This is a presentation by Prof. Anne Elster at the International Workshop on Open Source Supercomputing held in conjunction with the 2017 ISC High Performance Computing Conference.
This is a presentation by Prof. Anne Elster at the International Workshop on Open Source Supercomputing held in conjunction with the 2017 ISC High Performance Computing Conference.
Slides of a talk given at ERTS2008 in Toulouse. Abstract: with the increasing amount of electronics, making best usage of the bandwidth becomes of primary importance in automotive networks. One
solution that is being investigated by car manufacturers is to schedule the messages with offsets, which leads to a desynchronization of the message streams. As it will be shown, this “traffic shaping” strategy is very beneficial in terms of worst-case response times. In this slides, the problem of choosing the best offsets is addressed in the case of Controller Area Network, which is a de-facto standard in the automotive world. Comprehensive experiments shown give insight into the fundamental reasons why offsets are efficient, and demonstrate that offsets actually provide a major performance boost in terms of response times. These experimental results suggest that sound offset strategies may extend the lifespan of CAN further, and may defer the introduction of FlexRay and additional CAN networks.
Timing verification of automotive communication architectures using quantile ...Nicolas Navet
Early stage timing verification on CAN traditionally relies on simulation and schedulability analysis, also known as worst-case response time (WCRT) analysis. Despite recent progresses, the latter technique remains pessimistic especially in complex networking architectures with gateways and heterogeneous communication stacks. Indeed, there are practical cases where no exact WCRT analysis is available, and merely upper bounds on the response times can be derived, on the basis of which unnecessary conservative design choices may be made. Simulation, on the other hand, does not provide any guarantees per se and, in the context of critical networks, should only be used along with an adequate methodology. In this paper, we argue for the use of quantiles of the response time distribution as performance metrics providing an adjustable trade-off between safety and resource usage optimization. We discuss how the exact value of the quantile to consider should be chosen with regard to the criticality of the frames, and illustrate the approach on two typical automotive use-cases.
In the last few years energy efficiency of large scale infrastructures gained a lot of attention, as power consumption became one of the most impacting factors of the operative costs of a data-center and of its Total Cost of Ownership. Power consumption can be observed at different layers of the data-center: from the overall power grid, moving to each rack and arriving to each machine and system. Given the rise of application containers in the cloud computing scenario, it becomes more and more important to measure power consumption also at the application level, where power-aware schedulers and orchestrators can optimize the execution of the workloads not only from a performance perspective, but also considering performance/power trade-offs. DEEP-mon is a novel monitoring tool able to measure power consumption and attribute it for each thread and application container running in the system, without any previous knowledge regarding the characteristics of the application and without any kind of workload instrumentation. DEEP-mon is able to aggregate data for threads, application containers and hosts with a negligible impact on the monitored system and on the running workloads.
Information obtained with DEEP-mon open the way for a wide set of applications exploiting the capabilities offered by the monitoring tool, from power (and hence cost) metering of new software components deployed in the data center, to fine grained power capping and power-aware scheduling and co-location.
In this deck from the Stanford HPC Conference, Peter Dueben from the European Centre for Medium-Range Weather Forecasts (ECMWF) presents: Machine Learning for Weather Forecasts.
"I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will than talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future."
Peter is contributing to the development and optimization of weather and climate models for modern supercomputers. He is focusing on a better understanding of model error and model uncertainty, on the use of reduced numerical precision that is optimised for a given level of model error, on global cloud- resolving simulations with ECMWF's forecast model, and the use of machine learning, and in particular deep learning, to improve the workflow and predictions. Peter has graduated in Physics and wrote his PhD thesis at the Max Planck Institute for Meteorology in Germany. He worked as Postdoc with Tim Palmer at the University of Oxford and has taken up a position as University Research Fellow of the Royal Society at the European Centre for Medium-Range Weather Forecasts (ECMWF) in 2017.
Watch the video: https://youtu.be/ks3fkRj8Iqc
Learn more: https://www.ecmwf.int/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Discrete-event simulation: best practices and implementation details in Pytho...Carlos Natalino da Silva
Discrete-event simulation is one of the most useful techniques to evaluate quickly and effectively the performance of systems. It enables benchmarking proposed strategies against existing ones in a time- and computing-efficient manner. However, there are several aspects that should be considered when designing and implementing your simulation environment. In this tutorial, a number of best practices when designing and implementing event-driven simulations will be discussed. A use case of routing in optical networks will be used as an example. The implementation of the main simulator components using Java and Python will be described.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Modeling Uncertainty For Middleware-based Streaming Power Grid ApplicationsJenny Liu
The power grid is incorporating high throughput sensor devices into power distribution networks. The future power
grid needs to guarantee accuracy and responsiveness of applications that consume data from multiple sensor streams.
The end-to-end performance and overall scalability of cyber-physical energy applications depend on the middleware's ability to handle multi-source sensor data, which exhibits uncertain behavior under highly variable numbers of sensors and middleware topologies. In this paper, we present a parametric approach to model middleware uncertainty and to analyze its eect on distributed power applications. The models encapsulate the entire data
ow paths from sensor devices, through network and middleware components to the power application nodes that utilize sensor data streams. Using the Ptolemy II framework for modeling and simulation, we generate Monte Carlo samples of uncertain parameters that are used to generate parameterized middleware models that are used in end-to-end Discrete-Event(DE) system simulation simulation. The simulation results are further analyzed using regression methods to reveal the parameters that are influential in the limiting middleware behaviour to achieve temporal requirements of the power applications.
