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Optimising Architectures for Performance, Cost, and Security


Abstract and Figures

Deciding on the optimal architecture of a software system is difficult, as the number of design alternatives and component interactions can be overwhelmingly large. Adding security considerations can make architecture evaluation even more challenging. Existing model-based approaches for architecture optimisation usually focus on performance and cost constraints. This paper proposes a model-based architecture optimisation approach that advances the state-of-the-art by adding security constraints. The proposed approach is implemented in a prototype tool, by extending Palladio Component Model (PCM) and PerOpteryx. Through a laboratory-based evaluation study of a multi-party confidential data analytics system, we show how our tool discovers secure architectural design options on the Pareto frontier of cost and performance.
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Optimising architectures for performance, cost,
and security
Rajitha Yasaweerasinghelage1,2, Mark Staples1,2, Hye-Young Paik1,2, and
Ingo Weber1,2
1Data61, CSIRO, Level 5, 13 Garden St, Eveleigh NSW 2015, Australia
2School of Computer Science and Engineering,
University of New South Wales, NSW 2052, Australia
Abstract. Deciding on the optimal architecture of a software system is
difficult, as the number of design alternatives and component interactions
can be overwhelmingly large. Adding security considerations can make
architecture evaluation even more challenging. Existing model-based ap-
proaches for architecture optimisation usually focus on performance and
cost constraints. This paper proposes a model-based architecture opti-
misation approach that advances the state-of-the-art by adding secu-
rity constraints. The proposed approach is implemented in a prototype
tool, by extending Palladio Component Model (PCM) and PerOpteryx.
Through a laboratory-based evaluation study of a multi-party confiden-
tial data analytics system, we show how our tool discovers secure archi-
tectural design options on the Pareto frontier of cost and performance.
Keywords: Software architecture ·Software performance ·Data secu-
rity ·Architecture optimisation
1 Introduction
Many software systems today are complex, with thousands of deployed compo-
nents and many stakeholders [19]. With increasing complexity, there is increas-
ing development cost. Non-functional requirements for systems often include re-
sponse time, cost of development and operation, and security. When developing
systems, software architecture should support these requirements effectively.
There are inter-dependencies and trade-offs between quality attributes like
performance, cost, and security. For example, secure components are generally
more costly than non-secure components. Prior work reports costs of $10,000
per line of code to develop highly-secure components, compared to $30-$40 per
line of code for less-secure components [7,11]. When designing systems with
critical requirements for performance, cost, and security, architects try to achieve
optimal trade-offs between them. In a large design space, with many components
and design options, finding designs with good trade-offs is challenging, even for
experienced architects. Manually assessing and comparing quality attributes for
even a small number of design alternatives is difficult and error-prone.
2 Yasaweerasinghelage et al.
Model-based design is now a common practice, and helps architects explore
options during design. Many architecture modelling and optimisation methods
have been studied [2,3,4]. There are well-established methods for optimising
deployment architecture based on the performance of the system [13,16], costs
of development, deployment, and maintenance [16], and other constraints such
as energy consumption [21]. However, security constraints and policies are not
yet well-treated in existing literature on architectural optimisation [1].
In this paper, we propose a new approach for optimising for performance,
cost, and security in architectural design. We demonstrate the feasibility of
the approach by implementing a prototype which extends the Palladio Com-
ponent Model [4] and PerOpteryx optimisation tool [13] to support static taint
analysis. One challenge in designing secure systems is defining and evaluating
system security. Optimisation techniques require automated assessments. Static
taint analysis is a simple automatic security analysis approach. Taint analysis is
not a perfect model of security, but is a popular technique for identification of
architecture-level vulnerabilities related to data propagation in the design phase
[22]. Although our prototype uses taint analysis, our approach is more general
and we discuss the use of other techniques for security analysis.
