as humans and software agents interact and
communicate data to implement specific tasks or more sophisticated business or
technical processes [4], [14]. The Internet of Things maps and integrates real world
objects into the virtual world and extends the interaction with mobility systems,
collaboration support systems, and systems and services for big data and cloud
environments. Furthermore, the Internet of Things fundamentally influences the
Industry 4.0 [15] and adaptable digital enterprise architectures [23]. Therefore, smart
products as well as their production is supported by the Internet of Things and can
help enterprises to flexibly create customer-oriented products.
A main question is, how the Internet of Things architecture fits in a context of a
service-based enterprise computing environment? A service-oriented integration
approach for the Internet of Things is referenced in [23]. The core idea for millions of
cooperating devices is, how they can be flexibly connected to form useful advanced
collaborations within the business processes of an enterprise. The service-oriented
architecture abstracts the heterogeneity of embedded systems, their hardware devices,
software,The!Architecture
data formats and communication protocols. A layered architecture structures
the following bottom-up functionalities and prepares these layers for integration
within an Internet of Things focused enterprise architecture: Devices Layer, Platform
Abstraction Layer, Security Layer, Device Managemen t Layer with Monitoring
Internet ofServices,
Services, Inventory Things Service Lifecycle Management, Service Management
Layer, and the Application
Reference Interface Layer.
Architecture
Internet of Things
A layered Reference Architecture
Reference Architecture for the Internet of Things is proposed in [21] and
(Fig. 2). Layers can be instantiated by suitable technologies for the Internet of Things.
Web / Portal Dashboard API Management
Identity & Access Management
Event Processing and Analytics
Aggregation / Bus Layer
ESB and Message Broker
Device Manager
Communications
MQTT / HTTP
Devices
Fig. 2. Internet of Things Reference Architecture [21]
14 WSO2: A Reference Architecture for the Internet of Things. http://wso2.com 2014
13 WSO2: A Reference Architecture for the Internet of Things. http://wso2.com 2014
Figure!2!>!Reference!Architecture!for!IoT!
·
·
·
·
·
·
·
The Devices layer is the bottom layer, on which all other layers are built on.
Devices are of different types, like cell and smart phones, cars, machines, house
devices, and have to be connected directly or indirectly with the Internet. Each device
needs an ID, which may be an UUID (unique identifier) provided by a device-chip, an
UUID provided by the radio subsystem as a Bluetooth identifier, or a Wi-Fi MAC
address, or an OAuth2 Token.
The Communications layer provides the devices’ connectivity [13], [21], having to
support typically multiple protocols for communication, like HTTP/HTTPS also
supporting REST architectural styles, and lightweight protocols such as MQTT
[http://mqtt.org], a publish-subscribe messaging protocol based on a broker model,
and the Constrained Application Protocol (CoAP). CoAP enables IP and HTTP-based
communications in a constrained environment. Mobility requirements and solutions
for service-continuity in the Internet of Things in a mobile IPv6 environment are
elaborated in [13], [21]. MQTT enables communication in lossy and intermittently
connected networks on top of TCP. CoAP supports a RESTful application protocol
over UDP with reduced footprint and is directly binary coded. Using the HTTP
protocol for sending data to the device would caus e an inefficient HTTP polling.
Replacing it with the WebSocket protocol upgrades the HTTP connection into a full
two-way connection. Therefore, MQTT combined with WebSocket emerges as the
recommended efficient protocol for the Internet of Things.
The Aggregation / Bus Layer aggregates and combines commun ications from
different devices and routes communications as a gateway to specific devices.
Additionally, the aggregation / bus layer is responsible for bridging and
transformations between protocols and supports a HTTP server and a MQTT broker.
The Event Processing and Analytics Layer [21] are responsible for analyzing
events, which are taken from the bus and stored into a database. There are different
approaches to be used in the Event Processing and Analytics Layer: scalable column-
based data storage, map-reduce for long-running batch-oriented data processing,
complex event processing for fast in-memory processing, and traditional application
server processing.
