Evaluating the Effect of Utility-based Decision Making in Collective
Adaptive Systems
Vasilios Andrikopoulos
1
, Marina Bitsaki
2
, Santiago G
´
omez S
´
aez
1
, Michael Hahn
1
,
Dimka Karastoyanova
1
, Giorgos Koutras
2
and Alina Psycharaki
2
1
Institute of Architecture of Application Systems, University of Stuttgart, Stuttgart, Germany
2
Transformation Services Laboratory, University of Crete, Heraklion, Greece
Keywords:
Utility, Decision making, Collective Adaptive Systems, Choreography.
Abstract:
Utility, defined as the perceived satisfaction with a service, provides the ideal means for decision making on
the level of individual entities and collectives participating in a large-scale dynamic system. Previous works
have already introduced the concept into the area of collective adaptive systems, and have discussed what is
the necessary infrastructure to support the realization of the involved theoretical concepts into actual decision
making. In this work we focus on two aspects. First, we provide a concrete utility model for a case study that is
part of a larger research project. Second, we incorporate this model into our implementation of the proposed
architecture. More importantly, we design and execute an experiment that aims to empirically evaluate the use
of utility for decision making by comparing it against simpler decision making mechanisms.
1 INTRODUCTION
The concept of Collective Adaptive Systems (CAS)
as defined by (Kernbach et al., 2011) and extended
in (Andrikopoulos et al., 2013) encompasses systems
that consist of heterogeneous entities, both physical
and virtual, that collaborate towards satisfying their
individual objectives, and the global objective(s) of
the collective. Such systems underline the emergence,
rather than the prescription of behavior through the
interaction between entities that may be distributed
geographically and organizationally. The dynamics
of relationships between entities in CAS evolves over
time as their constituting entities adapt to external or
internal to the system environmental events. Large
scale, complex, and dynamic systems like those re-
quired for the realization of smart cities can therefore
be reflected and studied efficiently and effectively as
collective adaptive systems.
Previous work proposed the use of the concept of
utility from economics theory as the means to eval-
uate the satisfaction of the participating entities in a
CAS, calculated as the perceived achievement of their
individual objectives (Andrikopoulos et al., 2014b).
Utility for each entity is expressed as a function over
the preferences, context, state, and interactions of the
entity with other entities in the system. The concept
of utility is particularly interesting in the context of
CAS since it provides the means for informed deci-
sion making on both local and global level. This is
achieved by optimizing (that is, maximizing) the per-
ceived satisfaction of the entities from a certain state of
the system. Utility can be maximized for each partici-
pant entity individually, or as a group, introducing the
notion of collective utility. This work focuses on the
former case. A straightforward algorithm is proposed
in (Andrikopoulos et al., 2014b) for this purpose, to-
gether with the necessary steps for implementing them
in practice.
In this paper, we expand on the discussion in (An-
drikopoulos et al., 2014b) by defining concrete utility
functions for entities participating in the case study
CAS used by the ALLOW Ensembles project
1
. We
then show how the Cloud-enabled architecture pro-
posed in the same work can be realized in order to sup-
port utility-based decision making in the CAS of the
case study. The main focus of this work is on demon-
strating the effect of this decision making mechanism
in practice by emulating the behavior of the entities
in the CAS using the architecture we developed in
an experimental setting. This evaluation allows us to
draw conclusions with respect to the efficacy of our
1
http://www.allow-ensembles.eu/
Andrikopoulos, V., Bitsaki, M., Sáez, S., Hahn, M., Karastoyanova, D., Koutras, G. and Psycharaki, A.
Evaluating the Effect of Utility-based Decision Making in Collective Adaptive Systems.
In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 2, pages 39-47
ISBN: 978-989-758-182-3
Copyright
c
 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
39

proposal.
The contributions of this work can therefore be
summarized by the following:
• A detailed utility model for participant-level deci-
sion making in an example CAS (Section 3).
• An experimental evaluation of the proposed utility
model (Section 4) using the architecture proposed
in (Andrikopoulos et al., 2014b).
In addition, the paper provides the necessary back-
ground (Section 2) which this work builds upon, dis-
cusses the State of the Art in utility theory and related
works (Section 5), and presents our conclusions and
plans for future work in Section 6.
