2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW)

Toward Evaluation of Deployment Architecture of
ML-based Cyber-Physical Systems
Lucas Gisselaire, Florian Cario, Quentin Guerre-berthelot,
Bastien Zigmann, Lydie du Bousquet

Masahide Nakamura

University of Grenoble Alpes
CNRS, Grenoble INP, LIG 38000 Grenoble, France
lucas.gisselaire@etu.univ-grenoble-alpes.fr
lydie.du-bousquet@univ-grenoble-alpes.fr

Kobe University, Riken AIP
1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
1-4-1 Nihon-bashi, Chuo-ku, Tokyo 103-0027, Japanmasa-n@cs.kobe-u.ac.jp

moving computation closer to the physical space could reduce
latency and overhead, which is especially important for iCPS
with realtime requirements (such as self-driving cars) [4].
Thus, there is no obvious way for a software architect todetermine the best deployment conﬁguration of iCPS, even
if all the requirements and constraints are known.
Our long-term goal is to establish a method that allows
the software architect of iCPS to choose an appropriate deployment architecture, based on requirements on quality, costand security. As the ﬁrst step towards the goal, this paper
especially focuses on assessing the costs and security of iCPS
posed by the deployment conﬁguration. We ﬁrst present an
abstract model of iCPS to capture the possible deployment
conﬁgurations. We then propose a rule-based method thatassesses cost-related attributes for the conﬁgurations. Finally,
we investigate security threats within different conﬁgurations,to help the architect to choose appropriate conﬁguration basedon given security requirements.

Abstract—In this position paper, we explore a way to assess
deployment architectures for intelligent Cyber-Physical Systems(iCPS). Our long-term goal is to establish a method that allows asoftware architect of iCPS to choose an appropriate deploymentarchitecture, based on requirements on quality, cost and security.
As the ﬁrst step towards the goal, this paper ﬁrst presents
an abstract model of iCPS to capture the possible deployment
conﬁgurations. We then propose a method that assesses costrelated attributes for the conﬁgurations. Finally, we investigate
security threats within different conﬁgurations, to help theengineer to achieve the conﬁguration choice.
Index Terms—Assessment Model, Software Engineering, Software Architecture, Cyber-Physical Systems, Machine Learning

I. I NTRODUCTION
Nowadays, more and more software systems and solutions
are making use of Machine Learning (ML) to make complex
decisions towards the end-goal of undertaking some actions.
Cyber-Physical Systems (CPS) (integration of computation,
networking and physical processes) are impacted by this trend.
Indeed, thanking to emerging Internet of Things (IoT), a hugeamount of data can be collected from the physical world, andfed to ML algorithms to implement intelligent CPS (we call
iCPS). The trend of iCPS is spread to various areas, including
health, energy, and transportation [1]–[3].
To implement an iCPS application with a (supervised) ML
algorithm, it is necessary to train a prediction model. This
training usually requires a large amount of memory and highperformance resources, as well as complex libraries. Therefore, a reasonable solution to perform the model development
is to use a ML platform on a cloud (such as Microsoft Azure,
Google AI platform, Amazon AWS platform). The trained
prediction model is then deployed as a Web service within acloud. On the other hand, the software component that uses theprediction model is deployed on the edge side. The component
takes input data from a physical space, and makes intelligent
decisions based on the prediction derived by the model.
However, such a deployment conﬁguration is not always
best, due to a variety of requirements of individual iCPS, suchas security, latency, and cost. Data might contain personal andconﬁdential information, making cloud computing sensitive
without special pre-treatment. After the model development,

