Leandro Soares Indrusiak

Michal Witold Przewozniczek, Piotr Dziurzanski, Shuai Zhao et al.

Evolutionary methods are effective tools for obtaining high-quality results when solving hard practical problems. Linkage learning may increase their effectiveness. One of the state-of-the-art methods that employ linkage learning is the Parameter-less Population Pyramid (P3). P3 is dedicated to solving single-objective problems in discrete domains. Recent research shows that P3 is highly competitive when addressing problems with so-called overlapping blocks, which are typical for practical problems. In this paper, we consider a multi-objective industrial process planning problem that arises from practice and is NP-hard. To handle it, we propose a multi-objective version of P3. The extensive research shows that our proposition outperforms the competing methods for the considered practical problem and typical multi-objective benchmarks.

Leandro Soares Indrusiak, Robert I. Davis, Piotr Dziurzanski

The design space of networked embedded systems is very large, posing challenges to the optimisation of such platforms when it comes to support applications with real-time guarantees. Recent research has shown that a number of inter-related optimisation problems have a critical influence over the schedulability of a system, i.e. whether all its application components can execute and communicate by their respective deadlines. Examples of such optimization problems include task allocation and scheduling, communication routing and arbitration, memory allocation, and voltage and frequency scaling. In this paper, we advocate the use of evolutionary approaches to address such optimization problems, aiming to evolve individuals of increased fitness over multiple generations of potential solutions. We refer to plentiful evidence that existing real-time schedulability tests can be used effectively to guide evolutionary optimisation, either by themselves or in combination with other metrics such as energy dissipation or hardware overheads. We then push that concept one step further and consider the possibility of using evolutionary techniques to evolve the schedulability tests themselves, aiming to support the verification and optimisation of systems which are too complex for state-of-the-art (manual) derivation of schedulability tests.

Leandro Soares Indrusiak, James Harbin, Martha Johanna Sepulveda

Security can be seen as an optimisation objective in NoC resource management, and as such poses trade-offs against other objectives such as real-time schedulability. In this paper, we show how to increase NoC resilience against a concrete type of security attack, named side-channel attack, which exploit the correlation between specific non-functional properties (such as packet latencies and routes, in the case of NoCs) to infer the functional behaviour of secure applications. For instance, the transmission of a packet over a given link of the NoC may hint on a cache miss, which can be used by an attacker to guess specific parts of a secret cryptographic key, effectively weakening it. We therefore propose packet route randomisation as a mechanism to increase NoC resilience against side-channel attacks, focusing specifically on the potential impact of such an approach upon hard real-time systems, where schedulability is a vital design requirement. Using an evolutionary optimisation approach, we show how to effectively apply route randomisation in such a way that it can increase NoC security while controlling its impact on hard real-time performance guarantees. Extensive experimental evidence based on analytical and simulation models supports our findings.