2 BACKGROUND
As discussed in previous works (Andrikopoulos et al.,
2013; Andrikopoulos et al., 2014b; Andrikopoulos
et al., 2014a), the ALLOW Ensembles project uses the
concepts of entities, cells and ensembles to model CAS.
Entities represent both software and human actors by
aggregating different functionalities performed or as-
sociated with this actor, expressed as reusable cells.
Cells therefore encapsulate functionalities that the en-
tity offers or requires from the system. The interaction
of entities follows the interaction between their cells
through their predefined functionalities and results into
ensembles. Ensembles are therefore representing the
emerging interactions between different entities in or-
der to fulfill their individual goals. Despite the fact
that each entity has its own objectives, the coopera-
tion with each other should result into an increase of
their perceived satisfaction in participating in an en-
semble, expressed as an increase in each entity’s utility.
Deciding therefore whether to participate into an en-
semble, or to which of one of the possible alternative
ensembles that are available in the system, becomes a
utility maximization problem for each entity. While in
the context of the project we also discuss cooperative
games (Wiese, 2010) where utility is aggregated into
a group level, for the purpose of this work we scope
the discussion to non-cooperative games, where each
entity attempts to optimize for itself its utility.
The case study used in the ALLOW Ensembles
project for demonstration and evaluation purposes, and
therefore the one adopted by this work, is that of an
Urban Mobility System (UMS) that acts as a smart
city planner in a multi-modal transportation scenario.
The city used as the model for this scenario is that of
Trento, Italy. From the transportation means available
in the scenario, of particular interest for the purposes
of this paper is the use of utility for comparing be-
tween using one own’s car versus the use of public
transportation (that is, bus), which may also include
some walking to reach the closest bus stop, and of
course some waiting time until a bus arrives. The main
entities under consideration in this case consist of the
users of the UMS that want to travel from randomized
points to other randomized points on the map of Trento
in a given time interval. Depending on whether users
decide to use the public transportation (and walk/wait
as necessary) or to take their own car for this trip, the
entities participate in either a bus route ensemble, or
form their own ensemble, respectively.
In the following sections we discuss a) how the
utility of the UMS users can be modeled, b) what is the
necessary infrastructure for supporting the discussed
scenario, and c) what kind of conclusions can be drawn
about the effect of using utility in the decision making
of the entities in this scenario.
3 UTILITY MODEL
In this section we extend the discussion in (An-
drikopoulos et al., 2014b; Andrikopoulos et al., 2014a)
to propose a utility model that describes the procedure
for the calculation of the utility of an entity for partici-
pating in a specific ensemble (given as an alternative
among various ones that fit the entity’s request). The
model comprises the following components:
1. Input/information required to calculate utility for
an alternative (e.g. a route or a combination of
routes):
a. Variables that affect utility (e.g. travel time,
travel cost, walking duration, etc.). They are
specified by the designer of the utility model
in accordance with the characteristics of the en-
tity’s request. These variables are random vari-
ables; their value is realized after the end of the
execution of the ensemble. Their estimation is
used in calculating values of utility beforehand.
Let ~v = (v
1
,...,v
m
) be the vector of variables
that we are interested in. In our case study we
consider the following variables: travel time
(t) , travel cost (c) , walking duration (d) , and
number of changes (n).
b. Entity’s preferences including: upper and lower
bounds of the values of variables (constraints)
and weights for the variables that indicate their
effect on the utility function (e.g. specify priori-
ties with respect to variables: I prefer a cheaper
trip than a faster trip). Preferences are specified
by the entity and are part of the request. They
are constant numbers for the whole life-cycle of
CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science
40

the specific ensemble given by the user.
Let ~a = (a
1
,...,a
n
) be the vector of preferences
and ~w = (w
1
,...,w
m
) be the vector of weights
for the m variables defined above. In our case
study we consider the following preferences:
maximum travel time (t
max
) , maximum travel
cost (c
max
) , maximum walking duration (d
max
)
and maximum number of changes (n
max
).
2. Interdependencies between any two of the above
variables in order to select the appropriate forms
of utility functions. They are specified by the de-
signer of the model. We consider two different
ways for recognizing that two variables v
1
,v
2
are
interdependent:
a. There is a known function f such that v
1
=
f (v
2
) . In this case we substitute v
1
for v
2
in
the utility function and eliminate one of the two
variables.
b. For each value v
1k
of variable v
1
there is an
associated value v
2k
of variable v
2
. In this
case, we calculate the utility function for pairs
(v
1k
,v
2k
),k ∈ K and apply a common weight for
both variables.