The External Communication Layer [13], [21] enables communication outside of
devices by supporting processing models like: Web -based frontends and portals,
dashboards with analytics processing views, and system interaction outside the
network via APIs. The Device Management Layer contains the Device Manager
component and related device manager agents for different platform and device types.
The device manager is responsible for the installed software, enabling and disabling
features of devices, managing security controls and identifiers, monitoring the
availability of devices, and locking the device remotely.
A current holistic approach for the development of the Internet of Things
environments is presented in [13]. This research has a close link to our work about
leveraging the integration of the Internet of Things into a framework of digital
enterprise architectures. The main contribution from [13] considers a role-specific
development methodology and a development framework for the Internet of Things.
The development framework contains a set of modeling languages for a vocabulary
language to describe domain-specific features of an IoT-application, an architecture
language for describing application-specific functionality, and a deployment language
for deployment features.
4 Digital Enterprise Architecture
Enterprise Architecture Management (EAM) [7] defines today with frameworks,
standards [11], [12], tools and practical expertise a quite large set of different views
and perspectives. We argue in this paper that a new and refocused digital enterprise
architecture approach should support digitization of products and services and sho u ld
be both holistic [37] and [6] and easily adaptable [18], [3] to support the digital
transformation. We are evolving the first versions of ESARC–Enterprise Services
Architecture Reference Cube [22], [23] (Fig. 3).
Fig. 3. Enterprise Services Architecture Reference Cube [22], [23]
In this paper we extend our service-oriented enterprise architecture reference
model for the context of managed architectural cases with decision making [6], [24]
which are supported by interactive functions of an EA cockpit [5]. Additionally, we
have tailored our architectural metamodel integration approach [23] to support
architectures for digital transformations with Microservices and Internet of Things.
ESARC is more specific than existing architectural standards of EAM – Enterprise
Architecture Management [18] and [19] and extends these architectural standards for
digital enterprise architectures with services and cloud computing. ESARC provides a
holistic classification model with eight integral architectural domains. These
architectural domains cover specific architectural viewpoint descriptions in
accordance to orthogonal dimensions of both architectural layers and architectural
aspects [19]. ESARC abstracts from a concrete business scenario or technologies, but
it is applicable for concrete architectural instantiations to support digital
transformations. The Open Group Architecture Framework [18] provides the basic
blueprint and structure for our extended service-oriented enterprise architecture
domains of ESARC [22] having: Architecture Governance, Architecture
Management, Business and Information Architecture, Information Systems
Architecture, Technology Architecture, Operation Architecture, and Cloud Services
Architecture.
Our research extends a previous metamodel-based model extraction and integration
approach from [23] for digital enterprise architecture viewpoints, models, standards,
frameworks and tools to support the adaptable integration of micro-granular
architecture. Currently we are working on the idea of continuously integrating small
architectural descriptions (Fig. 4) for relevant objects of a digital enterprise
architecture. To continuously integrate a huge amount of dynamically growing
architectural descriptions from different microstructures with micro-granular
architecture into a consistent enterprise architecture is a considerable challenge. In
order to address this problem, we are currently formalizing small-decentralized mini-
Integralmodels,
metamodels, Digitaland
Enterprise Architecture
data of architectural microstructures, like Microservices and
IoT DEA-Mini-Models for each
into EA-Mini-Descriptions. (even
From the small)
case ofArchitectural
a web shop we Artifact
can extract the
following micro-granular structure examples with their local architecture models:
OrderService and BillingService.
