Towards Dependable Emergent Ensembles of Components: The DEECo Component Model

Keznikl, Jaroslav; Bureš, Tomáš; Plasil, Frantiek; Kit, Michał

doi:10.1109/wicsa-ecsa.212.39

Cited by 19 publications

(5 citation statements)

References 10 publications

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“…We implemented the self-adaptation method of IRM-SA as a plugin (publicly available [42]) into the jDEECo framework [44]. This framework is a realization of the DEECo component model [15,48] . For developing components and ensembles, jDEECo provides an internal Java DSL and allows their distributed execution.…”

Section: Prototype Implementationmentioning

confidence: 99%

Self-adaptation in software-intensive cyber–physical systems: From system goals to architecture configurations

Gerostathopoulos

Bureš

Hnětynka

et al. 2016

Journal of Systems and Software

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International audienceDesign of self-adaptive software-intensive Cyber-Physical Systems (siCPS) operating in dynamic environments is a significant challenge when a sufficient level of dependability is required. This stems partly from the fact that the concerns of self-adaptivity and dependability are to an extent contradictory. In this paper, we introduce IRM-SA (Invariant Refinement Method for Self-Adaptation) – a design method and associated formally grounded model targeting siCPS – that addresses self-adaptivity and supports dependability by providing traceability between system requirements, distinct situations in the environment, and predefined configurations of system architecture. Additionally, IRM-SA allows for architecture self-adaptation at runtime and integrates the mechanism of predictive monitoring that deals with operational uncertainty. As a proof of concept, it was implemented in DEECo, a component framework that is based on dynamic ensembles of components. Furthermore, its feasibility was evaluated in experimental settings assuming decentralized system operation

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Section: Prototype Implementationmentioning

confidence: 99%

Self-adaptation in software-intensive cyber–physical systems: From system goals to architecture configurations

Gerostathopoulos

Bureš

Hnětynka

et al. 2016

Journal of Systems and Software

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“…When structuring the part of the system which is heavily influenced by the dynamicity in the physical world, i.e., lowpower to resource-poor links (dotted links in Figure 3, left part of the architecture in Figure 4), it is beneficial to employ component models that specifically target environments with evolving physical substratum, such as DEECo (Dependable Emergent Ensembles of Components) component model [4,10].…”

Section: Deeco Modelmentioning

confidence: 99%

An Architecture-Based Approach for Compute-Intensive Pervasive Systems in Dynamic Environments

Ali

Gerostathopoulos

Gonzalez-Herrera

et al. 2014

Proceedings of the 2nd International Workshop on Hot Topics in Cloud Service Scalability

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Distributed systems have continued to evolve and we note two important trends: the dramatically increasing level of dynamism in contemporary distributed systems and the convergence of mobile computing with cloud computing. The end result is that it is very difficult to achieve the required level of scalability and dependability in a systematic way when considering pervasive systems that are software-and compute-intensive and whose functionality is typically augmented by static cloud infrastructure resources. This work discusses relevant challenges and requirements for integrating cloud computing with pervasive systems operating in dynamic environments. We present a set of requirements using a holistic case study and describe a reference architecture to address these requirements.

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“…Moreover, existing approaches fail to fully address the adaptability of cloud applications in heterogeneous resource environments spanning from mobile terminals to cloud infrastructure nodes. To this end, model-driven service design and development could be used to enable the adaptation of cloud applications [3,12,9,2]. FFinally, another important research challenge relates to the portability of cloud applications, i.e., creating applications such that they can be deployed on multiple target cloud platforms with as few changes as possible [10].…”

Section: Relate Research Areasmentioning

confidence: 99%

Relate

Kounev¹,

Rizou

Zschaler

et al. 2013

Proceedings of the 2013 International Workshop on Hot Topics in Cloud Services

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The RELATE Initial Training Network (ITN), funded by the EU in the Marie Curie Actions programme, is a multidisciplinary training network of European academic and industrial partners working together to train academic researchers and next generation experts in the area of engineering and provisioning of service-based Cloud applications. In this paper, we present the up-to-date goals and strategy of the RE-LATE project and give an overview of the ongoing research activities within the training network.

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Towards Dependable Emergent Ensembles of Components: The DEECo Component Model

Cited by 19 publications

References 10 publications

Self-adaptation in software-intensive cyber–physical systems: From system goals to architecture configurations

Self-adaptation in software-intensive cyber–physical systems: From system goals to architecture configurations

An Architecture-Based Approach for Compute-Intensive Pervasive Systems in Dynamic Environments

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