The main contributions of this paper are: an approach for architectural op-
timisation for cost, performance, and security; a model and method for taint
analysis for security analysis for Palladio and PerOpteryx; and an evaluation of
the approach on an industrial use case demonstrating feasibility and the ability
to generate useful insights: in the case study, best performance and cost were
achieved by non-secure architectures, but secure architectures were not far be-
hind. Also, the approach discovered distinctive design options on the Pareto
frontier of cost and performance for secure designs.
The paper structured is as follows. In Section 2, we introduce existing tech-
nologies relevant to the proposed approach. Then we provide an overview of
the proposed method in Section 3.Section 4 provides details about modelling
and optimisation through a running example. We discuss and compare literature
closely related to this work in Section 5, propose suggestions for future work in
Section 6 and conclude the paper with Section 7.
2 Background
This section reviews: architecture performance modelling; architecture design
space exploration and deployment optimisation; and static taint analysis.
2.1 Architecture Performance Modelling
Architectural models capture the system structure by representing the links be-
tween components. Performance characteristics are associated with these com-
ponents and their composition. Popular frameworks for architectural modelling
are the Palladio Component Model (PCM) [18], and Descartes Modelling Lan-
guage [12]. Architectural models can incorporate additional non-functional at-
tributes associated with the system structure, such as latency, resource usage,
Optimising architectures for performance, cost, and security 3
cost and throughput. The resulting models can be used by simulation engines
or analytical solvers to analyse non-functional properties [5]. Simulation-based
prediction can be time-consuming, but provides more flexibility for modelling.
Palladio Component Model (PCM) [18] is the platform used in this paper to
model architecture performance characteristics. Palladio was selected as it is
freely available, supports simulation, provides a familiar ‘UML-like’ interface for
model creation, and has the flexibility to incorporate extensions such as architec-
tural optimisation tools [13,8], new qualities [21], and new kinds of systems [23].
The modelling concepts in Palladio align with component-based development
paradigm and support component reuse across models.
2.2 Architecture design space exploration and deployment
architecture optimisation
Automated software architecture exploration based on architecture models is
increasingly popular in industry. Aleti et al. [1] surveys existing methods.
PerOpteryx [13] is an automated design space exploration tool for PCM, capable
of exploring many degrees of freedom. PerOpteryx starts with a PCM instance
and a set of design decision models that describe how the architecture can be
changed. Automated search over this design space is performed using a genetic
algorithm. For each generation in the search, a Palladio instance is generated
and analysed to evaluate quality attributes such as performance and cost.
PerOpteryx is capable of optimising multiple quality attributes by searching
for Pareto-optimal candidates. A candidate is Pareto optimal if there exists no
other candidate that is better across all quality metrics. A set of Pareto-optimal
candidates approximate the set of globally Pareto-optimal candidates [8].
2.3 Static Taint Analysis
Defining meaningful quantitative metrics for security is challenging. There have
been a number of approaches proposed, but in our opinion, there is no single
generic method suitable for all applications (see Section 5). In this paper, to
simplify our demonstration of security analytics for optimisation, we use taint
analysis. Taint analysis results in a binary secure/not-secure evaluation for a
system, which is arguably the most challenging kind of metric for use in optimi-
sation. Taint analysis is simple but useful in identifying fundamental issues in
the data flow of the system, as a form of information flow analysis [17,22].
Taint is used to represent the reach of an attack within a system. As shown
in Fig. 1, taint starts at a taint source (node 1), which could be a component
exposed to the external environment, then flows to connected components. Taint
blockers (e.g. node 6) are secure components which prevent further propagation
of taint. A system security property defines the set of critical components (e.g.
node 7) which must remain free of taint after maximal propagation of taint
through the system. The system in Fig. 1 is not secure, because taint can flow
through non-secure components (e.g. nodes 2, 5) to the critical component.
4 Yasaweerasinghelage et al.
Tar g e tTaint Source Tai n t B ar r ie r
Fig. 1. Graph taint analysis, illustrating an insecure system. Bad ‘taint’ from the source
Node 1 to the critical target Node 7, via a path through Node 2, despite being blocked
from flowing through the taint barrier at Node 6.