Let ~v
i j
= (v
i
,v
j
) be the vector of the interdepen-
dent variables v
i
and v
j
.
In our case study, the variables we use (see previ-
ous step) are independent of one another, thus, we
have an additive model for the utility function as
we will see in the next step.
3. Utility functions that are specified by the designer
and reflect the entity’s preferences. Let u(~v,~a,~w)
be the utility function of the entity as a function of
the variables, preferences and weights as defined
above.
a. In the case where no interdependencies exist
then:
u(~v,~a,~w) =
= w
1
f
1
(v
1
,~a) + ... + w
m
f
m
(v
m
,~a),
(1)
where f
i
,i = 1,..., m is the component that con-
tributes to the total utility related to variable v
i
.
b. In the presence of interdependencies, we have:
u(~v,~a,~w) =
=
∑
k∈I
1
w
k
f
k
(v
k
,~a) +
∑
i, j∈I
2
w
i j
f
i j
(v
i j
,~a)
(2)
where I
1
is the index for all independent
variables and I
2
is the index for all pairs
of interdependent variables. For exam-
ple, given that ~v = (v
1
,...,v
7
) and depen-
dencies exist on pairs v
34
= (v
3
,v
4
) and
v
67
= (v
6
,v
7
) , then the utility is given
by: u(~v,~a,~w) = w
1
f
1
(v
1
,~a) + w
2
f
2
(v
2
,~a) +
w
34
f
34
(v
34
,~a) + w
5
f
5
(v
5
,~a) + w
67
f
67
(v
67
,~a).
In our case study, the additive model in which no
interdependencies exist is appropriate and there-
fore we use Equation 1 for the definition of utility
functions.
Based on the above, we propose the following
types of utility functions for the variables of interest,
namely, travel time, travel cost, walking duration, and
number of changes:
1. u
1
(t,t
max
) = e
−k
1
t/t
max
,t ≥ 0
The component of utility that corresponds to time
is a decreasing function of time. Parameter k
1
(k
1
>
0) is a constant number that reflects the rate of the
decrease of utility with respect to t
max
.
2. u
2
(c,c
max
) = e
−k
2
c/c
max
,c ≥ 0
Utility is a decreasing function of cost. Parameter
k
2
(k
2
> 0) is a constant number that reflects the
rate of the decrease of utility with respect to c
max
.
3. u
3
(d,d
max
) = e
−k
3
(d/d
max
)
2
,d > 0
This is a decreasing function of walking duration.
The utility decreases slower in low values of d than
in high values.
4. u
4
(n) =
(
n
max
−n+1
n
max
n = 1, . . . , n
max
0 n > n
max
This is a decreasing function in the number of
changes.
The utility of each user entity (as discussed in Sec-
tion 2) can therefore be calculated as the weighted sum
of u
1
,...,u
4
:
u(~v,~a,~w) =
= w
t
e
−k
1
t/t
max
+ w
c
e
−k
2
c/c
max
+
+ w
d
e
−k
3
(d/d
max
)
2
+ w
n
n
max
− n + 1
n
max
(3)
where w
t
+ w
c
+ w
d
+ w
n
= 1 for normalization pur-
poses.
4 EVALUATION
The purpose of evaluation in this work is twofold:
on one hand, we want to evaluate the efficacy of the
proposed utility model, as discussed in Section 3, in
decision making in the context of the CAS outline in
Section 2; on the other hand, we also aim to evaluate
the appropriateness of the architecture discussed in
Evaluating the Effect of Utility-based Decision Making in Collective Adaptive Systems
41

the previous section as the underlying infrastructure
supporting the same CAS. For this purpose we use
an experimental evaluation that emulates the actual
operation of the CAS in a smaller, and more manage-
able sample of the population under the assumptions
discussed in the following.
4.1 Experiments Description
We designed two experiments for evaluation purposes.