978-1-7281-4136-7/19/$31.00 ©2019 IEEE
DOI 10.1109/ASEW.2019.00036

II. D EPLOYMENT A RCHITECTURE OF ML-BASED CPS
A. Intelligent Cyber-Physical System (iCPS)
Cyber-physical systems (CPS) are systems that are built
from the seamless integration of computation and physicalcomponents. Emerging cloud computing and IoT technologies
allow a wide range of system conﬁguration to meet individual
requirements and use cases [3]. Figure 1 shows a typical architecture of CPS, in which the cloud computing is exploited.
In the ﬁgure, devices in a physical world collect data, andsend the data to an edge component (i.e., local server). Theedge component can communicate with cloud servers via theInternet, in order to use any computational resource as aservice. Using the data and the resources on the cloud, theedge commands actions for some devices [5].
Integrating machine-learning (ML) technologies with CPS
may implement more intelligent and challenging applications,where decisions and behaviors cannot be speciﬁed by explicit
rules. We call such applications intelligent CPS (iCPS).
Figure 2 depicts a data ﬂow diagram (DFD), showing
how an iCPS application typically works. The ﬁrst step toimplement iCPS is to build a Predictive Model (PM) fromTraining Data-Sets (DTS). This Model Learning (ML) process90CloudServerTraining Data
Storage (DTS)

Serverdata

Model
Learning
(ML)

model

Predictive Model(PM)

Serverquery

dataInternet

LegendresultDecision
Taking(DT)

actiondata storage
ProcessEdgedata flow

actiondata

DeviceDeviceDeviceDeviceDeviceFig. 2. Abstract data ﬂow diagram of iCPS applications

Devicecomputation resources. Hence, using the cloud for machinelearning or deploying applications/services on the cloud produces the cost merit. The maintenance is carried out by theplatform provider. In the cloud-based machine-learning platform, various kinds of algorithms and processes are ready touse, and they are facilitated by application program interfaces(API). To reproduce such an environment locally on an edge
server, the time and expertise are required as well as hugememory and high performance computers.
2) Security: The security of iCPS takes different dimensions among the deployment conﬁgurations. It depends onwhether or not the conﬁguration allows appropriate protection
against common threats and attacks to the processes, as wellas the conﬁdentiality and authenticity of the data.
When an iCPS application is connected to the Internet, thesecurity becomes a crucial factor. The tight interaction between
the software and the physical components in CPS enables
cyber-attacks to have catastrophic physical consequences [6].
For instance, over a half million pacemakers have been recalled by the American Food and Drug Administration, due
to fears that hackers could exploit cyber-security ﬂaws tomodify the patient’s heartbeat [7]. In case of ML algorithms,
the poisoning attack for the training data can modify theprediction model [8]. Theoretically, if the data doesn’t traverse
the network, the security is supposed to be increased. The lessdata is in the network, the less the data is in a breach or leak.
Just considering these factors, it is quite confusing to choose
an appropriate conﬁguration. In the next section, we try to
propose a method that can assess the cost and the security,
for the sixteen deployment conﬁgurations.

Fig. 1. Architecure of CPS with cloud and edge systems

is supposed to be performed once (or periodically with a low
frequency) based on manual experiments.
Once a predictive model is built, it is deployed on the application to be used for intelligent decision making, as shownin the Decision Taking (DT) process. The process collects thedata from a device, and requests the predictive model for a
prediction based on the data. Based on the prediction returned,
the application takes a decision to command an action to a
device. This process is executed whenever there is a request,
with a frequency from few millisecond to several hours/days,
depending on the application.
B. Considering Deployment Conﬁgurations of iCPS
In order to simplify the problem, let us suppose that any
iCPS can be abstracted by the four components (DTS, ML,
PM, and DT) and physical devices, as shown in Figure 2. Then,each of the four components can theoretically be deployedeither on the edge or on the cloud, considering the architectureof CPS (see Figure 1).
Since there exist two different deployments for each of thefour, there are thus 16(= 24 ) possible deployment conﬁgurations, as illustrated in Figure 3. In each conﬁguration, twoboxes are depicted where the upper box represents a cloud, andthe lower box represents an edge. For example, conﬁguration(4) represents that TDS and ML are deployed on the cloud,
whereas PM and DT are deployed on the edge. An arrow
between components represents a data ﬂow, as deﬁned in the
DFD in Figure 2.

