Self-adaptation of Cyber-Physical Systems
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Self-adaptation of Cyber-Physical Systems C. Sau 1 , T. Fanni 1 , L. Raffo 1 , F. Palumbo 2 1 Università degli Studi di Cagliari, 2 Università degli Studi di Sassari www.cerbero-h2020.eu CERBERO IN A NUTSHELL SIE 2018 - Naples, 20 – 22 June 2018 Michael Masin, Francesca Palumbo, et al. Cross-layer design of reconfigurable cyber-physical systems DATE 2017 - Design, Automation & Test in Europe Conference Cyber Physical Systems (CPS) are embedded computational collaborating devices, capable of controlling physical elements and responding to humans. CERBERO aims at developing a CPS design environment based on two pillars: a cross-layer model based approach to describe, optimize, and analyze the system and all its different views concurrently; and an advanced adaptivity support based on a multi-layer autonomous engine. CERBERO effectiveness is assessed in challenging and diverse scenarios: planetary explorations, ocean monitoring and a smart travelling for electric vehicle. Self Healing for Planetary Explorations This use case focuses on a single unique embedded CPS. CERBERO technologies are going to be adopted to define self-healing and self-adaptive processing systems capable of operating in such a critical environment. Ocean Monitoring Smart video-sensing unmanned vehicles with immersive environmental monitoring capabilities. CERBERO will define algorithms for data analysis and information fusion to enable smart (self-) adaptation strategies to address rapidly changing environment and system conditions. Smart Travelling for Electric Vehicle Highly networked. Heterogeneous concurrent subsystems: Electric Vehicle, Person (partially observable personal agenda), Smart Mobility (parking, charge points, etc.), etc. CERBERO will support adaptability, plus modelling and managing the distributed communication layers. CPS HW-ORIENTED Copses SW-ORIENTED CONSORTIUM & PROJECT OVERVIEW Self - Adaptivity in CERBERO H2020 • MECA: decision support for user of a CPS • VT: quantitative requirements verification to provide correct-by-construction design. • AOW: multi-objective multi-view cross-level optimization. • DynAA: system analysis and design tool, combines features from system and network simulators. • PREESM: dataflow to core mapping with static optimization capabilities (i.e. latency and load balancing). • PAPI: runtime monitoring. • SPIDER: runtime manager. • JADE: Just-in-time compilation. • MDC, ARTICO 3 : hardware acceleration and support for adaptivity. Adaptatio n monitors KPI models Adaptation manager Adaptation engine Adaptation fabric PAPI-compatible extension of SPIDER for runtime management of hw-sw resources. Monitoring Counters Monitoring Counters Monitoring Counters ACCELERATOR LOGIC Monitoring Counters A G C F GAMMA D E C BETA A B C ALPHA Partitioning HW Generation + MDC - N:1 PAPI + SD-SoC - N:N PAPI Monitoring ACCELERATOR LOGIC Monitoring Counters SPIDER “PAPI-fication of the hw layer” for continuous monitoring of accelerators, from standard ARTICO3 slots to Coarse-Grained Reconfigurable (CGR) ones. Support @Design - Time Support @Run - Time PREESM - N:N In CERBERO, tackling the development of self-adaptive CPS and CPS of Systems, we provided a generic definition of a self-adaptive system: the adaptation loop. CERBERO adaptivity support is based on a multi-layer heterogeneous (HW-SW) autonomous engine Extremely flexible behaviours: surfing among working points that can be user commanded or self-determined (i.e. low battery level). To handle partitioning of applications over heterogeneous adaptive system PREESM is going to be used. PREESM was originally meant to handle actors partitioning over homogenous multi-core infrastructure, but we are now integrating within it MDC-compliant and ARTICO accelerators models. The HW accelerators are going to be instrumented with HW Performance Monitoring Counters (handled with PAPI) to monitor their internal status. PAPI This work has received funding from the EU Commission’s H2020 Programme under grant agreement No 732105
Transcript of Self-adaptation of Cyber-Physical Systems
Self-adaptation of Cyber-Physical Systems
C. Sau1, T. Fanni1, L. Raffo1, F. Palumbo2
1Università degli Studi di Cagliari, 2Università degli Studi di Sassari www.cerbero-h2020.eu
CERBERO IN A NUTSHELL
SIE 2018 - Naples, 20 – 22 June 2018
Michael Masin, Francesca Palumbo, et al.Cross-layer design of reconfigurable
cyber-physical systemsDATE 2017 - Design, Automation & Test in
Europe Conference
Cyber Physical Systems (CPS) are embedded computational collaborating devices, capable of controlling physical elements and responding to humans. CERBERO aims at developing a CPS designenvironment based on two pillars: a cross-layer model based approach to describe, optimize, and analyze the system and all its different views concurrently; and an advanced adaptivity supportbased on a multi-layer autonomous engine.CERBERO effectiveness is assessed in challenging and diverse scenarios: planetary explorations, ocean monitoring and a smart travelling for electric vehicle.
