=Paper=
{{Paper
|id=Vol-1848/CAiSE2017_Forum_Paper17
|storemode=property
|title=Information Logistics and Fog Computing: The DITAS* Approach
|pdfUrl=https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper17.pdf
|volume=Vol-1848
|authors=Pierluigi Plebani,David Garcia-Perez,Maya Anderson,David Bermbach,Cinzia Cappiello,Ronen I. Kat,Frank Pallas,Barbara Pernici,Stefan Tai,Monica Vitali
|dblpUrl=https://dblp.org/rec/conf/caise/PlebaniGABCKPPT17
}}
==Information Logistics and Fog Computing: The DITAS* Approach==
https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper17.pdf
Information Logistics and Fog Computing:
The DITAS∗ Approach
Pierluigi Plebani1 , David Garcia-Perez2 , Maya Anderson4 , David Bermbach3 ,
Cinzia Cappiello1 , Ronen I. Kat4 , Frank Pallas3 , Barbara Pernici1 , Stefan Tai3 ,
and Monica Vitali1
1
Politecnico di Milano – Dipartimento di Elettronica, Informazione e Bioingegneria
Piazza Leonardo da Vinci, 32 - 20133 Milan, Italy
[firstname].[lastname]@polimi.it
2
Atos Spain SA
Pere IV, 08018 Barcelona, Spain
david.garciaperez@atos.net
3
TU Berlin – Information Systems Engineering Research Group
Einsteinufer 17 - 10587 Berlin, Germany
{db,fp,st}@ise.tu-berlin.de
4
IBM Research – Haifa
Haifa University Campus, Mount Carmel, Haifa, 3498825, Israel
{ronenkat,mayaa}@il.ibm.com
Abstract. Data-intensive applications are usually developed based on
Cloud resources whose service delivery model helps towards building reli-
able and scalable solutions. However, especially in the context of Internet
of Things-based applications, Cloud Computing comes with some limi-
tations as data, generated at the edge of the network, are processed at
the core of the network producing security, privacy, and latency issues.
On the other side, Fog Computing is emerging as an extension of Cloud
Computing, where resources located at the edge of the network are used
in combination with cloud services.
The goal of this paper is to present the approach adopted in the recently
started DITAS project: the design of a Cloud platform is proposed to op-
timize the development of data-intensive applications providing informa-
tion logistics tools that are able to deliver information and computation
resources at the right time, right place, with the right quality. Applica-
tions that will be developed with DITAS tools live in a Fog Computing
environment, where data move from the cloud to the edge and vice versa
to provide secure, reliable, and scalable solutions with excellent perfor-
mance.
Keywords: Fog Computing, Edge Computing, Data movement, Cloud
Computing
∗
http://www.ditas-project.eu/
X. Franch, J. Ralyté, R. Matulevičius, C. Salinesi, and R. Wieringa (Eds.):
CAiSE 2017 Forum and Doctoral Consortium Papers, pp. 129-136, 2017.
Copyright 2017 for this paper by its authors. Copying permitted for private and academic purposes.
1 Introduction
Fog Computing [9], often also referred to as Edge Computing [12], is an emerg-
ing paradigm aiming to extend Cloud Computing capabilities to fully exploit
the potential of the edge of the network where traditional devices as well as new
generations of smart devices, wearables, and mobile devices – the so-called Inter-
net of Things (IoT) – are considered. Especially for data-intensive applications,
since IoT is a source for enormous amounts of data that can be exploited to
provide support in a multitude of different domains (e.g., predictive machinery
maintenance, patient monitoring), this new paradigm has opened new frontiers.
Nowadays, the typical approach for data processing relies on cloud-based
applications: data are collected and pre-processed on the edge, then they are
moved to the cloud, where a more scalable and reliable processing environment
is provided. Finally, the output of the analysis is sent to the end user, whose
devices are again on the edge. While the cloud offers virtually unlimited compu-
tational resources making data processing extremely efficient, the resulting data
movement could have an impact on the application performance due to possibly
significant latencies of the transmission. To improve this type of applications,
proper information logistics become fundamental for delivering information at
the right time, the right place, and with the right quality [10]. However, develop-
ing applications that are able to deal with these issues can be really challenging
for several reasons: heterogeneity of devices at the edge, privacy and security
issues when moving data from the edge to the cloud, or limited bandwidth.