Final Year Students Project
Opposite to Sripuram Bus Stop
Back of Rajadeepan Jewellers
Tirunelveli.
Phone:+91 - 8903410319
Mail: finalyearstudentsprojecttvl@gmail.com
web:www.finalyearstudentsproject.in
Edal an energy efficient, delay-aware, and lifetime-balancing data collection...LogicMindtech Nologies
NS2 Projects for M. Tech, NS2 Projects in Vijayanagar, NS2 Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, NS2 IEEE projects in Bangalore, IEEE 2015 NS2 Projects, WSN and MANET Projects, WSN and MANET Projects in Bangalore, WSN and MANET Projects in Vijayangar
Positioning University of California Information Technology for the Future: S...Larry Smarr
05.02.15
Invited Talk
The Vice Chancellor of Research and Chief Information Officer Summit
“Information Technology Enabling Research at the University of California”
Title: Positioning University of California Information Technology for the Future: State, National, and International IT Infrastructure Trends and Directions
Oakland, CA
SERENE 2014 Workshop: Paper "Simulation Testing and Model Checking: A Case St...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 2: Simulation Testing and Model Checking: A Case Study Comparing these Approaches
SERENE 2014 Workshop: Paper "Advanced Modelling, Simulation and Verification ...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 3: Advanced Modelling, Simulation and Verification for Future Traffic Regulation Optimisation
Slides of a talk given at ERTS2008 in Toulouse. Abstract: with the increasing amount of electronics, making best usage of the bandwidth becomes of primary importance in automotive networks. One
solution that is being investigated by car manufacturers is to schedule the messages with offsets, which leads to a desynchronization of the message streams. As it will be shown, this “traffic shaping” strategy is very beneficial in terms of worst-case response times. In this slides, the problem of choosing the best offsets is addressed in the case of Controller Area Network, which is a de-facto standard in the automotive world. Comprehensive experiments shown give insight into the fundamental reasons why offsets are efficient, and demonstrate that offsets actually provide a major performance boost in terms of response times. These experimental results suggest that sound offset strategies may extend the lifespan of CAN further, and may defer the introduction of FlexRay and additional CAN networks.
Timing verification of automotive communication architectures using quantile ...Nicolas Navet
Early stage timing verification on CAN traditionally relies on simulation and schedulability analysis, also known as worst-case response time (WCRT) analysis. Despite recent progresses, the latter technique remains pessimistic especially in complex networking architectures with gateways and heterogeneous communication stacks. Indeed, there are practical cases where no exact WCRT analysis is available, and merely upper bounds on the response times can be derived, on the basis of which unnecessary conservative design choices may be made. Simulation, on the other hand, does not provide any guarantees per se and, in the context of critical networks, should only be used along with an adequate methodology. In this paper, we argue for the use of quantiles of the response time distribution as performance metrics providing an adjustable trade-off between safety and resource usage optimization. We discuss how the exact value of the quantile to consider should be chosen with regard to the criticality of the frames, and illustrate the approach on two typical automotive use-cases.
In the last few years energy efficiency of large scale infrastructures gained a lot of attention, as power consumption became one of the most impacting factors of the operative costs of a data-center and of its Total Cost of Ownership. Power consumption can be observed at different layers of the data-center: from the overall power grid, moving to each rack and arriving to each machine and system. Given the rise of application containers in the cloud computing scenario, it becomes more and more important to measure power consumption also at the application level, where power-aware schedulers and orchestrators can optimize the execution of the workloads not only from a performance perspective, but also considering performance/power trade-offs. DEEP-mon is a novel monitoring tool able to measure power consumption and attribute it for each thread and application container running in the system, without any previous knowledge regarding the characteristics of the application and without any kind of workload instrumentation. DEEP-mon is able to aggregate data for threads, application containers and hosts with a negligible impact on the monitored system and on the running workloads.
Information obtained with DEEP-mon open the way for a wide set of applications exploiting the capabilities offered by the monitoring tool, from power (and hence cost) metering of new software components deployed in the data center, to fine grained power capping and power-aware scheduling and co-location.
In this deck from the Stanford HPC Conference, Peter Dueben from the European Centre for Medium-Range Weather Forecasts (ECMWF) presents: Machine Learning for Weather Forecasts.
"I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will than talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future."
Peter is contributing to the development and optimization of weather and climate models for modern supercomputers. He is focusing on a better understanding of model error and model uncertainty, on the use of reduced numerical precision that is optimised for a given level of model error, on global cloud- resolving simulations with ECMWF's forecast model, and the use of machine learning, and in particular deep learning, to improve the workflow and predictions. Peter has graduated in Physics and wrote his PhD thesis at the Max Planck Institute for Meteorology in Germany. He worked as Postdoc with Tim Palmer at the University of Oxford and has taken up a position as University Research Fellow of the Royal Society at the European Centre for Medium-Range Weather Forecasts (ECMWF) in 2017.
