MAPE-K as a service-oriented architecture

Vizcarrondo, Juan; Aguilar, José; Expósito, Ernesto; Subias, Albert Corominas

doi:10.1109/tla.2017.7932705

Cited by 37 publications

(18 citation statements)

References 10 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is based on the Autonomic Computing paradigm proposed by IBM, that monitors, analyzes, plans and executes using a knowledge base (MAPE-K) [29]. The integration of tasks in cycles closes the loop, enabling an autonomic problemsolving.…”

Section: Figure 1 Autonomous Cycle Of Data Analysis Tasksmentioning

confidence: 99%

Autonomic Management of a Building’s Multi-HVAC System Start-Up

et al. 2021

Self Cite

View full text Add to dashboard Cite

Most of the studies about control, automation, optimization and supervision of building HVAC systems are about the steady-state regime, i.e. when the equipment is already working on their setpoints. In this work, it is defined an approach for multi-HVAC systems to reach the setpoint at the startup, so that other steady statebased strategies can smoothly come into operation to control and supervise their operations. The proposed approach for the management of the transient regime for multi-HVAC systems is based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, it is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if it was not the case, another for reconfiguring the operational mode of the multi-HVAC system to redirect it. The proposal is proved in a real context that characterizes one multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is encouraging, since they show how this approach leads the multi-HVAC system to reach the setpoint optimizing contradictory objectives, such as to attain the desired comfort, or reduce the energy costs, among others.

show abstract

Section: Figure 1 Autonomous Cycle Of Data Analysis Tasksmentioning

confidence: 99%

Autonomic Management of a Building’s Multi-HVAC System Start-Up

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…DATs exploit the data collected from the system to build knowledge models that describe, optimize and predict its behavior. DATs co-operate among them and interact with the system according to their specific roles [35], [36]:…”

Section: E Autonomic Cycle Of Data Analysismentioning

confidence: 99%

“…The optimization identifies possible operational modes and which one best fits the set of objectives. The proposed architecture is based on the Autonomous Cycle of Data Analysis Tasks (ACODAT) [34], [35] paradigm, that defines a set of Data Analysis Tasks (DATs) [35] that autonomously interact providing intelligent supervision for achieving the pursued strategic goals. Some DATs monitor the selected variables (e.g., energy cost, CO2 emissions) and make decisions that deliver to other DATs; other DATs extract knowledge to predict potential system behaviors; others identify relations between variables; there are DATs that search for the optimal multi-HVAC system operational mode, and others supervise the system performance.…”

mentioning

confidence: 99%

Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings

Aguilar¹,

Garcés-Jiménez²,

Gallego-Salvador³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

This article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the ''Autonomous Cycle of Data Analysis Tasks'' concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems.

show abstract

“…the people, the system's mechanisms involved, or the environmental features. These components define an autonomic cycle for the acoustic management in AmICL, based on the concept of autonomic cycles as services proposed in [32,33]. This autonomic cycle establishes the relationship between the components in order to allow the self-management acoustics in an AmI.…”

Section: • Mas Management Layer (Mmal): This Layer Wasmentioning

confidence: 99%

ReM-AM: Reflective middleware for acoustic management in intelligent environments

Santiago

Aguilar

Chávez

2017

2017 XLIII Latin American Computer Conference (CLEI)

Self Cite

View full text Add to dashboard Cite

Abstract-This paper presents the architecture of a Reflective Middleware for Acoustic Management that will improve the interaction between users and agents in Intelligent Environments, using the Multiagent System paradigm. The middleware manages the acoustic services, from the identification and recognition of the acoustic signals, until the interpretation, processing and analysis of them. In this paper are detailed the architecture bases, the middleware components, and case studies where this Middleware can be used. Acoustic signals and vibrations will generate the connection between agents, but it will also include the sounds that are provided by the users of the Intelligent Environment, making it an interactive and immersive experience.

show abstract

MAPE-K as a service-oriented architecture

Cited by 37 publications

References 10 publications

Autonomic Management of a Building’s Multi-HVAC System Start-Up

Autonomic Management of a Building’s Multi-HVAC System Start-Up

Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings

ReM-AM: Reflective middleware for acoustic management in intelligent environments

Contact Info

Product

Resources

About