The goal of this paper is to introduce the recently started DITAS project
which aims to improve, through a cloud platform, the development of data-
intensive application by enabling information logistics in Fog environments where
both resources belonging to the cloud and the edge are combined. The resulting
data movement is enabled by Virtual Data Containers which provide an ab-
straction layer hiding the underlying complexity of an infrastructure made of
heterogeneous devices. Applications developed using the DITAS toolkit will be
able to exploit the advantages of both cloud-based solutions about reliability
and scalability, and edge-based solutions with respect to latency and privacy.
To properly discuss the relevant aspects of information logistics, Fog comput-
ing as well as challenges that need to be faced, the rest of the paper is organized
as follow: Section 2 proposes the DITAS vision on Cloud, Fog, and Edge com-
puting. Details on the importance of information logistics in general, as well as
in Fog environments in particular, are discussed in Section 3. Section 4 gives an
overview of the DITAS approach with emphasis on the architectural description
of the proposed cloud platform.
2 On the Fog, Edge, and Cloud Computing
Cloud computing [7] has been widely adopted as a model for developing and
providing scalable and reliable applications in a cost-efficient way with minimal
infrastructure management efforts for developers. In such an environment, com-
putational resources as well as storage capacity can be considered unlimited,
130
which has boosted the proliferation of applications able to manage huge amount
of data. Nevertheless, relying only on Cloud computing or even on federated
clouds [6] could have some drawbacks especially when the data to be processed
are generated by devices located at the edge of the network. If we consider
that predictions for IoT estimate about 32 billion connected devices in 2020
that will be producing about 440 Petabytes per year (accounting for 10% of the
world data) [13], the impacts of data movement between edge-located resources
– where data are produced – and the cloud-located resources – where data are
processed and stored – can be significant. In the telecommunication sector, when
Cloud resources
Edge resources
Fog Computing
Fig. 1. Fog Computing environment
mobile devices moved from being simple consumers of contents to being produc-
ers of contents, the term Fog Computing has been coined to identify a platform
able “to provide compute, storage, and networking services between Cloud data
centers and devices at the edge of the network” [3]. The term Edge Computing
has been instead proposed by information systems engineering researchers when
IoT was recognized as a great opportunity for providing new services. It iden-
tifies the “technologies allowing the computation to be performed at the edge of
the network, on downstream data on behalf of cloud services and upstream data
on behalf of IoT services” [11].
Based on these definitions, Cloud Computing is mainly related to the core of
the network whereas Edge Computing is focused on providing to the owner of
resources the local ’in-situ’ means for collecting and preprocessing data before
sending it to cloud resources (for further utilization), thus addressing typical
constraints of sensor-to-cloud scenarios like limited bandwidth and strict latency
requirements. For this reason, also in the light of the definition proposed by the
OpenFog Consortium [9], we here after consider Fog Computing as the sum
of Cloud and Edge Computing. As shown in Figure 1, cloud resources include
physical and virtual machines which are capable of processing and storing data.
On the other side, smart devices, wearables, or smartphones belong to the set of
edge-located sources. While Cloud Computing is devoted to efficiently managing
capabilities and data in the cloud, Edge Computing is focused on providing the
131
means for collecting data from the environment (which will then be processed by
cloud resources) to the owner of the available resources. On this basis, Cloud and
Edge Computing are usually seen as two distinct and independent environments
that, based on the specific needs, are connected to each other to move data
usually from the edge to the cloud.