Watch the video: https://youtu.be/ks3fkRj8Iqc
Learn more: https://www.ecmwf.int/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Discrete-event simulation: best practices and implementation details in Pytho...Carlos Natalino da Silva
Discrete-event simulation is one of the most useful techniques to evaluate quickly and effectively the performance of systems. It enables benchmarking proposed strategies against existing ones in a time- and computing-efficient manner. However, there are several aspects that should be considered when designing and implementing your simulation environment. In this tutorial, a number of best practices when designing and implementing event-driven simulations will be discussed. A use case of routing in optical networks will be used as an example. The implementation of the main simulator components using Java and Python will be described.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Modeling Uncertainty For Middleware-based Streaming Power Grid ApplicationsJenny Liu
The power grid is incorporating high throughput sensor devices into power distribution networks. The future power
grid needs to guarantee accuracy and responsiveness of applications that consume data from multiple sensor streams.
The end-to-end performance and overall scalability of cyber-physical energy applications depend on the middleware's ability to handle multi-source sensor data, which exhibits uncertain behavior under highly variable numbers of sensors and middleware topologies. In this paper, we present a parametric approach to model middleware uncertainty and to analyze its eect on distributed power applications. The models encapsulate the entire data
ow paths from sensor devices, through network and middleware components to the power application nodes that utilize sensor data streams. Using the Ptolemy II framework for modeling and simulation, we generate Monte Carlo samples of uncertain parameters that are used to generate parameterized middleware models that are used in end-to-end Discrete-Event(DE) system simulation simulation. The simulation results are further analyzed using regression methods to reveal the parameters that are influential in the limiting middleware behaviour to achieve temporal requirements of the power applications.
Final Year Students Project
Opposite to Sripuram Bus Stop
Back of Rajadeepan Jewellers
Tirunelveli.
Phone:+91 - 8903410319
Mail: finalyearstudentsprojecttvl@gmail.com
web:www.finalyearstudentsproject.in
Edal an energy efficient, delay-aware, and lifetime-balancing data collection...LogicMindtech Nologies
NS2 Projects for M. Tech, NS2 Projects in Vijayanagar, NS2 Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, NS2 IEEE projects in Bangalore, IEEE 2015 NS2 Projects, WSN and MANET Projects, WSN and MANET Projects in Bangalore, WSN and MANET Projects in Vijayangar
Positioning University of California Information Technology for the Future: S...Larry Smarr
05.02.15
Invited Talk
The Vice Chancellor of Research and Chief Information Officer Summit
“Information Technology Enabling Research at the University of California”
Title: Positioning University of California Information Technology for the Future: State, National, and International IT Infrastructure Trends and Directions
Oakland, CA
SERENE 2014 Workshop: Paper "Simulation Testing and Model Checking: A Case St...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 2: Simulation Testing and Model Checking: A Case Study Comparing these Approaches
SERENE 2014 Workshop: Paper "Advanced Modelling, Simulation and Verification ...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 3: Advanced Modelling, Simulation and Verification for Future Traffic Regulation Optimisation
SERENE 2014 Workshop: Paper "Adaptive Domain-Specific Service Monitoring"SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 4: Monitoring
Paper 2: Adaptive Domain-Specific Service Monitoring
SERENE 2014 Workshop: Paper "Combined Error Propagation Analysis and Runtime ...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 4: Monitoring
Paper 3: Combined Error Propagation Analysis and Runtime Event Detection in Process-driven Systems
SERENE 2014 Workshop: Panel on "Views on Runtime Resilience Assessment of Dyn...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Panel: Views on Runtime Resilience Assessment of Dynamic Software Systems
SERENE 2014 Workshop: Paper "Verification and Validation of a Pressure Contro...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 1: Verification and Validation of a Pressure Control Unit for Hydraulic Systems
SERENE 2014 Workshop: Paper "Using Instrumentation for Quality Assessment of ...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 4: Monitoring
Paper 1: Using Instrumentation for Quality Assessment of Resilient Software in Embedded Systems
SERENE 2014 Workshop: Paper "Formal Fault Tolerance Analysis of Algorithms fo...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 2: Analysis of Resilience
Paper : Formal Fault Tolerance Analysis of Algorithms for Redundant Systems in Early Design Stages
Hot Stand-By Disaster Recovery Solutions for Ensuring the Resilience of Railw...SERENEWorkshop
Specifications of modern railway control systems often include resilience requirements in order to quickly and safely recovery from disasters (e.g. system-level failures). To that aim, spatial redundancy is required, with main and backup systems installed in fully isolated buildings, together with very short switchover times from main to backup systems in case of disasters. In order to fulfil those requirements, Ansaldo STS has developed a system-level hot stand-by solution allowing to quickly and smoothly switch from the main system to the back-up one, ensuring the necessary continuity of service and transparency to train supervisors and other operators. The functional architecture of such a solution is able to keep aligned the safety-critical nucleuses, typically based on N-modular redundancy (i.e. ‘KooM’ voting), of the main and the back-up systems. Such a coherent alignment must be kept in terms of both interfaced field devices (e.g. interlocking signals, track circuits, switch points, etc.) – on the ‘bottom’ level – and control room Human Machine Interfaces (HMI) – on the ‘top’ level. The solution is based on heterogeneous and redundant network links (copper/fiber Ethernet/HyperRing) at different levels of system architecture. In this speech, the reference architecture and the fault-tolerance functionalities for disaster recovery are provided, considering the requirements of real railway and mass-transit installations.