M3 ArchiMate OWL Meta-Model
Integration Rules Ontology Model
M2 Architectural Ontology
Architectural Meta-Model
Architectural Model
M1
Meta-Data Rules Meta-Data
Run-Time-Data
M0 Run-Time Data
Fig. 4. Structure of EA-M ini-Descriptions
EA-Mini-Descriptions
17 consists of partial EA-Data, partial EA-Models, and partial
EA-Metamodels associated with Microservices and/or Internet of Things . These
structures are based on the Meta Object Facility (MOF) standard [10] of the Object
Management Group (OMG). The highest layer M3 represents the abstract language
concepts used in the lower M2 layer and is therefore the meta-metamodel layer. The
next layer M2 is the metamodel layer and defines the language entities for M1 (e.g.
constructs from UML, ArchiMate [12], or OWL [23]). Instantiations of these
languages then form the layer M1 that contains models in the specified language.
These models are a structured representation of the lowest layer M0 that is formed by
collected concrete data from real-world use cases.
For building our EA-Mini-Descriptions, we applied the four layers of MOF to
provide sufficient information structures for an EA integration scenario with
microstructures. M0 and M1 are local layers to a single microstructure (cell
metaphor). While M0 consists of operational run-time or monitoring data, M1
contains meta-data of the microstructure (e.g. purpose, API endpoints, or usage costs)
as well as its inner architectural model (e.g. components or communication channels).
On top of these, the layer M3 acts as a global meta-model layer that holds necessary
information for several collaborating microstructures (body metaphor, combining
several cells). It incorporates architectural meta-models and ontologies of micro-
granular systems and services while also providing the important integration rules for
the semi-automatic integration of specific metamodels to the overall integrated and
dynamically growing EA metamodel from the composition of EA -Mini-Descriptions.
On top of that, M3 specifies the languages and semantic representations that we are
using for modeling and representing adaptable enterprise architecture metamodels.
Adaptability in the context of EA and microstructures is mostly concerned with
heterogeneity, distribution, and volatility. Adaptation [3] is a key success factor for
the survival of digital enterprise architectures, platforms, and application
environments. Therefore, we have extracted the idea of digital ecosystems from [18]
and linked this with main strategic drivers for system development and system
evolution. Additionally, we have to consider internal factors : The alignment of
Architecture Governance [3] shapes resiliency, scalability, and reusability of
components and services for distributed information systems.
5 Conclusion
In this paper we identified the need for a bottom-up integration of a huge amount of
dynamically growing micro-granular systems and services, like Microservices and the
Internet of Things, as part of a new suited digital enterprise architecture. In order to
integrate micro-granular architecture models for a living and holistic digital enterprise
architecture model we are extending more traditional enterprise architecture reference
models with state of art elements for agile architectural engineering to support the
digitization of products, services, and processes.
According to our research questions we have leveraged a new enterprise
architecture approach to model a living digital enterprise architecture, which is well in
line with adaptive models and digital transformation mechanisms. We have
investigated new architectural properties of Microservices as a base for integrating
them into our digital enterprise reference architecture. We have extended in our work
the new architectural integration context from the Internet of Things architecture to
support Microservices as well and the digital transformation of products and services.
Finally, we have extended our previous quite static enterprise architecture reference
model to be able to integrate micro-granular systems and services, like Microservices
and Internet of Things. This is a fundamental extension of our previous work on the
ESARC reference model to be able to integrate through a continuously bottom-up
approach a huge amount of micro-granular systems with own and heterogeneous local
architectures.
We have to additionally to consider alternative approaches for the integration of a
large set of divergent systems considering an open world approach. Our approach has
some limitations of our original focus with manually working integration models for
existing architectural metamodels assuming a closed word of a classical enterprise.
We are currently working on extended decision support mechanisms for an
architectural cockpit for digital enterprise architectures and related engineering
processes. Future work will extend both mechanisms for adaptation and flexible
integration of digital enterprise architectures as well as decision al processes with
rationales and explanations.