Exploiting the Fog Computing paradigm, in DITAS these two environments
seamlessly interoperate to provide a platform where both computation and data
can be exchanged in both downstream and upstream direction. For instance,
when data cannot be moved from the edge to the cloud, e.g., due to privacy
issues, then the computation is moved to the edge. Similarly, when there are
insufficient resources at the edge data are moved to the cloud for further process-
ing. With DITAS, we want to provide tools, specifically designed for developers
of data-intensive applications, which are able to autonomously decide where to
move data and computation resources based on information about the type of
the data, the characteristics of applications as well as the available resources
at both cloud and edge locations and applicable constraints such as EU GDPR
privacay regulation 5 .
3 Information Logistics
Since the 1990s, when interconnection of heterogeneous information systems
managed by different owners became easier and when the Web started managing
significant amounts of information, the problem of delivering such information
has become more and more relevant: the more the data are distributed, the more
difficult is it to find the information needed. Thus, tools are required to guide
the users in this task. In this scenario, Information Logistics6 has emerged as
a research field for optimizing the information provision and information flow
especially in networked organizations [10].
As discussed in [8], Information Logistics can be studied from different per-
spectives: e.g., how to exploit the data collected and managed inside an organi-
zation for changing the strategy of such organizations, how to deliver the right
information to decision makers in a process, or how to support supply chain
management. In our case, according to the classification proposed in [8], we are
interested in user-oriented Information Logistics: i.e., the delivery of informa-
tion at the right time, the right place, and with the right quality and format
to the user [4]. As a consequence, user requirements can be defined in terms
of functional aspects, i.e., content, and non-functional ones, i.e., time, location,
representation, and quality.
Especially in the recent years, where data deluge has been ever increasing,
providing solutions that are able to satisfy these types of requests becomes more
5
http://http://www.eugdpr.org/
6
Actually, according to [5] the Information Logistics term appeared the very first
time in Wormley P. W., Information logistics: Local distribution (delivery) of infor-
mation, Journalism Quarterly, Vol. 55 Issue 3, 1/9p, 1978, but a real interest on this
field started only more recently.
132
and more challenging. Different sources available on the Web could provide data
of interest to the user and selecting the best source depends on non-functional
requirements. Data quality dimensions, such as timeliness or accuracy [1], can
be used to measure whether data are useful or not. Location of data sources and
the performance of the system offering these data can influence data quality:
e.g., data stored in the cloud will take more time to be obtained than data on
the premises of the user. Privacy aspects are also fundamental as some data
must not leave the boundaries of the organization which holds these data, be
used for specific purposes as defined by GDPR regulations, or may only do so in
preprocessed (pseudonymized, aggregated, etc.) form. Finally, dealing with edge-
located resources significantly increases the complexity of the resulting system for
many reasons. These resources are often very heterogeneous in terms of software
platforms, storage capabilities, computational resources, and data formats. The
network connection may vary in bandwidth and, due to their nomadic nature,
there is a high rate of churn among edge-located resources.
When developing data-intensive applications in a Fog Computing environ-
ment, information logistics holds a central role and DITAS wants to simplify the
work of developers providing tools that are able to enact the proper data and
computation movements so as to satisfy user requirements. In DITAS, the pro-
cessing tasks composing the data-intensive application specify not only the con-
tent of required data, but also the data utility which subsumes all non-functional
requirements that may vary with respect to the status and the context of the
application. In contrast to the typical approaches where data move to the pro-
cessing modules, in DITAS, computational tasks may also move to where the
data are stored whenever it is neither feasible, e.g., due to privacy issues, nor
acceptable, e.g., due to high latency, to move the data. Thus, DITAS is studying
data and computation movement strategies to decide where, when, and how to
persist data – on the cloud or on the edge of the network – and where, when, and
how to compute part of the tasks composing the application to create a combi-
nation of edge and cloud that offers a good balance between reliability, security,
sustainability, and cost. To ascertain whether such DITAS-based applications
actually meet their quality goals monitoring, but also experiment-driven cloud
service benchmarking [2] will be used.
4 DITAS approach
The objectives of DITAS discussed above will be offered through a cloud plat-
form where developers can design their data-intensive applications where tasks
are linked to Virtual Data Containers that satisfy the data and application re-
quirements by enacting the proper information logistics.