In both experiments we assess the impact of utility
in decision making by comparing the decisions taken
for a fixed set of users when attempting a trip from
a random point in Trento’s map to another also ran-
domly generated destination in Trento. Users have
profiles with different preferences allowing them to
prioritize the available transportation modes (public
transportation or car) and routes in different ways. The
experiments are performed for a set of one thousand
users distributed in three different profile types: work-
ers, students, and pensioners. All trip requests take
place within a fixed interval during the morning of a
working day. Experiment A measures which transporta-
tion mode was selected by each user when a) the utility
model of Section 3 (and in particular, Equation 3) was
used to decide the best option for each user, b) only the
duration of the trip is taken into consideration (short-
est is better), and c) only the cost of the trip is used
(cheaper is better). Experiment B measures the effect
of the bus fare price to the choice between public and
private transportation by users when utility is used for
decision making. For this purpose we reduce the bus
fare in fixed decrements and we measure the selected
transportation mode by each user as before. The setup
for both experiments is described in the following.
4.2 Experimental Setup
In terms of infrastructure for our experimental eval-
uation, we extend the system architecture proposed
in (Andrikopoulos et al., 2014b) and add the necessary
components for generating and driving the resulting
system with a representative load. The resulting sys-
tem is summarized by Fig. 1. More specifically, the
system discussed in (Andrikopoulos et al., 2014b) dis-
tinguishes between a Modeling Environment and a
Runtime Environment. The Modeling Environment,
implemented as an Eclipse Graphical Editor
2
allows
the definition of cells and ensembles (as discussed
in Section 2) as a set of service orchestrations and
choreographies, respectively. WS-BPEL is used for
the former, and the BPEL4Chor language for the latter,
2
Eclipse Graphical Editing Framework:
https://eclipse.org/ gef/
as discussed in (Andrikopoulos et al., 2014b). For
the purposes of our evaluation, we used the Modeling
Environment to design the ensemble and the respec-
tive cells allowing a passenger to inquire the UMS for
traveling options between two points in a city. UMS
replies with a set of route alternatives that include both
public, i.e. buses and possibly walking, and private,
i.e. car driving transportation modes. Decision making
based on different policies, i.e. utility, trip duration, or
cost is also modeled as a cell in the system, allowing
the automation of the experiment.
The execution of the cells takes place in the Ex-
ecution Engine component of the Runtime Environ-
ment, implemented based on the Apache ODE
3
En-
gine, an open source implementation of BPEL. The
Utility Module in Fig. 1 implements the utility model
discussed in Section 3 as a set of Web Services that
are interacting with the Execution Engine through an
Enterprise Service Bus (ESB). More specifically, the
Utility Model services accept a list of route alterna-
tives and a unique entity identifier, representing each
user of the UMS, and return the route alternative list
ordered by their calculated utility for the specific en-
tity. The entity identifiers, together with their profiles
consisting of their preferences with respect to maxi-
mum traveling time, cost, etc. are stored in the Entity
Management System component of the system, also
implemented as a set of Web Services on top of a
database for persistence purposes. The Adaptation
Manager and Monitoring components in Fig. 1 are out
of the scope of this evaluation, and therefore they will
be omitted from the rest of the discussion; here they
are presented for completeness.
The main difference between the system discussed
in (Andrikopoulos et al., 2014b), and the one presented
in Fig. 1 is the addition of a third aspect, that of Load
Generation & Driver. This contains the components
of Entity Generator, responsible for generating enti-
ties and their profiles for experimental use, the Route
Generator, which produces the route alternatives for
the possible trips, and the Load Driver that generates
a load for the system emulating the behavior of the
entities generated by Entity Generator using the routes
produced by the Route Generator. In the following we
discuss how we implemented these latter components,
as well as the steps necessary for preparing and exe-
cuting our experiment. W.r.t. the deployment of the
infrastructure depicted in Figure 1, the components
are distributed in on-premise, private cloud facilities
of both institutions. The Modeling and Load Genera-
tion & Driver components, and Execution engine are
distributed in separate machines in the University of
3
Apache ODE: http://ode.apache.org/
CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science
42

1
Runtime Environment
Utility
Module
Decision
Module
Utility
Evaluator
Monitoring
Entity
Management
System
Execution Engine
ESB
Modeling Environment
Choreography
Processes
Choreography
Processes
Adaptation
Manager
Load Generation & Driver
Entity
Generator
Load Driver
Route
Generator
Trip Booking Ensemble & Cells
Trip Booking Strategic Ensemble & Cells
Figure 1: Architecture of the Experimental Setup (adapted from (Andrikopoulos et al., 2014b)).
