ArcheOpterix: An extendable tool for architecture optimization of AADL models

Abstract-State-of-the art constrained multiobjective optimisation methods are often explored and demonstrated with the help of function optimisation problems from these accounts. It is sometimes hard for practitioners to extract good approaches for practical problems. In this paper we apply an evolutionary algorithm to a factual problem with realistic constraints and compare the effects of different operators and constraint handling methods. We observe that in spite of an apparently very insular search space, we consistently obtain the best results when using a repair mechanism, effectively eliminating infeasible solutions. This runs contrary to some recommendations in the optimisation literature which propose penalty functions for search spaces where feasible solutions are sparse.

show abstract

“…The set of all possible solutions is D = {d | d : C → U }. More in-depth description of the problem and its aspects is found in [6], [3], [7].…”

Section: Problem Descriptionmentioning

confidence: 99%

The automotive deployment problem: A practical application for constrained multiobjective evolutionary optimisation

Moser

Mostaghim

2010

IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…The toolkit integrates modeling technologies with performance evaluation tools in order to evaluate the goodness of generated solution according to a cost function. Aleti et al proposed a similar approach in [28]; they presented the ArcheOpterix framework that exploits evolutionary algorithms to derive Pareto optimal component deployment decisions with respect to multiple quality criteria. A multi-objective optimization strategy on top of ArcheOpterix is implemented in [29] to find trade-off between reliability and energy consumption in embedded systems.…”

Section: Related Workmentioning

confidence: 99%

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Guerriero

Ciavotta

Gibilisco

et al. 2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

View full text Add to dashboard Cite

Recently we witnessed a deep transformation in the the design, development and management of modern applications, which have grown in scope and size becoming distributed and service-oriented. A big part in this metamorphosis is played by the Cloud with the availability of almost-infinite resources, high availability and outsourced maintenance. This has led to the emergence of new software development methodologies to effectively deal with this paradigm shift in the field of software engineering. DevOps is one of them, it advocates for a greater level of collaboration and convergence between developers and other IT professionals. Consequently, new tools, purposely designed to ease this process, are required. In this scenario, we present SPACE4Cloud, a DevOps integrated environment for model-driven design-time quality of service assessment and optimization, and runtime capacity allocation of Cloud applications.

show abstract

“…Aleti et al (2009a) adapt Pareto-Ant Colony Optimization (P-ACO) algorithm (Doerner et al, 2004) to solve a multi-objective deployment optimization problem. The performance of P-ACO is compared with a MultiObjective Genetic Algorithm (MOGA) by using the Archeopterix tool platform (Aleti et al, 2009b). The parameters for the optimization problem are memory requirement, communication frequencies, and event sizes for components (tasks), memory capacity, network bandwidth, network delay for hosts (processors).…”

Section: Defined In Sectionmentioning

confidence: 99%

S-IDE: A tool framework for optimizing deployment architecture of High Level Architecture based simulation systems

Çelik

Tekinerdoğan

2013

Journal of Systems and Software

View full text Add to dashboard Cite

One of the important problems in High Level Architecture (HLA) based distributed simulation systems is the allocation of the different simulation modules to the available physical resources. Usually, the deployment of the simulation modules to the physical resources can be done in many different ways, and each deployment alternative will have a different impact on the performance. Although different algorithmic solutions have been provided to optimize the allocation with respect to the performance, the problem has not been explicitly tackled from an architecture design perspective. Moreover, for optimizing the deployment of the simulation system, tool support is largely missing. In this paper we propose a method for automatically deriving deployment alternatives for HLA based distributed simulation systems. The method extends the IEEE Recommended Practice for High Level Architecture Federation Development and Execution Process by providing an approach for optimizing the allocation at the design level. The method is realized by the tool framework, S-IDE (Simulation-IDE) that we have developed to provide an integrated development environment for deriving a feasible deployment alternative based on the simulation system and the available physical resources at the design phase. The method and the tool support have been validated using a case study for the development of a traffic simulation system.

show abstract

ArcheOpterix: An extendable tool for architecture optimization of AADL models

Cited by 139 publications

References 39 publications

The automotive deployment problem: A practical application for constrained multiobjective evolutionary optimisation

The automotive deployment problem: A practical application for constrained multiobjective evolutionary optimisation

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

S-IDE: A tool framework for optimizing deployment architecture of High Level Architecture based simulation systems

Contact Info

Product

Resources

About