SERENE 2014 School: Measurement-Driven Resilience Design of Cloud-Based Cyber...SERENEWorkshop
SERENE 2014 School on Engineering Resilient Cyber Physical Systems
Talk: Measurement-Driven Resilience Design of Cloud-Based Cyber-Physical Systems, by Imre Kocsis
Serene 2015
Davide Scaramuzza
Abstract: With drones becoming more and more popular, safety is a big concern. A critical situation occurs when a drone temporarily loses its GPS position information, which might lead it to crash. This can happen, for instance, when flying close to buildings where GPS signal is lost. In such situations, it is desirable that the drone can rely on fall-back systems and regain stable flight as soon as possible. In this talk, I will present novel methods to automatically recover and stabilize a quadrotor from any initial condition or execute emergency landing. On the one hand, this new technology will allow quadrotors to be launched by simply tossing them in the air, like a “baseball ball”. On the other hand, it will allow them to recover back into stable flight or land on a safe area after a system failure. Since this technology does not rely on any external infrastructure, such as GPS, it enables the safe use of drones in both indoor and outdoor environments. Thus, it can become relevant for commercial use of drones, such as parcel delivery.
Recent videos:
Automatic failure recovery without GPS: https://youtu.be/pGU1s6Y55JI
Autonomous Landing-site detection and landing: https://youtu.be/phaBKFwfcJ4
SERENE 2014 School: Resilience in Cyber-Physical Systems: Challenges and Oppo...SERENEWorkshop
SERENE 2014 School on Engineering Resilient Cyber Physical Systems
Talk: Resilience in Cyber-Physical Systems: Challenges and Opportunities, by Gabor Karsai
IncQuery-D: Distributed Incremental Model Queries over the Cloud: Engineerin...Daniel Varro
In model-driven software engineering (MDE), model queries are core technologies of many tool and transformation-specific challenges such as design rule validation, model synchronization, view maintenance, simulation and many more. As software models are rapidly increasing in size and complexity, traditional MDE tools frequently face scalability issues that decrease productivity of engineers and increase development costs. Incremental graph queries offer a graph pattern based language for capturing queries. Furthermore, the result set of a query is cached and incrementally maintained upon model changes to provide instantaneous query response time. In this talk, first a brief overview is given on the EMF-IncQuery framework (which is an official Eclipse subproject). Then we discuss how to incorporate incremental queries over a distributed cloud infrastructure (to scale up from a single-node tool to a cluster of nodes) deployed over popular database back-ends (such as Cassandra. 4store, Neo4J, etc). We present our first benchmarking experiments with IncQuery-D to highlight that distributed incremental model queries can perform significantly better than the native query technologies of the underlying database back-end, especially, for complex queries.
RAMSES: Robust Analytic Models for Science at Extreme ScalesIan Foster
RAMSES: A new project in data-driven analytical modeling of distributed systems
RAMSES is a new DOE-funded project on the end-to-end analytical performance modeling of science workflows in extreme-scale science environments. It aims to link multiple threads of inquiry that have not, until now, been adequately connected: namely, first-principles performance modeling within individual sub-disciplines (e.g., networks, storage systems, applications), and data-driven methods for evaluating, calibrating, and synthesizing models of complex phenomena. What makes this fusion necessary is the drive to explain, predict, and optimize not just individual system components but complex end-to-end workflows. In this talk, I will introduce the goals of the project and some aspects of our technical approach.
Abstractions and Directives for Adapting Wavefront Algorithms to Future Archi...inside-BigData.com
In this deck from PASC18, Robert Searles from the University of Delaware presents: Abstractions and Directives for Adapting Wavefront Algorithms to Future Architectures.
"Architectures are rapidly evolving, and exascale machines are expected to offer billion-way concurrency. We need to rethink algorithms, languages and programming models among other components in order to migrate large scale applications and explore parallelism on these machines. Although directive-based programming models allow programmers to worry less about programming and more about science, expressing complex parallel patterns in these models can be a daunting task especially when the goal is to match the performance that the hardware platforms can offer. One such pattern is wavefront. This paper extensively studies a wavefront-based miniapplication for Denovo, a production code for nuclear reactor modeling.
We parallelize the Koch-Baker-Alcouffe (KBA) parallel-wavefront sweep algorithm in the main kernel of Minisweep (the miniapplication) using CUDA, OpenMP and OpenACC. Our OpenACC implementation running on NVIDIA's next-generation Volta GPU boasts an 85.06x speedup over serial code, which is larger than CUDA's 83.72x speedup over the same serial implementation. Our experimental platform includes SummitDev, an ORNL representative architecture of the upcoming Summit supercomputer. Our parallelization effort across platforms also motivated us to define an abstract parallelism model that is architecture independent, with a goal of creating software abstractions that can be used by applications employing the wavefront sweep motif."