III. T OWARDS CONFIGURATION EVALUATION
Our long-term goal is to establish a method that allows
software architects of iCPS to quantitatively evaluate thedeployment conﬁguration for a given set of requirements
and constraints. Towards the goal, this paper presents brief
assessment methods with respect to the cost and security.

C. The Problem: How to Choose the Best Conﬁguration?
A major challenge of designing iCPS lies in the fact that it is
not obvious for software architects which deployment conﬁguration best suits a given set of requirements and constraints.
The best conﬁguration should be determined based on various
attributes of the target application. However, in this paper, wenarrow our focus on the cost and the security only.
1) Cost: The cost of machines, computation, networking,operation, and maintenance in both edge (local) and cloud(remote) infrastructures is an important factor to take into
account. Cloud computing can provide on-demand and elastic

A. Assessing the Cost
Every iCPS application requires to create a predictive modelby machine learning, before the application is in operation.
The resources and the time needed for the model learning
phase are quite different from those in the operation phase.
In addition, the predictive model should be updated during

91

TDS

TDS
ML

TDS
ML

ML
PM

TDS

ML

DT

ML
DT

TDS

PM

PM

TDS

DT

PM

TDS

DT

PM

DT

PM

ML
ML

DT

Edge

PM

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

all on edge

only dataset
on cloud

only learningon cloudtrain. on cloud,infer. on edge

only modelon cloudzigzag fromcloud (weird)

train. on cloudwith data on edge

only decision
on edge

TDS

TDS
ML

DT

DT

TDS

ML

ML

PM

PM

TDS

DT

DT

TDS

TDS

PM

PM

ML

DT

Cloud

TDS
DT

DT

ML
PM

PM

ML

DT

DT

TDS

ML

PM

ML

DT

TDS
Edge

ML
PM

Cloud

PM

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

only decision
on cloudtrain. on edge
with data on cloudzigzag fromedge (weird)

only model
on edgetrain. on edge,
infer. on cloud

only learningon edgeonly dataset
on edgeall on cloud

TDS: Training Data Storage; ML: Model Learning componet; PM: Predictive Model location; DT: Decision Taking component

Fig. 3. The 16 possible deployment conﬁgurations for iCPS

the maintenance phase. For these reasons, we decided to
distinguish the cost of the three phases.
The cost at the edge side and the cost at the cloud side areconsidered separately. The cost at one side is highly dependent
on the components that are deployed. In the learning phase, thelocations of TDS, ML, and PM are considered. In the operation
phase, the locations of PM and DT are considered. In themaintenance phase, all the four components are considered. To
compare different conﬁgurations by cost, we try to rate every
conﬁguration, depending on where each component is present.
As an example, we chose to rate 1.5 when TDS is present on aside; 2.5 for ML; 0.5 for PM; 0.5 for DT. These values werechosen because; a huge capacity of memory is required forstoring the training data; a large memory, high performancecomputing, and many libraries are required for the machinelearning task; the predictive model is usually smaller than
the training data set; the size of decision-taking component
depends on the application but usually requires considerably
smaller resources than the machine learning task.
Note that TDS should be on the same side as ML to executethe learning phase. Therefore, if TDS exists at another side ofML, it has to be moved, and the resources for TDS should
be counted for both side. Similarly, PM is generated at thesame side as ML. Therefore, if the conﬁguration deploys PM
at another side, then PM has to be moved and the resources

should be counted for both sides.
Table I presents the values obtained with the proposed
method. For example, Conﬁguration 4 requires a small coston the edge during the learning phase. In the operation phase,
however, the cost is maximized on the edge, and is zero on thecloud. In Conﬁguration 8, the operation costs are balanced on
both sides. The actual cost will be derived by multiplying eachvalue by the unit price of the platform, and the time spent foreach phase, which depend on individual iCPS applications.
B. Assessing the Security
The security threats to iCPS are mainly related to two activities: (1) preventing the system from working correctly, or (2)stealing the primary data. In the ﬁrst case, an attacker inserts
incorrect data or modiﬁes correct data in DTS to produce awrong PM. They are called poisoning attack or evasion attack[9]. Also, communications between PM and DT can be alteredor subject to denial-of-service (DoS) attacks. Moreover, DT
can be a source of attacks (e.g., service manipulation). Data
can be stolen at any levels. For instance, the literature [8],indicates that it is possible to infer a part of original model ordataset on the basis of input/output pairs.
Of course, the deployment conﬁguration inﬂuences the
security issues [10], [11]. If PM and DT are deployed on
different side, a DoS attack to PM increases the reponse time92