4.1 Virtual Data Container
The concept of a Virtual Data Container (VDC) represents one of the key ele-
ments in the DITAS proposal. Generally speaking a VDC embeds the logic to
133
Task 1 Task 2 Task 3
VDC 1 VDC 2
Cloud resources Edge resources
Fig. 2. DITAS Virtual Data Container
enable a proper information logistics depending on both the application to be
executed and the available data sources. On the one hand, a VDC is linked to
one or more tasks composing the data-intensive applications. Along with these
links the developers specify the needs in terms of data, including both functional
(i.e., the content) and non-functional aspects (i.e., data quality). On the other
hand, a VDC is connected to a set of data sources offering a given information.
Thus, the goal of a VDC is to provide a single virtual layer, which can
be built upon the principles of Service Oriented Computing, which abstracts
the connected data sources. This virtual layer hides from the developer the in-
tricacies of the underlying complex infrastructure composed by smart devices,
sensors, as well as traditional computing nodes located in the cloud. The VDC
also embeds the capabilities not only for moving the data from the data sources
to the tasks operating on them, but also between the data sources to make the
data provisioning effective taking into account data access policies and regula-
tions. Finally, the definition of data movement techniques implies the definition
of data transmission (e.g., data stream, bulk) and data transformation (e.g., en-
cryption, compression) mechanisms, provided by a VDC, to define which will be
the destination of the data, which is the data format to be adopted during the
transmission, and how data will be stored in the destination.
Once the application is running, the application interacts only with the VDC
as designed, which, in turn, enact all the data movement strategies needed to
satisfy the posed requirements.
4.2 DITAS platform
The DITAS platform is a cloud-based solution which offers to the data-intensive
application developers a set of tools for improving the design and the execution
of the applications exploiting the functionalities provided by VDCs.
As shown in Figure 3 the functionalities offered by the DITAS cloud plat-
form are composed of two main blocks: (i) an SDK in charge of supporting the
developers in design and deployment of the applications, and (ii) a distributed ex-
ecution environment responsible for running and controlling the behavior of the
Fig. 3. DITAS Cloud Platform Architecture
application. About the SDK, extension of popular tools (e.g. Node-RED), will
be proposed to define applications in which data processing is a central element.
The key element of this tool is to allow the developer to design the applications
by specifying the VDC and constraints/preferences about the resources to be
exploited. For this reason, a communication with the cloud platform infrastruc-
ture is required to have a complete picture about the available resources both
on the cloud and the edge. Based on the developer’s instructions, and leveraging
on the degree of freedom given by the VDC while satisfying all the constraints,
the application is deployed among the selected resources.
The deployment of a DITAS-enabled data-intensive application implies that
the resources selected to be involved in the execution embed a set of modules
supporting the data and computation movement. For this reason, the execution
environment is based on a distributed architecture where the execution engine
is in charge of executing the tasks assigned to the resources on which the engine
is running and of maintaining coordination with the other resources involved
in the same application. Moreover, a monitoring system is able to check the
status of the execution, track data movement, and collect all data necessary for
understanding the behaviour of the application. In case of deviation with respect
to the expected behavior, data and/or computation movements can be enacted
to move the application to an acceptable state. Finally, data collected by all
involved monitoring systems can be further analysed to provide advice to the
developers for improving the design of their application.
135
5 Conclusions
This paper has presented the vision of DITAS, a recently started EU project,
which aims to simplify the development of data-intensive applications where data
and computation can be moved among resources belonging to both the core and
the edge of the network, i.e., both federated clouds and edge environments.
As the project is in its infancy, future work is mainly devoted to implementing
all the concepts expressed in this paper. Two case studies, one about an Industry
4.0 scenario, and another about an e-Health scenario will be used for testing and
validating the proposed approach.
Acknowledgments
DITAS project receives funding from the European Union’s Horizon 2020 re-
search and innovation programme under grant agreement RIA 731945.
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