Watch the video: https://wp.me/p3RLHQ-iPU
Read the Full Paper: https://doi.org/10.1145/3218176.3218228
and
https://pasc18.pasc-conference.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Keynote at COMMitMDE'18 showing the basic concepts behind Hawk, our past case studies, and some of our experience in designing the Hawk Thrift APIs for remote model querying.
Driving Moore's Law with Python-Powered Machine Learning: An Insider's Perspe...PyData
People talk about a Moore's Law for gene sequencing, a Moore's Law for software, etc. This is talk is about *the* Moore's Law, the bull that the other "Laws" ride; and how Python-powered ML helps drive it. How do we keep making ever-smaller devices? How do we harness atomic-scale physics? Large-scale machine learning is key. The computation drives new chip designs, and those new chip designs are used for new computations, ad infinitum. High-dimensional regression, classification, active learning, optimization, ranking, clustering, density estimation, scientific visualization, massively parallel processing -- it all comes into play, and Python is powering it all.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
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SERENE 2014 School: Incremental Model Queries over the Cloud
1. Distributed Incremental
Model Queries over the Cloud
Budapest University of Technology and Economics
Department of Measurement and Information Systems
Dániel Varró
Budapest University of Technology and Economics
Fault Tolerant Systems Research Group
2. Outline of the Talk
Motivation & Background:
• Change detection in CPS
•Design Space Exploration
Incremental Model Queries:
The EMF-IncQuery framework
• Language - Execution
Distributed Incremental
Model Queries (IncQuery-D)
•Architecture -
Performance Benchmarks
•Distributed model load
• Incremental query evaluation
Main Contributors
o István Ráth (lead)
o Ákos Horváth
o Gábor Bergmann
o Ábel Hegedüs
o Zoltán Ujhelyi
o Benedek Izsó
o Gábor Szárnyas
o Csaba Debreceni
o Dénes Harmath
o József Makai
o Dániel Stein
3. Challenges for IoT / CPS
Cyber
world
Physical
world
Problem
Solution
scheme
Deployment
Service
Solution
pattern
Component
service
offering
Challenge:
Detect changes
• in system state
• in environment
Abstractions
Design space
exploration
5. Big Data Analytics for CPS
Sensors / Services Data and Event sources Cloud based apps
Data
Store
Data
Store
EvenCtlsoud based
Computation
Polling
Events
6. Challenge: Change Detection in CPS
Sensors / Services Data and Event sources Cloud based apps
Data
Store
Data
Store
EvenCtlsoud based
Computation
Polling
Events
?
7. Change Detection in CPS by Incremental Queries
Sensors / Services Data and Event sources Cloud based apps
Polling
Data
Store
Events
Data
Store
UUnniiffiieedd CChhaannggee DDeetteeccttiioonn bbyy
Distributed Incremental Queries
Cloud based
Computation
9. Motivation: Early validation of design rules
SystemSignalGroup design rule (from AUTOSAR)
o A SystemSignal and its group must be in the same IPdu
o Challenge: find violations quickly in large models
o New difficulties
• reverse
navigation
• complex
manual
solution
AUTOSAR:
• standardized SW architecture
of the automotive industry
• now supported by modern modeling tools
Design Rule/Well-formedness constraint:
• each valid car architecture needs to respect
• designers are immediately notified if violated
Challenge:
• 500 design rules in AUTOSAR tools
• 1 million elements in AUTOSAR models
• models constantly evolve by designers
11. Validation of Well-formedness Constraints
Meta-model
Model
Query
pattern switchWOSignal(sw) {
Switch(sw);
neg find switchHasSignal(sw);
}
pattern switchHasSignal(sw) {
Switch(sw);
Signal(sig);
Signal.mountedTo(sig, sw);
}
Modify
User
Result
12. Model sizes in practice
Models with 10M+ elements are common:
o Car industry
o Avionics
o Source code analysis
Models evolve and change continuously
Validation can take hours
Application Model size
System models 108
Sensor data 109
Geospatial models 1012
Source: Markus Scheidgen, How Big are Models – An Estimation, 2012.
14. What is a model query?
For a programmer:
o A piece of code that searches for parts of the model
For the scientist:
o Query = set of constraints that have to be satisfied by
(parts of) the (graph) model
o Result = set of model element tuples that satisfy the
constraints of the query
oMatch = bind constraint variables to model elements
A query engine: Supports
o the definitionexecution
of model queries
Query(A,B) ∧condi(Ai,Bi)
• all tuples of model elements a,b
• satisfying the query condition
• along the match A=a and B=b
• parameters A,B can be input/ output
15. Graph Pattern Matching for Queries
route: Route sp: SwitchPosition
routeDefinition
sensor: Sensor switch: Switch
Match:
o m: L G
(graph morphism)
o CSP:
• Variables: Nodes of L
• Constraints: Edges of L
• Domain values: G
o Complexity: |G|^|L|
L
G
straight
left
switchPosition
switch
sensor
All sensors with a switch that belongs to a route must directly be linked to the same route.
