Language Support for Modular Autonomic Managers in Reconfigurable Software Components

Alvares, Frederico; Delaval, Gwenaël; Rutten, Éric; Seinturier, Lionel

doi:10.1109/icac.2017.48

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Perspectives are in several directions, amongst which coordination of multiple autonomic loops, switching controllers (integrating into the DSL schemes explored in another context [21]) ; modularity and hierarchical loops, both for re-use and to managed compilation complexity, and considering more advanced discrete control features like logico-numeric properties [22], [23] ; and integrating the DSL for Autonomic Managers into a global design process for FPGA-based applications.…”

Section: Discussionmentioning

confidence: 99%

A Domain-Specific Language for Autonomic Managers in FPGA Reconfigurable Architectures

Gueye

Delaval

Rutten

et al. 2018

2018 IEEE International Conference on Autonomic Computing (ICAC)

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Field Programmable Gate Array (FPGA) architectures are suitable hardware platforms for systems that need high performance and flexibility, because they support dynamic partial reconfiguration (DPR) to implement adaptive hardware algorithms e.g., for performance or energy efficiency. They are used for example in embedded systems such as UAV, e.g. for video processing. It is a challenge to design Autonomic Managers for such highly dynamic systems, taking into account the combinatorial design space of configurations and criteria and policies to decide on whether to reconfigure, and what next configuration to choose. In this paper, we propose a Domain Specific Language (DSL) called Ctrl-DPR, allowing designers to easily generate Autonomic Managers. They can describe their system and their management strategies, in terms of the entities composing the system : tasks, versions, applications, ressources, policies. The DSL relies on a behavioural modelling of these entities, targeted at the design of autonomic managers to control the reconfigurations in such a way as to enforce given policies and strategies. The models we use involve automata to describe the state space of configurations, and the transitions representing reconfigurations; they also involve discrete control techniques exploiting such models in order to obtain a correct runtime manager. These model-based control techniques are embedded in a compiler, connected to a reactive language and discrete controller synthesis tool, which enables to generate a C implementation of the controller enforcing the management strategies. We apply our DSL for the management of a video application on a UAV.

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Section: Discussionmentioning

confidence: 99%

A Domain-Specific Language for Autonomic Managers in FPGA Reconfigurable Architectures

Gueye

Delaval

Rutten

et al. 2018

2018 IEEE International Conference on Autonomic Computing (ICAC)

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“…Other application domains included clonal plasticity [195], smart travels [138], agriculture [38], UML [74], system on chip [225], MAPE-K [112], traffic management [156], holonic systems [83], and managing support of recoonfigurable software components [3]. Tables 16 and 17 characterize this time period in terms of the prevalent paper categories and application domains, underlining the trend towards IoT and IaaS in recent years.…”

Section: -2020 -The Last Decade -Second Halfmentioning

confidence: 99%

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

Wong¹,

Wagner²

2021

Preprint

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Championed by IBM's vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system -a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals despite changes in its environment. Despite a number of literature reviews on specific aspects of SASs ranging from their requirements to quality attributes, we lack a systematic understanding of the current state of the art. This paper contributes a systematic literature review into self-adaptive systems using the dblp computer science bibliography as a database. We filtered the records systematically in successive steps to arrive at 293 relevant papers. Each paper was critically analyzed and categorized into an attribute matrix. This matrix consisted of five categories, with each category having multiple attributes. The attributes of each paper, along with the summary of its contents formed the basis of the literature review that spanned 30 years . We characterize the maturation process of the research area from theoretical papers over practical implementations to more holistic and generic approaches, frameworks, and exemplars, applied to areas such as networking, web services, and robotics, with much of the recent work focusing on IoT and IaaS.

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“…Optional web contents on servers are to be showed selectively to users to save resource usage [10]. Componentsbased applications deactivate optional components to manage resource utilization [11]. In containerized clouds, each service is implemented as a container, and the optional containers can be activated/deactivated based on system status [12].…”

Section: Related Workmentioning

confidence: 99%

Learning - based Adaptation Framework for Elastic Software Systems

Sun¹,

Cai²,

Loparo³

2019

International Conferences on Software Engineering and Knowledge Engineering

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Adaptation is a concern for elastic software systems. Conventional methods like Brownout try to deactivate optional computation per request after decoupling the software into different components, to lower the workload when peaks occur. However, resource-intensive components are not always easy to isolate, and some software systems are even not separable. In this paper, we propose a new paradigm that provides each core and mandatory component a corresponding alternative component, with similar function but lower resource consumption, and use reinforcement learning in a feedback loop control for the selfadaptation process. We modified the widely used benchmark RU-BiS to make it weakly coupled and add alternative components. Experiments show that our framework dramatically improves both the efficiency and effectiveness of self-adaptation.

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Language Support for Modular Autonomic Managers in Reconfigurable Software Components

Cited by 5 publications

References 31 publications

A Domain-Specific Language for Autonomic Managers in FPGA Reconfigurable Architectures

A Domain-Specific Language for Autonomic Managers in FPGA Reconfigurable Architectures

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

Learning - based Adaptation Framework for Elastic Software Systems

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