TABLE I
A SSESSING THE COST IN DIFFERENT CONFIGURATIONS

Conﬁguration
Learning cost (edge)
Learning cost (cloud)Operation cost (edge)
Operation cost (cloud)Maintenance cost (edge)
Maintenance cost (cloud)

1
4.5
0
1
0
5
0

2
4.5
1.5
1
0
5
1.5

3
2
4.5
1
0
2.5
4.5

4
0.5
4.5
1
0
1
4.5

5
4.5
0.5
0.5
0.5
5
0.5

6
4.5
2
0.5
0.5
5
2

7
1.5
4.5
0.5
0.5
2
4.5

8
0
4.5
0.5
0.5
0.5
4.5

9
4.5
0
0.5
0.5
4.5
0.5

10
4.5
1.5
0.5
0.5
4.5
2

11
2
4.5
0.5
0.5
2
5

12
0.5
4.5
0.5
0.5
0.5
5

13
4.5
0.5
0
1
4.5
1

14
4.5
2
0
1
4.5
2.5

15
1.5
4.5
0
1
1.5
5

16
0
4.5
0
1
0
5

Acknowledgment: This research was supported by JSPS
KAKENHI, Grant Numbers JP19H01138, JP17H00731,
JP18H03242, JP18H03342, JP19H04154. It was also supported by Région Rhône-Alpes in France: Kouno-Tori projectof the SCUSI’18 program (18006667 01-41024).

of the prediction, and thus declines the performance. If DT
is on the cloud, an attacker may attempt to re-size the cloud
resources, to increase the operation cost [11].
Also, the edge side is not free of threats [12]. Privacy leakage, service manipulation, injection of information, and evenphysical damage can be achieved on the edge side, facilitatedby the usage of vulnerable equipment or inappropriate systemadministration. We are currently in the process to derive rules
to assess the security threats for the deployment conﬁguration.

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C. Limitations
We assumed the simpliﬁed architecture and model for iCPS
(see Figures 1 and 2). We did not count the fog computing,nor the case where DT is spread on the cloud and edge.
IV. C ONCLUSION AND F UTURE P ERSPECTIVESIn the context of intelligent CPS with machine-learningtechnologies (iCPS), data and software components can be
deployed either on the cloud or on the edge. Althoughsuch deployment conﬁguration inﬂuences the quality, cost,and security of the target application, it is not obvious forsoftware architects how to determine the best conﬁguration
for a given set of requirements and constraints. In this paper,
we investigated a method that brieﬂy assesses the deployment
conﬁguration of iCPS with respect to the cost and the security.
Introducing the architecture and data ﬂow models, we ﬁrst enumerated 16 possible conﬁgurations. We then deﬁned methods
to assess the conﬁgurations for the cost and the security.
Our future perspective is to extend the method for otherquality attributes, including reliability, compatibility, maintainability, and portability, as deﬁned in the SQuaRE [13].
A second perspective is to introduce quantitative functions
associated to the attribute assessment. For instance, the network usage is correlated to the size of DTS in the learning
phase, as well as the volume of communication between DT
and PM in the operation phase. Complex models have been
proposed in the literature to evaluate the cost at both side [14].
The idea is to re-investigate those models to derive a more
abstract and universal view. Our ﬁnal goal is to recommend
the best conﬁguration. For this, we need to develop a methodthat collects requirements and constraints from the user (e.g.
the size of the training data, the location of the data, the edge
capabilities). In the workshop, we hope to discuss these issues
with interested researchers and practitioners.

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