Stuttgart
4
. The Utility Module, as well as the Entity
Management System, reside in an on-premise infras-
tructure in the University of Crete
5
.
4.3 Experiment Preparation &
Execution
Entity Profiles
The Entity Generator component of Fig. 1 was cre-
ated using the capabilities offered by the R language
6
.
More specifically, for each randomly generated user
identified by name/surname and a generated UUID, a
profile was created by randomizing their preferences
within acceptable value ranges for the variables un-
der consideration (trip duration, trip cost, number
of changes, walking duration, and their respective
weights). Table 1 summarizes the ranges that we de-
fined for this purpose, chosen to reflect the different
characteristics of the defined user groups. As shown in
the table, users were assigned to the student, worker,
or pensioner group in a 0.3/0.5/0.2 ratio, respectively,
and values for each of their preference components
was generated based on a uniform distribution model
4
IAAS - University of Stuttgart: http://www.iaas.
uni-stuttgart.de/
5
TSL - University of Crete: http://www.tsl.gr/
6
The R project for Statistical Computing: https://
www.r-project.org/
Figure 2: Trip distribution organized by population cluster
center.
within the ranges defined in Table 1. The created user
profiles were then imported into the Entity Manage-
ment System of Fig. 1 for persistence and retrieval
purposes.
Trips
The next step for setting up the experiments was to
distribute randomly the generated users on the map
Evaluating the Effect of Utility-based Decision Making in Collective Adaptive Systems
43

Table 1: Value ranges for user profile generation.
User Profile Type
Preference Student Worker Pensioner
Upper bound of duration t
max
(minutes per km) [3,5] [2,4] [3,4]
Upper bound of travel cost c
f ixed
(euros) [1,3] [1,4] [2,3]
Maximum number of changes n
changes
(integer) {2,3} {2,3} {1,2}
Maximum walking duration w
max
(minutes) [10,20] [10,15] [5,10]
Weight of travel time w
t
[0.3,0.5] [0.5,0.7] [0.2,0.4]
Weight of cost w
c
[0.5,0.9] [0.4,0.8] [0.3,0.5]
Weight of walking duration w
d
[0.1,0.3] [0.2,0.5] [0.4,0.6]
Weight of number of changes w
n
[0.2,0.4] [0.3,0.6] [0.2,0.3]
Ratio in population 30% 50% 20%
of Trento, roughly following the population distribu-
tion in the actual city, and by choosing an origin and
a destination point for each. In order to reduce the
complexity of the experimental setup, remove possible
performance perturbation due to uncontrolled network
latencies, and avoid unnecessary for our purposes de-
velopment efforts we reduced the possible trips into a
smaller possible set and pre-calculated the route alter-
natives for each trip. For this purpose we started by
clustering (more specifically: k-means clustering) the
origin and destination points for each user into two sets
of 10 clusters each. The resulting origin and destina-
tion cluster centers are shown in Fig. 2 as upward- and
downward-facing triangles, respectively. The width
of the straight lines between cluster centers in Fig. 2
denotes the relative density of this trip in numbers of
entities per trip (cluster center pair).
We then created the Route Generator component
of Fig. 1. For this purpose, we used the integration
offered by the ggmap package of R with the Google
Maps API
7
to retrieve, and then re-format appropri-
ately and consequently persist route alternatives for
the 10 × 10 possible trips between cluster centers. For
most of the trips, 4 route alternatives combining bus
and walking, and 3 route alternatives for using a car
were produced in this way. A total of 611 possible
routes were produced in this way. The estimated time
and distance provided by the Google Maps service was
also attached to each route alternative. This allowed
us to calculate an estimate for the cost of each trip, as
follows:
1. For routes by car, we used the gas consumption of
a reference popular car model (Fiat Panda 2014
8
)
7
Google Maps API: https://developers.google.com/maps/
?hl=en
8
Based on the statistics available in http://
www.statista.com/statistics/417585/italy-leading-car-
brand-sales/
and an indicative price for gas for the Trento area
9
to calculate the cost as
c
driving
=
distance(km) × gas price per liter
kilometers per liter
.
2. For routes combining buses and walking, we
amortized the cost of public transportation tick-
ets in Trento (0.70 AC for 70’ of traveling, as of
September 2015) into a linear cost model and used
only the time spent on bus segments to calcu-
late the cost of the route alternative as c
public
=
#segments
∑
i
duration (sec) ×
0.7
70 × 60
.