16. Graph Pattern Matching (Local Search)
switchPosition
route: Route sp: SwitchPosition
switch
routeDefinition
sensor
sensor: Sensor switch: Switch
Search Plan:
o Select the first node
to be matched
o Define an ordering on
graph pattern edges
Search is restarted from
scratch each time
1
2
0
3
4
straight
left
17. Incremental Graph Pattern Matching
switchPosition
route: Route sp: SwitchPosition
switch
routeDefinition
sensor
sensor: Sensor switch: Switch
Main idea: More space to less time
o Cache matches of patterns
o Instantly retrieve match (if valid)
o Update caches upon model changes
o Notify about relevant changes
Approaches:
o TREAT, LEAPS, RETE, …
o Tools: VIATRA, GROOVE, MoTE, TCore
straight
left
route sp switch sensor
r1 sp1 sw1
19. EMF-IncQuery: An Open Source Eclipse Project
• Declarative graph query
language
• Transitive closure,
Negative cond., etc.
• Compositional, reusable
Definition
• Incremental evaluation
• Cache result set
•Maintain incrementally
upon model change
Execution
• Derived features,
• On-the-fly validation
• View generation,
Notifications, Soft links,
Databinding,
Features
http://eclipse.org/incquery
20. The IncQuery (IQ) Graph Query Language
route: Route sp: SwitchPosition
routeDefinition
sensor: Sensor Switch: Switch
IQ: declarative query language
o Attribute constraints
o Local + global queries
o Compositionality+Reusabilility
o Recursion, Negation,
o Transitive Closure
o Syntax: DATALOG style
pattern routeSensor(sensor: Sensor) = {
TrackElement.sensor(switch,sensor);
Switch(switch);
SwitchPosition. switch(sp, switch);
SwitchPosition(sp);
Route.switchPosition(route, sp);
Route(route);
neg find head(route, sensor);
}
pattern head(R, Sen) = {
Route.routeDefinition(R, Sen);
}
ModelQuery(A,B):
• tuples of model elements A, B
• satisfying the query condition
• enumerate 1 / all instances
• A,B can be input or output
switchPosition
switch
sensor
21. Incremental Query Evaluation by RETE
AUTOSAR well-formedness validation rule
Communication
channel
Logical signal Mapping Physical signal
Instance model
Invalid model fragment
Valid model fragment
22. Incremental Query Evaluation by RETE
Read the changes in the
PFrFRioMlileplltoaahtddghea ietftwhyeineottphrrhkueeeetsm rucnhnlootaoddsndeeegeltsess
result set (deltas)
join
join
antijoin
Result set
Communication
channel
Logical signal Mapping Physical signal
23. Performance of EMF-INCQUERY
Incremental graph queries based on Rete
Built for the Eclipse Modeling Framework
model size
runtime
batch
queries
incremental
queries
Runtime is proportional to
the size of the modification.
24. Performance of EMF-INCQUERY
Storing partial
memory results
consumption
incremental
queries
batch
queries
memory
limit
model size
25. Selected Applications (EMF-IncQuery)
• Complex traceability
• Query driven views
• Abstract models by
derived objects
Toolchain for
IMA configs
• Connect to Matlab
Simulink model
• Export: Matlab2EMF
• Change model in EMF
• Re-import:
EMF2Matlab
MATLAB-EMF
Bridge
• Live models
(refreshed 25
frame/s)
• Complex event
processing
Gesture
recognition
• Experiments on open
source Java projects
• Local search vs.
Incremental vs.
Native Java code
Detection of
bad code smells
• Rules for operations
• Complex structural
constraints (as GP)
• Hints and guidance
• Potentially infinite
state space
Design Space
Exploration
• Itemis (developer)
• Embraer
• Thales
• ThyssenKrupp
• CERN
Known Users
27. Goals of INCQUERY-D
Objectives
o Distributed incremental pattern matching
o Adaptation of EMF-INCQUERY’s tooling to graph DBs
o Executed over cloud infrastructure (COTS hardware)
Achieve scalability by avoiding memory bottleneck
o Sharding separately
• Data
• Indexers
• Query network
o In memory:
• Index + Query
Assumptions
• All Rete nodes fit on a server node
• Indexers can be filled efficiently
• Modification size ≪ model size
• The application requires the complete result
set of the query (opposed to just one match)
28. Dimensions of Scalability
Infrastructure
o Number of machines
o Available memory / CPU
o Network performance
o Number of concurrent users
Model
o Model size
o Model characteristics
Queries
o Number of queries
o Query complexity
Metrics
29. INCQUERY-D Architecture
EMF-INCQUERY INCQUERY-D
Join
Database
shard 1
Server 1
Join
Database
shard 2
Server 2
Triple store (4store),
Document DB (Mongo),
RDF over Column family
Database
shard 3
Server 3
Transaction
Database
shard 0
In-memory
EMF model
Server 0
Antijoin
Rete net
Indexer
layer
Akka
Distributed query evaluation network
Distributed indexer Model access adapter Indexing Indexer Indexer Indexer Indexer
In-memory storage
Distributed indexing,
notification
Production network
• Stores intermediate query results
• Propagates changes
Distributed persistent
storage
Distributed production network
• Each intermediate node can be allocated
to a different host
• Remote internode (Cumulus)
communication
30. Termination Protocol in INCQUERY-D
Database
shard 0
When a production node reached
an ACK message is sent back Stack added to each update msg
Database
shard 1
Server 1
• Registers the Rete nodes the
message passes through
Database
shard 2
Server 2
User retrieves
query result
Database
shard 3
Server 3
Transaction
Server 0
INCQUERY-D
Join
Join
Antijoin
Indexer Indexer Indexer Indexer
31. IncQuery-D Architectural Layers
• Gremlin, Cypher
• SPARQL
• IQPL (IncQuery)
High-Level
Query Lang
• Distributed Indexers
(MONDIX)
• SPARQL
Low-Level
Query Lang
• Cayley
• Titan
• 4store
Distributed
Graph DB
• MongoDB
• Cassandra
• 4store
Native
Storage
• RDF
• XMI / Ecore
• Property Graphs
Storage
Format
• Global queries
• Complex navigations
• Efficient element access by indices
• Local queries
• Can be transparent (via indexers)
• Integrates popular graph storages
• Efficient NoSQL storages
• Triple stores
• Standardized data formats
• Popular interchange formats
32. Summary: Key Components of IncQuery-D
Distributed
Model Storage
• Adaptable to
different back-end
storages
• Agnostic to
graph repres.