Security%analysi s%
Annotation models
Architecture model Design search space model
Design decision
Design space exploration
(Extended PerOpteryx)
Optimal architecture
annotated%Palladio %
component %model
Proposed models/extensions
Fig. 2. Method overview, highlighting extensions proposed in this paper.
3 Method Overview
Our approach, shown in Fig. 2, combines architecture-level performance mod-
elling, simulation and optimisation. We use three types of models to represent
the system: the initial architecture model, annotation models, and the design
search space model. We use the Palladio Component Model (PCM) tool for the
underlying architecture model. To define annotation models, we annotate PCM
with information about three quality attributes; performance, cost, and security.
The performance annotation model is supported directly in PCM, and the Pal-
ladio cost extension is used for cost annotations. The security model is defined
separately. In Section 3.1, we describe how each quality attribute is modelled.
For the design search space model, we used Palladio Design Decision Dia-
grams. These are used to generate candidate architectures in the optimisation
phase. Some design options are specific to security architecture. For example, a
component might be modelled as being a secure component that works as a taint
Optimising architectures for performance, cost, and security 5
barrier. So, the default Palladio Design Decision Diagrams need to be extended
to accommodate these model elements.
For design space exploration, we use PerOpteryx optimisation tool with mod-
ifications to use these extended security annotation models. The output is a set of
generated Pareto-optimal candidate architectures, which can be used by experts
to select the final design.
3.1 Quality attribute modelling for the optimisation
The first step of the proposed approach is to model each quality attribute.
Performance Modelling. We used PCM performance analysis, as discussed
in the literature [9], which has been shown to be sufficiently accurate for various
types of applications, including the example system discussed in this paper.
This demonstrates that our approach allows the reuse of previously-developed
Palladio performance models.
The security level of a component may affect the resource utilisation of the
component, impacting the overall performance of the system. (For example, en-
crypting communications may incur a performance overhead.) In such cases,
a component with one kind of functionality is modelled with different perfor-
mance (and security) properties as design alternatives, and are used for design
exploration during optimisation.
Cost Modelling. We use the existing and well-studied Palladio cost modelling
extension for modelling cost attributes. This can capture different types of costs
such as component costs, variable and fixed resource costs, and networking costs.
The security level of a component can impact its cost. For example, secure
components are more expensive to develop than less-secure components. We
model a component with one kind of functionality as multiple alternative com-
ponents that represent different levels of security each with a corresponding cost
in the cost model. Then we use those component alternatives when exploring
options during optimisation.
Security Modelling. A key contribution of this paper is integrating security
analysis into automatic design space exploration. Unlike other quality attributes
such as performance and cost, security is not easily quantifiable. Security ana-
lyses often only make Boolean judgements about system security (i.e., secure,
or not), but some analyses give continuous metrics of security (e.g., expected
time to next attack). In this paper, we demonstrate our approach using taint
analysis as the basis for security analysis. However, our general approach could
be adapted to use other security analysis metrics, as discussed in Section 5.
6 Yasaweerasinghelage et al.
4 Modelling and Optimising
The prototype for our approach uses taint analysis (see Section 2.3) as the se-
curity analysis technique. As our goal is to optimise performance and cost while
satisfying a security requirement, we developed an extension for integrating taint
analysis with existing Palladio Models and incorporating taint properties into
the PerOpteryx optimisation. To describe the modelling and optimisation pro-
cess, we use a running example based on a privacy-preserving computing system
called N1Analytics3[9]. This section provides details about the extension and
how it works for the running example. Finally, we discuss how the architecture
of the N1 Analytics system can be optimised for performance, cost, and taint
4.1 Running example
N1Analytics is a platform that allows statistical analyses using data distributed
among multiple providers, while maintaining data confidentiality between the
providers. Following the main principles of N1Analytics systems, we designed
an initial abstract architecture, to illustrate some of the critical features of our
proposed approach. It should be noted that this abstract architecture differs
from actual N1Analytics implementations.