References
1. Bass, L., Weber, I., Zhu, L.: DevOps: A Software Architect’s Perspective. Addison
Wesley (2015)
2. Evans, E.: Domain-driven Design: Tackling Complexity in the Heart of Software. Addison
Wesley (2004)
3. Farwick, M ., Pasquazzo, W., Breu, R., Schweda, C. M ., Voges, K., Hanschke, I.: A M eta-
M odel for Automated Enterprise Architecture M odel M aintenance. EDOC, 1-10 (2012)
4. Gubbi, J., Buyya, R., M arusic, S., Palaniswami, M .: Internet of Things (IoT): A vision,
architectural elements, and future directions. F. Gen. Comp. Syst. 29(7), 1645-1660 (2013)
5. Jugel, D., Schweda, C. M .: Interactive functions of a Cockpit for Enterp rise Architecture
Planning. EDOCW, Ulm, Germany, 2014, IEEE, 33-40 (2014)
6. Jugel, D., Schweda, C.M ., Zimmermann, A.: M odeling Decisions for Collaborative
Enterprise Architecture Engineering. In: CAISE / TEAR 2015 Springer, 351-362 (2015)
7. Lankhorst, M .: Enterprise Architecture at Work. Springer (2013)
8. Newman, S.: Building M icroservices: Designing Fine-Grained Systems. O’Reilly (2015)
9. Ollife, G.: M icroservices: Building Services with the Guts on Outside. (2015), online at
http://blogs.gartner.com/garryollife/2015/01/30/microservces-guts-on-the-outside/.
10. OM G, M eta Object Facility (M OF) Core Specification, Version 2.5 (2011)
11. Open Group: TOGAF Version 9.1. Van Haren Publishing (2011)
12. Open Group: ArchiM ate 2.0 Specification. Van Haren Publishing (2012)
13. Patel, P., Cassou, D.: Enabling High-level Application Development for the Internet of
Things. In CoRR abs/1501.05080, Journal of Systems and Software (2015)
14. Porter, M . E., Heppelmann, J. E.: How Smart Connected Products are Transforming
Competition. Harvard Business Review, November, 1-23, (2014)
15. Schmidt, R., M öhring, M ., Härting, R. C., Reichstein, C., Neumaier, P., & Jozinović, P.:
Industry 4.0-Potentials for Creating Smart Products: Empirical Research Results. In
Business Information Systems, Springer International Publishing, 16-27, (2015)
16. Schmidt, R., Zimmermann, A., M öhring, M ., Nurcan, S., Keller, B., & Bär, F.:
Digitization–Perspectives for Conceptualization. In Advances in Service-Oriented and
Cloud Computing (pp. 263-275). Springer International Publishing, (2015)
17. Tiwana, A.: Platform Ecosystems. M organ Kaufmann (2013)
18. Trojer, T. et. al.: Living M odeling of IT Architectures: Challenges and Solutions.
Software, Services, and Systems 2015, 458-474 (2015)
19. Westerman, G., Bonnet, D.: Revamping Your Business Through Digital Transformation.
M IT Sloan M anagement Review, February (2015)
20. Weill, P., Ross, J. W.: IT Governance: How Top Performers M anage It Decision Rights
for Superior Results. Harvard Business School Press (2004)
21. WSO2 White Paper: Reference Architecture for the Internet of Things. Version 0.8.0
http://wso2.com (2015)
22. Zimmermann, A., Gonen, B., Schmidt, R., El-Sheikh, E., Bagui, S., Wilde, N.: Adaptable
Enterprise Architectures for Software Evolution of SmartLife Ecosystems. EDOCW,
IEEE, 316-323 (2014)
23. Zimmermann, A., Schmidt, R., Sandkuhl, K., Wißotzki, M ., Jugel, D., M öhring, M .:
Digital Enterprise Architecture – Transformation for the Internet of Things. EDOC 2015
SoEA4EE, 21-25 September 2015, Adelaide, Australia, IEEE, 130-138 (2015)
24. Zimmermann, A., Jugel, D., Sandkuhl, K., Schmidt, R., Schweda, C., M öhring, M .:
Architectural Decision M anagement for Digital Transformation of Products and Services.
Complex Systems Informatics and M odeling Quarterly, CSIM Q 2016, No. 6, 31-53 (2016)