• TripleStores
(RDF), EMF,
Property
graph
Model Access
Adapter
• Surrogate key
to identify
distibuted
elements
• Graph manip.
API
• Change
notifications
Distributed
Indexer
• Type-instance
indices, etc.
• Stored on
multiple
servers
• Protects
exceeding
memory limits
Distributed
Query Evaluator
• Distributed
RETE network
• Distributed
termination
protocol
• Constructed
and deployed
by coordinator
node
Decouple and separately distribute Storage, Indexer and Query layers
34. Load
Model
(1) Loading a Query
Update
Model
Request
Result
Deploy
RETE
RETE
Network
Allocate
RETE
Cloud
Infra-structure
Construct
RETE
Load
Query
Construct RETE
• From EMF-IncQuery specs
• Should incorporate
infrastructure constraints
Deploy RETE
• Managed by a
coordinator node
• Intelligent sharding of
RETE nodes
35. Load
Model
(2) Loading a Model
Update
Model
Request
Result
Model
shards
Deploy
RETE
RETE
Network
Allocate
RETE
Maintain
Result Set
Cloud
Infra-structure
Construct
RETE
Model
Access
Adapter
Load
Query
Load model
• Model traversal
• Init indexers
• Network
communication
36. Load
Model
(3) Updating a Model
Update
Model
Request
Result
Model
shards
Deploy
RETE
RETE
Network
Allocate
RETE
Maintain
Result Set
Cloud
Infra-structure
Construct
RETE
Model
Access
Adapter
Load
Query
Model manipulation
• Update messages
• Create / Delete
37. (4) Requesting Query Result
Load
Model
Update
Model
Request
Result
Model
shards
Deploy
RETE
RETE
Network
Allocate
RETE
Evaluate
Query
Maintain
Result Set
Cloud
Infra-structure
Construct
RETE
Model
Access
Adapter
Load
Query
Evaluate query
• Process incoming
messages
• Propagate along
RETE network
Retrieve results
• instantly
38. (5) Monitoring and Reconfiguration
Load
Model
Update
Model
Request
Result
Model
shards
Deploy
RETE
RETE
Network
Allocate
RETE
Evaluate
Query
Maintain
Result Set
Cloud
Infra-structure
Monitor Manage
Construct
RETE
Model
Access
Adapter
Load
Query
Visualized on a
web-based dashboard
OS metrics JVM metrics Akka metrics Rete metrics
46. RETE Deployment Process
Configuration scripts for
o Deployment
o Communication
middleware
Derived by automated
code generation
o Using Eclipse technology:
EMF-IncQuery + Xtend
Query
Language
Query
Predicates
RETE
Structure
Platform
Description
Allocation /
Mapping
Deployment
Descriptor
48. The Train Benchmark
Model validation workload:
o User edits the model
o Instant validation of
well-formedness constraints
o Model is repaired accordingly
Scenario:
o Load
o Check
o Edit
o Re-Check
Models:
o Randomly generated
o Close to real world instances
o Following different metrics
o Customized distributions
o Low number of violations
Queries:
o Two simple queries
(2 objects, attributes)
o Two complex queries
(4-7 joins, negation, etc.)