Base Deployment Architecture. Fig. 3 presents the federated deployment
architecture of the N1Analytics platform. Data providers and coordinators are
the two main building blocks. In an analytics operation, the coordinators have
the private key to decrypt the computed results but do not have access to plain or
encrypted input data. They only have a partial output that is not itself capable
of revealing plaintext results.
The private key is not accessible to the data providers, so they cannot violate
the privacy of the encrypted input data shared with them. Data providers and
coordinators may have a set of worker nodes to perform their operations. It is
possible to move data between nodes, as long as they preserve the protocol: the
coordinator should not have access to the encrypted data, and data providers
should not have access to the private keys.
Component Architecture. To simplify the demonstration, we modify the
architecture of the N1Analytics system used in our earlier work [24] by assuming
that the basic component architecture of the coordinator and each data provider
is similar. Even so, the resource utilisation and the functionality of each node
are different. Notably, the computation overhead and workflow of each node are
significantly different. We model each node separately to reflect those differences.
Fig. 4 presents the architecture model we considered.
Optimising architectures for performance, cost, and security 7
Homomorphic/ Key/Gene ration
Job/S cheduling
Resul t/ Decry ptio n
Data/Ma nagement
Providing/ Part/of/F eatures/a nd/
Data/Ma nagement
Providing/ Part/of/F eatures/a nd/
Providing/ Labels
Partial Output
Partial Data/
Fig. 3. N1Analytics platform distributed architecture
User AP
External Node
Data Acess
Request to the next node with partial results
Request from External Nodes
Fig. 4. N1Analytics Component Architecture in UML notation
4.2 Modelling System for optimisation
Performance Modelling. We modelled performance characteristics following
the general Palladio approach. Our model of the N1Analytics system is similar
to that presented in our earlier work [24], but introduces changes to demonstrate
cost-based optimization and security-critical components.
In [24], the N1Analytics system was deployed in a test environment, and the
resource utilisation of each development component was measured. Then, each
development component was mapped to a functional component to be used in
the model architecture. The architecture is modelled in PCM using functional
components, and the resource utilisation of each component is derived from mi-
crobenchmark results. Resource utilisation is defined as a function of workload
and the size of the data set. The resource environment definition, usage model,
and allocation model were defined based on the design specification of the sys-
tem. We reuse their published abstract model 4, but with minor modifications
to introduce a user access point component, a parser, and database access com-
ponent for demonstrating data propagation design options.
Cost Modelling. We used the standard Palladio Cost modelling approach.
Note that if a single component can have multiple levels of security, it needs to
8 Yasaweerasinghelage et al.
be modelled as multiple alternative components with different cost properties.
Similarly, when introducing additional components such as secure load balancers
and secure bridging interfaces, base costs and operating costs need to be specified
accordingly. There will also be an overhead for operation cost, because some
secure components may have higher resource utilisation.
User AP
Taint Access Point
Fig. 5. Taint Graph
Security Modelling - Modelling Taint Properties. We extended PCM
to define taint properties of the system. These properties are then used in the
optimisation algorithm. First, the extension retrieves the candidate system ar-
chitecture and converts to a taint graph as shown in Fig. 5.
In the proposed method, each software component can be taint safe or taint
unsafe. Assigning this state to a component, based on whether it is secure or
not, is a decision for the model designer, as discussed further in Section 6. Taint
safe components act as a taint barrier preventing taint propagation from that
point onwards. In this study, our cost models assume that taint safe components
cost more than their taint unsafe counterparts.
From an initial taint setting, we analyse the graph by graph search, spreading
taint except through taint safe components. The search includes cyclic dependen-
cies which might spread taint over multiple passes. The results about whether
security critical components become tainted are provided to the optimisation
engine (see Section 4.4).
When modelling the N1Analytics architecture, we represent each compo-
nent twice, with secure and non-secure alternatives, each with a different cost.
Our not-unrealistic assumption is that a secure component is ten times more
expensive than its non-secure version. Additionally, to explore the impact of
security-specific design patterns, we define two optional secure bridge compo-
nents in front of the parser and the data access component. Our experiments are
executed with and without these secure bridging components.
