An Ontology-based Approach to Performance Monitoring of MUSCLE-bound Multi-scale Applications

Funika, Włodzimierz; Janczykowski, Michal; Jopek, Konrad; Grzegorczyk, Maciej

doi:10.1016/j.procs.2013.05.278

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…In the domain of device monitoring, Funika et al present an ontology-based approach to perform the monitoring of resource usage in multi-scale platforms [53]. Connecting the domains of device monitoring and IoT, Ryabinin et al demonstrate an ontology-based approach to manage and monitor resource-constrained Edge Computing devices [54].…”

Section: Monitoring Ontologiesmentioning

confidence: 99%

Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDE

De Brouwer,

De Turck,

Ongenae

2024

J Netw Syst Manage

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In the Internet of Things (IoT), semantic IoT platforms are often used to solve the challenges associated with the real-time integration of heterogeneous IoT sensor data, domain knowledge and context information. Existing platforms mostly have a static distribution and configuration of queries deployed on the platform's stream processing components. In contrast, the environmental context in which queries are deployed has a very dynamic nature: real-world set-ups involve varying tasks, device resource usage, networking conditions, etc. To solve this mismatch, this paper presents DIVIDE, an IoT platform component built on Semantic Web technologies. DIVIDE has a generic design containing multiple subcomponents that monitor the environment across a cascading architecture. By monitoring the use case context, DIVIDE adaptively derives the appropriate stream processing queries in a context-aware way. Using a Local Monitor deployed on edge devices, situational context parameters are measured and aggregated. The Meta Model allows modeling these measurements, and meta-information about devices and deployed stream processing queries. Through the definition of application-specific Global Monitor queries that are continuously evaluated centrally on the Meta Model, end users can dynamically configure how the situational context should influence the window parameter configuration and distribution of queries in the network. The paper evaluates a first implementation of DIVIDE on a homecare monitoring use case. The results show how DIVIDE can successfully adapt to varying device and networking conditions, taking into account the use case requirements. This way, DIVIDE allows better balancing use case specific trade-offs and achieves more efficient stream processing.

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Section: Monitoring Ontologiesmentioning

confidence: 99%

Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDE

De Brouwer,

De Turck,

Ongenae

2024

J Netw Syst Manage

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“…Brandis et al (2014) introduce a structure for cloud governance with a particular focus on the semantic aspects. Funika et al (2013) introduce an ontologybased approach for building a monitoring tool for multi-scale applications implemented in the Cloud. Chang et al (2012) suggest an intelligent agent-oriented middleware that catches the user's contextual information and propose services in a mobile Cloud.…”

Section: Existing CM and Cc Domain Modelsmentioning

confidence: 99%

Ontology for cloud manufacturing based Product Lifecycle Management

Talhi

Fortineau²,

Huet

et al. 2017

J Intell Manuf

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The manufacturing environment has become increasingly competitive in the past few years, and product development is getting even more complex. The agility of an information system is a way to manage this complexity, and cloud technologies enable the sharing of tools and information in a new way. In particular, in the field of Product Lifecycle Management, a review of the literature demonstrates that there are technical issues that limit the efficient collaboration between the various stakeholders along the product lifecycle. Cloud manufacturing is a new concept that enables the virtualization of manufacturing resources and capabilities and provides them as a service. Therefore it offers new prospects for usage and collaboration in the extended enterprise, and along the product lifecycle. For instance, it helps to manage variations in production demand, by providing a large set of potential manufacturing resources on demand. However, to share the resources within the cloud manufacturing environment, a unification model of the domain information is required, to which any provider and/or user of cloud manufacturing must conform in order to dialog with the other CM stakeholders. This study provides an ontological model of the cloud manufacturing domain in order to support information exchange between the cloud manufacturing resources. The concepts of the proposed ontology are based on a literature review of models of cloud and models of manufacturing. The detailed ontology is then validated using the OntoClean methodology and within its implementation in an industrial scenario.

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Ontology-Based Approach to Scheduling of Jobs Processed by Applications Running in Virtual Environments

Khegai

Zubok

Maiatin

2015

Communications in Computer and Information Science

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An Ontology-based Approach to Performance Monitoring of MUSCLE-bound Multi-scale Applications

Cited by 4 publications

References 8 publications

Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDE

Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDE

Ontology for cloud manufacturing based Product Lifecycle Management

Ontology-Based Approach to Scheduling of Jobs Processed by Applications Running in Virtual Environments

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