These costs were persisted together with the rest of the
trips information also in the Entity Management Sys-
tem of Fig. 1. For the purposes of Experiment B, we
recalculated the cost of the route alternatives involving
public transportation by reducing the original bus fare
by 10% in each decrement until 50% of the original
fare is reached. The cost for the respective trips was
updated in the Entity Management System before each
iteration of the experiment as appropriate.
System Load
For the system load used by the Load Driver (Fig. 1)
that drives our experiments we decided that it needs
to be as close as to the real-word as possible. For
this purpose, we modeled the load of the system using
a Poisson distribution with arrival rate λ = 5 , which
allows us to spread the 1K possible trips (one trip per
person) into 15 time intervals using the rpois command
of R for generating sample values of this distribution.
Figure 3 illustrates the number of requests per time
interval. We created intervals of 10 seconds in between
each group of concurrent requests (meaning that e.g.
9
That is, 1.54 AC per liter on average in September 2015,
according to http://www.numbeo.com/cost-of-living/
CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science
44

Figure 3: Trip requests by time interval (λ = 5).
Figure 4: Experiment A — Distribution of transportation
modes per policy.
10 requests were sent simultaneously at the beginning
of the experiment, 42 after 10 secs, etc.), and that of
15’ in the emulation starting from 06:00 on the 1st of
October 2015 (i.e. 10 users sent their trip request at
06:00, 42 at 06:15, etc.).
Apache JMeter
10
was used as the load driver for the
system. We created a JMeter Test Plan comprising the
collection and processing functionalities for the load
provided by the Load Driver, and the aggregation of the
results obtained from the execution of each ensemble.
System performance metrics were also measured and
plan to be reported in future reports.
4.4 Findings
Figure 4 summarizes the results of Experiment A in
terms of the decisions between route alternatives based
on private or public transportation for each entity in the
load, and for the different policies applied. As it can
be seen from the figure, when entities make decisions
solely based on the cost of the trip, the majority (72.3%
of the population) opts for using public transportation.
While this result is sensitive to the cost model attached
10
Apache JMeter 2.10: http://jmeter.apache.org/
to the routes entailing bus usage, it confirms the intu-
ition that public transportation is indeed cheaper on
average. On the other hand, selecting transportation
mode based on trip duration has the opposite result,
with almost 90% of the population opting for driving
their own cars to their destination. Finally, as shown
in Fig. 4, using utility essentially balances the effect of
cost and duration to decision making with 25.8% of the
population choosing for public transportation. How-
ever, cost still seems to play the most important role in
the decision making. This can be largely attributed to
the generated profile values for the weight coefficients
in Equation 3 in comparison to the available route al-
ternatives. Evaluating the effect of different profiles
on the decision making is currently ongoing work.
Figure 5 presents the results of Experiment B on
the effect of reducing the bus fare cost by 30% and
50% to the decision making of entities in the system,
when utility is used as policy. As expected, reducing
the bus fare results into more entities choosing for pub-
lic transportation; however this increase into public
transportation is linear in nature (Fig. 5(a)). Further-
more, the rate of increase in terms of utility (Fig. 5(b))
for entities choosing public transportation actually de-
creases as the cost is further reduced, since the other
parameters in the model (total duration, walking du-
ration, number of changes) start affecting the entity’s
utility mode more. As a result, when considering the
trade-off between passenger utility gains and trans-
portation company revenue loss it can be concluded
that the 30% reduction to bus fare would be preferable
for all parties involved.
5 RELATED WORK
Utility-based decision making is a well-known eco-
nomic study field typically used in decision mak-
ing mechanisms, e.g. in multi-attribute utility the-
ory (Keeney and Raiffa, 1976). Focusing on the scope
of this work, we leverage the usage of utility as a the
fundamental mechanism for measuring the satisfaction
and the decisions taken by travelers in a multi-modal
transportation system, e.g. based on their commuting
time, city pollution, etc.