4.3 Additional Design Options
We modelled additional architectural design alternatives related to data prop-
agation of the system and basic security policies in Design Decision Diagrams.
These define the exploration space for architecture optimisation.
Optimising architectures for performance, cost, and security 9
In this paper, we include design options directly related to the security prop-
erties. The design options model their impact on the overall performance, cost,
and security of the analysed architecture. These design options are used along-
side other general architecture design options.
Non-secure component
Secure component
Fig. 6. Design option - taint blockers/ secure components
Taint blockers/ Secure components. Developing a secure component is
significantly more expensive than developing a component using a standard de-
velopment process. To be cost-optimal, only a limited number of components
can be secure.
As illustrated in Fig. 6, a component can be made taint safe to act as a
taint barrier protecting critical components and thus ensuring system security.
A secure component may have higher resource utilisation compared to less-secure
components due to validity checks, or encryption, and this is also reflected in
the performance models.
Non-secure component
Secure component
Fig. 7. Design option - secure bridging interfaces
Secure bridging interfaces. There is a significant cost of securing components
if those components are large. One design strategy to prevent taint propagation
is to implement secure bridging interfaces in-between components, as shown
in Fig. 7. A typical bridging interface component is small compared to major
functional components because it focuses on enforcing key security properties.
Being smaller, their development cost can be significantly lower. On the other
hand, introducing a bridging interface component adds new fundamental cost
for developing the component, increases resource utilisation, and may act as a
performance bottleneck.
Non-secure component
Secure component
Fig. 8. Design option - secure component access interfaces and secure load balancers
10 Yasaweerasinghelage et al.
Secure component access interfaces and secure load balancers. Simi-
lar to the secure bridging interface components, a design strategy might be to
introduce secure common interfaces/load balancers, or to bring existing com-
mon interfaces/ load balancers to a higher security level (see Fig. 8). Generally,
these components are smaller than major functional components, and so have
significantly lower development cost. However, these components also can be bot-
tlenecks to the system and incur additional base development cost. In addition,
as load balancer interfaces can be concurrently accessed by multiple components
with different resource utilisation, we have to consider such interactions when
optimising the system under different workloads.
4.4 Model Optimisation
We started the optimisation with the architecture shown in Fig. 4. Even though
the proposed approach can handle multiple components defined as taint starting
points or security critical systems, for the simplicity of illustration we define the
external access component as the taint starting point and the database com-
ponent as the only security-critical component. In the initial architecture, all
components are non-secure.
In the Design Decision Model, we allow every component except access points
and databases to be made taint safe or taint unsafe. Additionally, we defined op-
tional trusted bridge components before the parser and computation controller.
Access points, databases, and computation components should only be allocated
to the DMZ server, database server, and computation server respectively. Other
components can be allocated to any server.
We modelled the example system using Palladio Workbench version 4.0 using
SimuCom Bench for performance analysis with Sensor Framework as the persis-
tence framework. For design space exploration we used PerOpteryx version 4.0
with slight modifications for accommodating taint analysis when optimising. We
executed the optimisation on a machine with a 2.7 GHz Intel Core i5 CPU and
8 GB main memory. It took approximately 4 hours to run 500 iterations of the
4.5 Results
The selection of optimal components for a system depends on its requirements.
Here we assume the reasonable goal is the lowest-cost secure architecture that
achieves a response time above a given threshold.
Fig. 9 plots the identified candidate architectures as a distribution of response
time and cost. The red dot indicates the initial architecture configuration (i.e.
Fig. 4) fed into the system. Secure candidates are shown as blue diagonal crosses,
and non-secure candidates are shown with an orange plus. As can be seen, the
genetic algorithm generated fewer non-secure candidates than secure candidates.
Importantly, the results show that when the architecture is secure the system
tends to be more expensive and have inferior performance. In other words, if
security is ignored when picking a candidate architecture, one would likely pick a
Optimising architectures for performance, cost, and security 11
010000 20000 30000 40000 50000
Cost.( si m ula ti on.unit s)
Secure Not-secure
Fig. 9. Response time and cost of candidate architectures generated by PerOpteryx.