o Validated match sets
Incremental Batch validation validation
Instance
model
Read Check ! Edit ReCheck
100x
49. Evaluation of distributed scalability
Extensions to previous work (single workstation)
o Generation of large instance models
o Distributed, parallel loading of models
o Distributed transformation and validation
Benchmark Distributed benchmark
Model size 1K – 13M 1K – 88M
Load method Batch Distributed, parallel
Transformation and validation Single workstation Multiple servers
50. IncremBenattcahl sgcreanpahr isoc e–nIanrciQouery-D
Load and first validation: load the graph to the databases
and execute initialize the the Rete query
net and retrieve the results
Transformation: query the incrementally graph query and the delete graph some
and
delete elements
some elements, propagate the changes
Revalidation: execute retrieve the query
results from the Rete net
Load and first
validation
GraphML Transformation Revalidation
DB shards Result set
Rete net
DB shards Result set
Rete net
51. Benchmark environment
Private cloud
Different DBMSs
Query
o The DBMS’s own query language
o IncQuery-D
SPARQL Gremlin
52. 4096
1024
256
64
16
4
1
Runtime [s]
Load and first validation
55M model: approx. 15 minutes
Rete network’s
initialization
overhead pays off
Model size [million elements]
4store IncQuery-D Titan IncQuery-D 4store
53. 256
128
64
32
16
8
4
2
1
Runtime [s]
Model modification
1. Elementary model query
2. Model modification
2 orders of
magnitude
– Query from the Rete network’s indexer
– Propagation of modifications is fast
Model size [million elements]
4store IncQuery-D Titan IncQuery-D 4store
54. 128,00
32,00
8,00
2,00
0,50
0,13
0,03
0,01
Runtime [s]
Revalidation
Different characteristics
Sub-second response
time for models with
88M elements
Model size [million elements]
memory
limit
4store IncQuery-D Titan IncQuery-D 4store
55. Benchmarking Conclusions
Memory consumption
o Single workstation: 13M model, 4 GB
o Cloud of four servers: 55M model, 4×8 GB
Runtime
o Same order of magnitude and similar characteristics to
the single workstation tool
INCQUERY-D is scalable and significantly more efficient for query
evaluation than the native query engines in 4store, Titan and Neo4j
56. Applications of Distributed Incremental Queries
• Query based optimistic locking
• Queries for Attribute Based Access
Control
Collaborative
Modeling (MONDO)
• Events vs. Changes
• Handle compound changes as events
Complex Event
Processing w/
compound changes
• System evolves along operations
• Cost / Objectives associated to
• States + Environment + Trajectory
Rule-based Design
Space Exploration
58. TRANS-IMA Project (Avionics)
Goal: Allocate SW components to
ARINC653 compliant IMA platform
58
Functional
Architecture
Component
database
Platform
description
Allocation
Integrated
System
Model
Inputs:
• Platform Independent Model (PIM)
(functional + nonfunc. reqs; Simulink)
• Platform Description Model (PDM)
for ARINC 653 (DSL)
Output:
• Integrated system model
• Ready for simulation
• End-to-end traceability
59. Designing ARINC653 configurations
(critical + non-critical)
Supply fresh air
Supply hot air
Monitor
temperature
Set
temperature
SW functionality
Pack
Controller
Zone
Controller
3
System
Display
AirCond
Panel
3
Redundancy
requirement
60. Job instances, Partitions, Modules
SW functionality
(critical + non-critical)
Pack
Controller
Zone
Controller
3
System
Display
AirCond
Panel
3
Job instances
1
2 3
4
5 6
7
8
Partitions
Modules
Constraints
2
5
3
4
8
8
8
8
Memory needs
+ constraints
Do not mix critical
and non-crit. jobs
Do not mix
instances of the
same critical job
Additional constraints
• WCET,
• scheduling, etc.
• interfaces
• datatypes
61. Allocating communication channels
SW functionality
Pack
Controller
Zone
Controller
3
System
Display
AirCond
Panel
3
1
2
3
7
4
5
6
8
Communication
channels
Humidity
Pressure
Temperature
62. Design Space Exploration
Design Space Exploration
62
Design
Alternative 1
Design
Alternative 2
Design
Alternative 3
Design
Alternative 4
Objectives
Global
Constraints
Initial Design
Solvers
• CLP solvers (Choco)
• model finders (Alloy)
• meta-heuristics +
multi-objective optimization
63. Design Space Exploration (Example)
63
Consistency Analysis
Design
Alternative 1
Design
Alternative 2
Design
Alternative 3
Design
Alternative 4
Objectives
Global
Constraints
Initial Design
A
B
x=2
C
A
A A
B
x=?
C
I1 I2
64. Design Space Exploration (Example)
64
+ Filled
Attributes
Consistency Analysis
Design
Alternative 1
Design
Alternative 2
Design
Alternative 3
Design
Alternative 4
Objectives
Global
Constraints
Initial Design
A
B
x=2
C
x=?
A
B
x=5
C
C
C
A
C
O
C1
C2
+ Objects
I1 I2
+ Relations
65. Rule Based Design Space Exploration
Design Space Exploration
65
Design
Alternative 1
Design
Alternative 2
Design
Alternative 3
Design
Alternative 4
Objectives
Global
Constraints
Operations
Initial Design
Special state space exploration
• potentially infinite state space
• „dense” solution space
66. Rule-Based Guided Design Space Exploration
Design Space Exploration
66
Seq of Transf.
Rules 1
Seq of Transf.
Rules 2
Seq of Transf.
Rules 3
Seq of Transf.
Rules 4
Model queries
as Objectives
Model queries
as Constraints
Transf. Rules
as Operations
Initial
Model
Guidance for exploration: Hints
• designer / end user
• formal analysis