Woop - Workflow Optimizer
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The document discusses resource optimization of workflow problems. It introduces the motivation to optimize flexible production systems using a single assembly line for multiple variants. It then describes workflow abstraction and terms, including attributed resources, partially ordered plans, flexible processes, continuous supply, and partitioning. The document outlines the basic model, including data structures, process logic representation as a partially ordered plan, partitioning workflows into blocks, and calculating infrastructure needs and cycle time. It also discusses the heuristic approach and implementation in Mozart/Oz constraint programming.
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Development of high-tech systems is a complex task done by diverse specialists distributed across the globe. Reference architectures including a clear functional breakdowns can support them and support their decisions. This presentation proposes an approach to improve the development of advanced electron microscopes by using Capella as an authoritative source of information. To support design decisions, a Capella AddOn has been developed to obtain quantitative information, such as throughput numbers, for a particular workflow. First, we will illustrate how functional and system decompositions can be captured and serve as company-wide architecting assets to inform design decisions. Next, we will outline how simulating Capella models can bring valuable insights to modelers. During a demo, we’ll simulate Capella’s Functional chains using the open-source simulation tool POOSL (https://github.com/eclipse/poosl) , and visualize results using the freely available TRACE4CPS tool (https://www.eclipse.org/trace4cps/). Re-using functions from the reference architecture allows us reason about design aspects such as the relation between throughput and design choices about function allocation and parallelism.
***
The open-source code of the solution is available at https://github.com/TNO/capella-workflow-dse
The Next Generation of Data Processing and Open Source
The document discusses Apache Beam, a solution for next generation data processing. It provides a unified programming model for both batch and streaming data processing. Beam allows data pipelines to be written once and run on multiple execution engines. The presentation covers common challenges with historical data processing approaches, how Beam addresses these issues, a demo of running a Beam pipeline on different engines, and how to get involved with the Apache Beam community.
Apache Beam: A unified model for batch and stream processing data
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Unbiased, Fine-Grained Description of Processes Performance from Event Data
International Conference on Business Process Management 2018 (BPM2018 Sydney).
Performance is central to processes management and event data pro-vides the most objective source for analyzing and improving performance. Currentprocess mining techniques give only limited insights into performance by aggre-gating all event data for each process step. In this paper, we investigate processperformance of all process behaviors without prior aggregation. We propose theperformance spectrumas a simple model that maps all observed flows betweentwo process steps together regarding their performance over time. Visualizing theperformance spectrum of event logs reveals a large variety of very distinctpatternsof process performanceand performance variability that have not been describedbefore. We provide a taxonomy for these patterns and a comprehensive overviewof elementary and composite performance patterns observed on several real-lifeevent logs from business processes and logistics. We report on a case study whereperformance patterns were central to identify systemic, but not globally visibleprocess problems.
Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...
This talk explores deploying a series of small and large batch and streaming pipelines locally, to Spark and Flink clusters and to Google Cloud Dataflow services to give the audience a feel for the portability of Beam, a new portable Big Data processing framework recently submitted by Google to the Apache foundation. This talk will look at how the programming model handles late arriving data in a stream with event time, windows, and triggers.
Computing Just What You Need: Online Data Analysis and Reduction at Extreme ...
This document discusses computing challenges posed by rapidly increasing data scales in scientific applications and high performance computing. It introduces the concept of online data analysis and reduction as an alternative to traditional offline analysis to help address these challenges. The key messages are that dramatic changes in HPC system geography due to different growth rates of technologies are driving new application structures and computational logistics problems, presenting exciting new computer science opportunities in online data analysis and reduction.
PODS 2013 - Montali - Verification of Relational Data-Centric Dynamic Systems...
PODS 2013 - Montali - Verification of Relational Data-Centric Dynamic Systems...Faculty of Computer Science - Free University of Bozen-Bolzano
- The document discusses verification of Data-Centric Dynamic Systems (DCDSs), which combine data and processes. DCDSs have a data layer consisting of a relational database and constraints, and a process layer consisting of condition-action rules that can call external services.
- The verification problem involves checking if a DCDS satisfies a temporal/dynamic property, given as a formula in first-order μ-calculus. However, unrestricted first-order quantification and even simple temporal properties can make verification undecidable.
- The talk proposes restricting quantification and properties to obtain decidable verification, by constructing a finite-state abstraction of the DCDS transition system that soundly and completely represents its behavior.HPPS 2008 - Maesani Moro
The document proposes enhancements to an automated test-tubes analysis system. It identifies limits in the existing centralized architecture and proposes a distributed architecture using FPGA-based nodes running Linux. The new architecture aims to improve performance and flexibility while supporting complex distributed systems, error detection, and live reconfiguration of nodes.
2008-10-09 - Bits and Chips Conference - Embedded Systemen Architecture patterns
In the past, embedded software was intended to automate simple isolated tasks for dedicated purposes. However, there is a trend towards integrating embedded components into large networks which can perform complex tasks. Customers expect systems to be open and extensible, to prepare for future challenges. This introduces new challenges for embedded software engineers: the integration of components into larger integrated networks poses new demands upon component quality (how to prepare a component for all possible future assemblies) as well as an integrated system architecture viewpoint (how to construct a flexible but secure and reliable network).