ComputationServerAppServer DataServer
External Node
User AP
Data Acess
External requests
cont. processing rate = 4.1 cont. processing rate = 4.2 cont. processing rate = 3.7
Fig. 10. Secure candidate architecture with low cost where the simulated cost is 2,778
units. Simulated response time of this architecture is 33.9 units.
User AP
External Node
Data Acess
cont. processing rate = 3.9
cont. processing rate = 10.9
cont. processing rate = 8.6
cont. processing rate = 7.7
Fig. 11. Secure candidate architecture with low simulated response time of 13.4 units
where simulated cost is 46,692 units.
12 Yasaweerasinghelage et al.
User AP
External Node
Data Acess
cont. processing rate = 3.5
cont. processing rate = 5.6
cont. processing rate = 4.8
Fig. 12. Secure intermediate point where a bridge component has been introduced.
Cost is 2,832 units and response time is 32.1 units.
DMZserver ComputationServer
User AP
External Node
Data Acess
cont. processing rate = 1.7 cont. processing rate = 3.2
cont. processing rate = 4.6
Fig. 13. Generated non-secure architecture. Simulated cost is low as 1,580 and response
time is 37.8 units. The system is non-secure despite one component being secure.
non-secure architecture. However, there are secure alternatives with just slightly
inferior cost and performance.
For some concrete examples, Fig. 10 shows the cheapest secure architecture
that costs 2,778 units but has 33.9 units response time. Fig. 11 illustrates the
best performing secure architecture identified, which has a response time of 13.4
units but costs of 46,692 units. Fig. 13 shows a non-secure architecture which
has cost low as 1,580 while response time is 37.8. From these examples, it is
evident that this method is capable of generating wide range of feasible candidate
architectures based on given design options. This is true for all the candidates.
Identifying vastly different architectures with similar performance, cost and
security can be beneficial in some cases. The difference between those archi-
tectures can be measured by calculating the edit distance between two Palla-
dio instances by aggregating the weighted difference of each design option. We
assigned a lower weight for differences in the resource environment and higher
weight for structural changes to identify architectures with vastly different struc-
tural changes. Fig. 10 and Fig. 12 show a pair of such alternative architectures
we identified by comparing distance between alternatives, i.e., structurally quite
different but with similar performance and cost, and both secure.
5 Related Work
Here we compare our work to related security modelling and analysis approaches.
Optimising architectures for performance, cost, and security 13
Design space exploration for security. Eunsuk Kang [10] identifies the
importance of design space exploration for security and outlines key elements of
a framework intended to support it. The main focus of his work is on low-level
system design and configuration, which is not directly applicable to architecture
level design exploration.
Security Modelling using Palladio Component Model. Busch et al. [6]
provide a Palladio extension to predict the mean time to the next security inci-
dent. Their methodology is to model what to protect (e.g., data of a database),
different ways to access the protected data (e.g., hacking the fronted and then
hacking the non-public database), attacker’s experience, available knowledge
about the system, and the quality of the components in the system. The model
can then predict the mean time to the next security incident.
Busch et al.’s approach facilitates security comparison of different architec-
tures and can be used to identify secure architectures. The main limitation is
the difficulty of identifying specific model parameters such as the experience of
an attacker or quality of a component. It is also complicated to model insider at-
tacks. Nonetheless, the approach defines a metric for system security that might
be able to be incorporated into the general approach proposed in this paper.
Quantifying Security. Sharma et al. [20] propose to use Discrete-Time Markov
Chains (DTMCs) to model software architecture. This is a hierarchical model
that captures quality attributes of components, including security. They quan-
tify security through a model that represents the probability of exposing the
vulnerability of a component in a single execution and its effect on system se-
curity. This model considers how often a certain component is accessed, which
is ignored in our approach based on the assumption that an attacker accesses a
component as often as needed. Sharma et al. [20] designed the model to con-
sider the system as broken if at least one component is successfully attacked.