In the scope of multi-modal transportation, several
studies have been conducted for modeling commuting
time and analyzing congestion management strategies,
including travelers departure time choice, route choice
or mode choice (Lam and Small, 2001; B. Johansson
and Olsson, 2003; Li and Huang, 2005). In (Lam and
Small, 2001), a method to measure how travel time
and its reliability are valued by travelers is proposed,
e.g. taking into account the value of time for a trav-
Evaluating the Effect of Utility-based Decision Making in Collective Adaptive Systems
45

(a) Number of private/public transportation mode selections
as a function of bus fare cost
(b) Average utility as a function of bus fare cost
Figure 5: Experiment B — Effect of bus fare cost reductions to decision making.
eler as the rate of change in utility. In (B. Johansson
and Olsson, 2003), the labor market commuter be-
havior is analyzed in terms of the number of traveled
short, medium and long time distances. In (Li and
Huang, 2005), the reliability of morning commuting
in congested and uncertain transport networks is inves-
tigated. Focusing on non-cooperative games, i.e. how
commuters independently choose their optimal routes,
(Sun and Gao, 2007), (Anas and Berliant, 2010)),
and (Sun and Gao, 2007) examine how commuters
choose their optimal routes and trip modes using non-
cooperative games. Our work defers w.r.t. current state
of the art in the following aspects: (i) we provide ser-
vices that are shared among various users in the system
taking into consideration the interdependencies among
them, (ii) the decision making mechanism in our sys-
tem is a distributed process performed by the users
themselves using private information (value of utility)
and sharing common parts of information (route char-
acteristics such as travel time and cost), and (iii) our
approach deals with the evaluation of services offered
to users that are not predefined, but rather customized
according to user preferences.
The definition of such services and decision mak-
ing mechanisms as interdependent distributed pro-
cesses, however, requires further analysis on exist-
ing modeling, provisioning, execution, and adaptation
techniques in large scale CAS. For instance, typical
interactions between multiple participants in a chore-
ography can be modeled following the interaction or
the interconnection modeling approaches (Barker et al.,
2009). In the former, communication between partic-
ipants is modeled using atomic interaction activities.
The WS-CDL (Kavantzas et al., 2005) and the Savara
11
11
http://www.jboss.org/savara
project are approaches that support the explicit spec-
ification of the interaction activities. On the other
hand, the interconnection modeling approach consists
on interconnecting the communication activities of
each participant of the choreography. This approach
is supported in the CHOReOS Integrated Develop-
ment and Runtime Environment
12
, in the Open Knowl-
edge European project
13
, and in BPEL4Chor (Decker
et al., 2007). As BPEL4Chor enables the choreog-
raphy specification atop of WS-BPEL and decouples
the choreography behavior specification from the tech-
nical communication details, it is considered as an
appropriate extensible point in CAS modeling and
specification. The CoDyCo framework has been devel-
oped in the scope of the ALLOW Ensembles project
supporting the modeling, provisioning, and execu-
tion of large scale interactions among multiple enti-
ties (G
´
omez S
´
aez et al., 2015). Such a framework has
been completely reused and extended towards integrat-
ing performance and utility aspects for the experiments
depicted in this paper.
6 CONCLUSIONS AND FUTURE
WORK
The previous sections use the case study of an urban
mobility system discussed in an EU project to demon-
strate the use of the concept of utility in decision mak-
ing in large-scale systems like this of the case study.
Utility as the perceived satisfaction with a service is
used here to quantify the degree of satisfaction of the
12
CHOReOS: Large Scale Choreographies for the Future
Internet: http://www.choreos.eu/
13
Open Knowledge: http://www.openk.org/
CLOSER 2016 - 6th International Conference on Cloud Computing and Services Science
46

entities with the system, i.e. the users asking the sys-
tem to provide them with the optimal option for trans-
portation between points in the city. In this case, utility
is expressed as a function over a set of preferences and
context attributes related to e.g. the duration and cost
of each route alternative that the system can produce.
As it is shown by the experimental evaluation based
on an emulation of the actual urban mobility system
using synthetic data but the actual infrastructure for
it, utility-based decision making produces results that
depend heavily on the preferences of the entities in the
system.
Future work aims to further evaluate the observed
effect of user preferences to utility calculation in the
same experimental scenario. Furthermore, we intend
to introduce and evaluate similarly a cooperative game
formulation to the case study, taking into account, for
example, the delays to the trip depending on the num-
ber of passengers joining a bus route.
ACKNOWLEDGEMENTS
This work is partially funded by the FP7 EU-FET
project 600792 ALLOW Ensembles and the German
610872 DFG Project SitOPT.
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