Self Healing for Planetary Explorations This use case focuses on a singleunique embedded CPS.CERBERO technologies are going tobe adopted to define self-healingand self-adaptive processingsystems capable of operating insuch a critical environment.
Ocean Monitoring Smart video-sensing unmanned vehicleswith immersive environmental monitoringcapabilities.CERBERO will define algorithms for dataanalysis and information fusion to enablesmart (self-) adaptation strategies toaddress rapidly changing environmentand system conditions.
Smart Travelling for Electric VehicleHighly networked. Heterogeneousconcurrent subsystems: ElectricVehicle, Person (partially observablepersonal agenda), Smart Mobility(parking, charge points, etc.), etc.CERBERO will support adaptability,plus modelling and managing thedistributed communication layers.
CPS HW-ORIENTED Copses SW-ORIENTED
CONSORTIUM & PROJECT OVERVIEW
Self-Adaptivity in CERBERO H2020
• MECA: decision support for user of a CPS• VT: quantitative requirements verification to
provide correct-by-construction design.• AOW: multi-objective multi-view cross-level
optimization.• DynAA: system analysis and design tool,
combines features from system and networksimulators.
• PREESM: dataflow to core mapping with staticoptimization capabilities (i.e. latency and loadbalancing).
• PAPI: runtime monitoring.• SPIDER: runtime manager.• JADE: Just-in-time compilation.• MDC, ARTICO3: hardware acceleration and
support for adaptivity.
Adaptation monitors
KPI models
Adaptation manager
Adaptation engine
Adaptation fabric
PAPI-compatible extension of SPIDER for runtime management of hw-sw resources.
Monitoring Counters
Monitoring Counters
Monitoring Counters
ACCELERATOR LOGIC
MonitoringCounters
AG C
F
GAMMA
D E CBETA
A B CALPHA
Partitioning HW Generation
+MDC - N:1 PAPI
+SD-SoC - N:N PAPI
Monitoring
ACCELERATOR LOGIC
MonitoringCounters
SPIDER
“PAPI-fication of the hw layer”for continuous monitoring ofaccelerators, from standardARTICO3 slots to Coarse-GrainedReconfigurable (CGR) ones.
Support @Design-Time
Support @Run-Time
PREESM - N:N
In CERBERO, tackling thedevelopment of self-adaptiveCPS and CPS of Systems, weprovided a generic definitionof a self-adaptive system:the adaptation loop.
CERBERO adaptivity support is based on a multi-layer heterogeneous (HW-SW) autonomous engine
Extremely flexible behaviours: surfing among working points that can be user commanded or self-determined (i.e. low batterylevel).
To handle partitioning of applications over heterogeneous adaptive system PREESM is going to be used. PREESM was originally meant to handle actors partitioning over homogenous multi-core infrastructure, but we are now integrating within it MDC-compliant and ARTICOaccelerators models. The HW accelerators are going to be instrumented with HW Performance Monitoring Counters (handled with PAPI) to monitor their internal status.
PAPI
This work has received funding from the EU Commission’s H2020 Programme under grant agreement No 732105