Yet, as the systems we consider are typically deployed on several machines, a
broken component does not mean that the whole system is compromised. Hence,
we designed our approach to consider the control flow of a system as could be
followed by an attacker.
Madan et al. [15] propose a Semi-Markov process-based model to quantify se-
curity for intrusion-tolerant systems. This model is based on two state-transition
models describing how the system behaves under attack. Their scope is Denial-
of-Service (DoS) and attacks to compromise the system. The objective of the
models is to calculate the Mean Time To Security Failure, to quantify the secu-
rity of the system. In contrast to this model, our approach can assess the security
of component-based architectures and is not restricted to monolithic systems.
SECOMO. SECOMO (Security Cost Model) [14] is a cost modelling tech-
nique associated with a framework for risk management in telecommunications.
It estimates the effort required to conduct a risk management project in a net-
worked environment. This estimation forms a basis for other task estimations
14 Yasaweerasinghelage et al.
such as the cost, human resources and duration of the project. The estimations
are calculated using network size and parameters called scale factors and effort
multipliers, which combined together can provide a measure for the security task
6 Discussion and Future Work
Unlike performance and cost, security is not easily quantifiable. Although se-
curity must be considered when making architecture design decisions, the com-
plicated nature of security makes it difficult to follow traditional automated
design optimisation practices. In this paper, we demonstrated that, instead of
directly modelling the security of architecture, it is possible to perform architec-
ture optimisation using security analysis techniques in conjunction with other
quantifiable system properties (cost, performance). We used taint analysis as an
example architecture security analysis technique to demonstrate the proposed
Based on system security requirements and a domain of operation, we expect
it would be possible to use alternative security analysis techniques such as those
discussed in Section 5 in place of taint analysis. By using alternative security
analysis techniques, users may better identify security vulnerabilities relevant
to their domain. We plan to extend this work by developing a wider range of
security analysis techniques to be used along with Palladio component model,
covering different aspects of security analysis.
In an architectural model, secure components may have higher cost, because
of the time and resources required to secure and provide assurance for that com-
ponent. This may include formal security evaluation techniques such as Evalua-
tion Assurance Level (EAL). These assumptions of increased cost are reasonable,
but could be refined or tailored in specific industries or organisations if empiri-
cal cost data is available. The security of a component can also depend on the
domain. For example, a component might be sufficiently secure for a small-scale
software system with no significant security threats, but be non-secure for a
highly security-critical system in a hostile environment.
PerOpteryx performs a heuristic search on the design space. So it is not
guaranteed to find the optimal or simplest viable architecture. Different initial
architectures may converge to different sub-optimal Pareto candidates. The sys-
tem also does not find a single optimal architecture, but instead defines a range
of optimal alternatives on the Pareto frontier. It is the architect’s responsibility
to choose the final architecture. The architectures discussed here are for illus-
tration purposes only. In real-world scenarios, all the relevant components need
to be modelled with higher detail in order to get more accurate results.
Taint analysis technique we chose for the evaluation of the proposed approach
outputs a binary value for the security. In the real world, architects may want to
use continuous values such as mean time for an attack (see Section 5). In such
cases, they can apply the same principles we propose and optimise the system for
Optimising architectures for performance, cost, and security 15
multi-objectives considering security as another dimension because PerOpteryx
inherently supports multi-objective optimisations.
7 Conclusion
This paper proposes a new method that incorporates security analysis tech-
niques, in addition to cost and performance (latency), when automatically ex-
ploring and optimising system architecture designs. We demonstrate our ap-
proach using taint analysis, a basic architecture security analysis technique where
secure components stopped propagation of taint from attackers to security-
critical components, as the basis for security analysis. We prototyped the ap-
proach by extending the Palladio Component model and PerOpteryx systems.
The extensions include support for our security modelling and analysis. We re-
ported on the experiment and demonstrate the feasibility of using the approach,
illustrating contrasting examples of generated secure system architectures on the
cost/performance Pareto frontier.
The evaluation was performed on an industrial example of a secure system
architecture for a privacy-preserving computing system. The case study high-
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