In this presentation we show, based on practical examples, what the value of systematic thinking of software quality and systems architecture is in developing complex integrated embedded systems.
Cloud Dataflow - A Unified Model for Batch and Streaming Data Processing
Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
High Performance & High Throughput Computing - EUDAT Summer School (Giuseppe ...
Giuseppe will present the differences between high-performance and high-throughput applications. High-throughput computing (HTC) refers to computations where individual tasks do not need to interact while running. It differs from High-performance (HPC) where frequent and rapid exchanges of intermediate results is required to perform the computations. HPC codes are based on tightly coupled MPI, OpenMP, GPGPU, and hybrid programs and require low latency interconnected nodes. HTC makes use of unreliable components distributing the work out to every node and collecting results at the end of all parallel tasks.
Visit: https://www.eudat.eu/eudat-summer-school
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Apache Beam is Flink’s sibling in the Apache family of streaming processing frameworks. The Beam and Flink teams work closely together on advancing what is possible in streaming processing, including Streaming SQL extensions and code interoperability on both platforms.
Beam was originally developed at Google as the amalgamation of its internal batch and streaming frameworks to power the exabyte-scale data processing for Gmail, YouTube and Ads. It now powers a fully-managed, serverless service Google Cloud Dataflow, as well as is available to run in other Public Clouds and on-premises when deployed in portability mode on Apache Flink, Spark, Samza and other runners. Users regularly run distributed data processing jobs on Beam spanning tens of thousands of CPU cores and processing millions of events per second.
In this session, Sergei Sokolenko, Cloud Dataflow product manager, and Reuven Lax, the founding member of the Dataflow and Beam team, will share Google’s learnings from building and operating a global streaming processing infrastructure shared by thousands of customers, including:
safe deployment to dozens of geographic locations,
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separating compute and state storage for better scaling behavior,
dynamic work rebalancing of work items away from overutilized worker nodes,
offering a throughput-optimized batch processing capability with the same API as streaming,
grouping and joining of 100s of Terabytes in a hybrid in-memory/on-desk file system,
integrating with the Google Cloud security ecosystem, and other lessons.
Customers benefit from these advances through faster execution of jobs, resource savings, and a fully managed data processing environment that runs in the Cloud and removes the need to manage infrastructure.
OSLC KM (Knowledge Management): elevating the meaning of data and operations ...
OSLC KM (Knowledge Management): elevating the meaning of data and operations ...CARLOS III UNIVERSITY OF MADRID
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Slides from a lecture I gave at the Big Data Analytics with Hadoop and Spark course (Chalmers University of Technology)
EO notes Lecture 27 Project Management 2.ppt
The document discusses project management techniques including PERT and CPM. It explains that PERT and CPM are used to plan, schedule, and coordinate large projects by graphically displaying project activities, estimating project duration, identifying critical activities, and determining float. The framework involves defining activities and relationships, drawing network diagrams, estimating activity times, computing the critical path, and using the network to plan and control the project. Key terms and how to draw network diagrams are also covered.
In Depth Constructive Cost Modeling related slides
The COCOMO model is a family of software cost estimation models developed by Barry Boehm. It includes COCOMO I from 1981 and COCOMO II from 2000. The models take source lines of code (SLOC) as input and provide estimates of effort (person-months), development time, and staffing needed. Later models added more parameters to improve accuracy. COCOMO I has basic, intermediate, and detailed models. COCOMO II models include application composition, early design, and post-architecture models. Both use effort adjustment factors/multipliers to account for attributes like requirements, team experience, and schedule.
Statistical Framework for Technology-Model-Product Co-Design and Convergence
This document proposes a statistical framework for co-designing technology, models, and products. It includes three main components: cross-correlation analysis to link device characteristics to product performance; statistical yield estimation to predict yield using hardware data; and variability decomposition to separate systematic variability. The framework is applied to an integrated circuit oscillator product developed across three 65nm technology generations, improving yield from 47% to 99% while meeting design specifications.
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and Gr...
Title:
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and GridDyn
Presenters:
Luigi Vanfretti (RPI) & Philip Top (LNLL)
luigi.vanfretti@gmail.com, top1@llnl.gov
Abstract:
The Modelica language, being standardized and equation-based, has proven valuable for the for model exchange, simulation and even for model validation applications in actual power systems. These important features have been now recognized by the European Network of Transmission System Operators, which have adopted the Modelica language for dynamic model exchange in the Common Grid Model Exchange Standard (v2.5, Annex F).
Following previous FP7 project results, within the ITEA 3 openCPS project, the presenters have continued the efforts of using the Modelica language for power system modeling and simulation, by developing and maintaining the OpenIPSL library: https://github.com/SmarTS-Lab/OpenIPSL
This seminar first gives an overview of the origins of the OpenIPSL and it’s models, it contrasts it against typical power system tools, and gives an introduction the OpenIPSL library. The new project features that help in the OpenIPSL maintenance (use of continuous integration, regression testing, documentation, etc.) are also described.
Finally, the seminar will present current work at LNLL that exploits OpenIPSL in coordination with other tools including ongoing work integrating openIPSL models into GridDyn an open-source power system simulation tool, as well as a demos of the use of openIPSL libraries in GridDyn.
Bios:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
His research interests are in the area of synchrophasor technology applications; and cyber-physical power system modeling, simulation, stability and control.
Philp Top (Lawrence Livermore National Lab)
PhD 2007 Purdue University. Currently a Research Engineer at Lawrence Livermore National Laboratory in Livermore, CA. Philip has been involved in several projects connected with the DOE effort on Grid Modernization including projects on modeling and simulation, co-simulation and smart grid data analytics. He is the principle developer on the open source power system simulation tool GridDyn, and a key contributor to the HELICS open source co-simulation framework.
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Woop - Workflow Optimizer
- 1. Diploma Thesis Martin Homik Resource Optimization of Workflow Problems Tutors: Christian Schulte Tobias Müller
- 9. Attributed Resources (Workflow Participant Types) C = Cost Sut = set-up time St = Stability Skills overlap arbitrarily! 11, 12, 15 12, 13, 16, 19 17, 18, 19 C: 400 € Sut: 200 St: 95% C: 150 € Sut: 200 St: 90% C: 250 € Sut: 200 St: 95% Overlap Overlap
- 10. Partially Ordered Plan (POP) Each activity has to be executet! 0 12 13 15 16 17 18 19 100 11 Start End Activity Precedence relation
- 11. Flexible Processes 0 12 13 15 16 17 18 19 100 11 0 12 13 15 17 18 19 100 11 20 0 12 13 15 16 17 19 100 11 21 22
- 12. Continuous Supply There is always work to do at each station! Object are passed only in one direction!
- 14. Problem Specification (Given) maximal cycle time 0 12 13 15 16 17 18 19 100 11 0 12 13 15 17 18 19 100 11 20 0 12 13 15 16 17 19 100 11 21 22
- 15. Problem Specification (2) Organisation? Infrastructure? Block i A j
- 16. Problem Specification (2) Organisation? Infrastructure? Process logic? Block i A j
- 17. Partitioning (Example) Partitioning guarantees a directed data flow! 0 12 13 15 16 17 18 19 100 11 0 12 13 15 16 17 18 19 100 11
- 18. Partitioning 0 12 13 15 16 17 18 19 100 11 11, 12, 15 12, 13, 16, 19 17, 18, 19 1. Rule: 15 16: 15 in 16 in different blocks
- 19. Partitioning (2) 11, 12, 15 12, 13, 16, 19 17, 18, 19 0 12 13 15 16 17 18 19 100 11 2. Rule: 12 15: 12 in and 15 in the same block
- 20. Partitioning (3) 0 12 13 15 16 17 18 19 100 11 3. Rule: 16 19: 16 in 19 in and a) different blocks b) the same block 11, 12, 15 12, 13, 16, 19 17, 18, 19
- 22. Model: Data Structures Number blocks: Set of all costs: General loss: Maximum cycle time : Set of all activities: Set of all dates: Set of percentage:
- 24. Model: Data Structures Costs: Participant type tuple: Number participant types tuple:
- 25. Model: POP A POP is a directed, acyclic and compound graph: We denote the set of all POPs by : Nodes Edges Projections :
- 26. Model: POP Union: Let Then
- 27. Model: Partition Partition of A u in: Let and And ...next slide
- 28. Model: Partition(2) Otherwise: Properties of a directed flow:
- 29. Model: Partition(3) General Assignment:
- 30. Total Running Time per Block set-up time running times transit time total running time / block t A1 U A2 U A3 Su1 Su2
- 31. Infrastructure Number of resources in a block S i (where WPT W i ) Number resources Rtb
- 33. Cycle Time number resources cycle time
- 35. Heuristic (Design) number blocks Number of blocks is increasing resources Choose the block with the least resource type assignment possibilities. Assign the cheapest resource type. activities Choose the longest activity. Assign it to the block with the cheapest resource.
- 36. Goal-oriented Heuristic 2. Order/Select: choose longest activity 1. Filter: nondetermined activity 3. Value: assign to the block with the cheapest WPT. 400 € 150 € 250 €
- 37. Problem: Goal-oriented Heuristic Bad! 400 € 150 € 250 €
- 38. Problem: Goal-oriented Heuristic Better! 400 € 150 € 250 €
- 48. Scheduling (Bridge) 2 4 5 6 7 3 1 8 9 11 13 14 15 16 12 17 19 20 21 22 18 23 25 26 27 28 24 10 41 29 31 32 33 34 30 36 38 39 40 35 37 42 43 44 excavator Pile driver carpentry concrete mixer worker bricklaying crane caterpillar
Editor's Notes
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