Genetic Algorithm Approach for Solving the Machine-Job Assignment with Controllable Processing Times

This paper considers a genetic algorithm (GA) for a machine-job assignment with controllable processing times (MJACPT). Integer representation with standard genetic operators is used. In an objective function, a job assignment is obtained from genetic code and for this, fixed assignment processing times are calculated by solving a constrained nonlinear convex optimization problem. Additionally, the job assignment of each individual is improved by local search. Computational results are presented for the instances from literature and modified large-scale instances for the generalized assignment problem (GAP). It can be seen that the proposed GA approach reaches almost all optimal solutions, which are known in advance, except in one case. For large-scale instances, GA obtained reasonably good solutions in relatively short computational time

Predictable execution of scientific workflows using advance resource reservations

Scientific Workflows are long-running and data intensive, and may encompass operations provided by multiple physically distributed service providers. The traditional approach to execute such workflows is to employ a single workflow engine which orchestrates the entire execution of a workflow instance, while being mostly agnostic about the state of the infrastructure it operates in (e.g., host or network load). Therefore, such centralized best-effort execution may use resources inefficiently -- for instance, repeatedly shipping large data volumes over slow network connections -- and cannot provide Quality of Service (QoS) guarantees. In particular, independent parallel executions might cause an overload of some resources, resulting in a performance degradation affecting all involved parties. In order to provide predictable behavior, we propose an approach where resources are managed proactively (i.e., reserved before being used), and where workflow execution is handled by multiple distributed and cooperating workflow engines. This allows to efficiently use the existing resources (for instance, using the most suitable provider for operations, and considering network locality for large data transfers) without overloading them, while at the same time providing predictability -- in terms of resource usage, execution timing, and cost -- for both service providers and customers. The contributions of this thesis are as follows. First, we present a system model which defines the concepts and operations required to formally represent a system where service providers are aware of the resource requirements of the operations they make available, and where (planned) workflow executions are adapted to the state of the infrastructure. Second, we describe our prototypical implementation of such a system, where a workflow execution comprises two main phases. In the planning phase, the resources to reserve for an upcoming workflow execution must be determined; this is realized using a Genetic Algorithm. We present conceptual and implementation details of the chromosome layout, and the fitness functions employed to plan executions according to one or more user-defined optimization goals. During the execution phase, the system must ensure that the actual resource usages abide to the reservations made. We present details on how such enforcement can be performed for various resource types. Third, we describe how these parts work together, and how the entire prototype system is deployed on an infrastructure based on WSDL/SOAP Web Services, UDDI Registries, and Glassfish Application Servers. Finally, we discuss the results of various evaluations, encompassing both the planning and runtime enforcement

Formal Modelling for Population Dynamics

The spirit of sustainable development has inspired our research work. Ecologically sus- tainable development needs preventative strategies and measures against environmental degradation. In our work we focus on constructing a formalism that enables modellers to model the population dynamics within an ecosystem and to analyse them. Furthermore, preventative strategies can be put into the model so that their effectiveness for ecosystems can be measured. An ecosystem consists of many interacting components. These components have many behaviours which are not easy to put together in a model. Work on such modelling started a long time ago, and even more has been done recently. These approaches have been taken from ordinary differential equations to stochastic processes. There are also some existing formalisms that have already been used for this modelling. In ecosystems there are several important aspects that need to be incorporated into the model, especially: stochasticity, spatiality and parallelism. One formalism has strengths in a certain aspect but weaknesses in others. Being motivated by this situation our work is to construct a formalism that could accommodate these aspects. Besides this, the formalism is intended to facilitate the modellers, who are generally biologists, to define the behaviours in the model in a more intuitive way. This has led our work to adopt features from existing formalisms: Cellular Automata and P Systems. Then, after adding new features, our work results in a new formalism called Grid Systems. Grid Systems have the spatiality of Cellular Automata but also provide a way to define behaviours differently in each cell (also called membrane) according to the reaction rules of P Systems. Therefore, Grid Systems have a richer spatiality compared to CA and the parallelism and stochaticity of P Systems. Besides these, we incorporate stochastic reaction duration for the reaction rules so that Grid Systems have stochasticity in rule selection and stochasticity in reaction termination. This enables us to define scheduled external events which are important aspects in modelling ecosystems. In addition to these, we extend Grid Systems with a new feature called ‘links’. A link is an object that can carry pointers. The pointer of a link can be used in the rule to transfer objects to another membrane. Because a link is also an object, its existence as well as its pointer are dynamic. By using the links, the membranes of Grid Systems can be structured as a tree to imitate the membrane structure of P Systems, or even more as a graph for a more general computation. The property of the links enables the structure to be dynamic, in a similar way to the dissolving membrane in the P Systems. The features of Grid Systems are defined in terms of syntax and semantics. The syntax describes how the model should be expressed by the modeller. The semantics describes what will happen to the model when the model evolves. From the semantics a software tool can be developed for analysing the model. In our research work we have developed the models in two case studies. In the first case study, we focus on the interacting events and external events that affect the population dynamics of mosquitoes. We observe how the impacts of events are propagated in space and time. In the second case study, we focus on the spatiality movement created by the seasonal migration of wildebeests. We observe that the pathways in the migration can be modelled well using links. The models of both case studies are analysed by using our simulation tool. From both case studies we conclude that our formalism can be used as a modelling framework especially for population dynamics, and in general for analysing the models of ecosystems

Eighth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, Aarhus, Denmark, October 22-24, 2007

This booklet contains the proceedings of the Eighth Workshop on Practical Use of Coloured Petri Nets and the CPN Tools, October 22-24, 2007. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark. The papers are also available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop0

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Infobiotics : computer-aided synthetic systems biology

Until very recently Systems Biology has, despite its stated goals, been too reductive in terms of the models being constructed and the methods used have been, on the one hand, unsuited for large scale adoption or integration of knowledge across scales, and on the other hand, too fragmented. The thesis of this dissertation is that better computational languages and seamlessly integrated tools are required by systems and synthetic biologists to enable them to meet the significant challenges involved in understanding life as it is, and by designing, modelling and manufacturing novel organisms, to understand life as it could be. We call this goal, where everything necessary to conduct model-driven investigations of cellular circuitry and emergent effects in populations of cells is available without significant context-switching, “one-pot” in silico synthetic systems biology in analogy to “one-pot” chemistry and “one-pot” biology. Our strategy is to increase the understandability and reusability of models and experiments, thereby avoiding unnecessary duplication of effort, with practical gains in the efficiency of delivering usable prototype models and systems. Key to this endeavour are graphical interfaces that assists novice users by hiding complexity of the underlying tools and limiting choices to only what is appropriate and useful, thus ensuring that the results of in silico experiments are consistent, comparable and reproducible. This dissertation describes the conception, software engineering and use of two novel software platforms for systems and synthetic biology: the Infobiotics Workbench for modelling, in silico experimentation and analysis of multi-cellular biological systems; and DNA Library Designer with the DNALD language for the compact programmatic specification of combinatorial DNA libraries, as the first stage of a DNA synthesis pipeline, enabling methodical exploration biological problem spaces. Infobiotics models are formalised as Lattice Population P systems, a novel framework for the specification of spatially-discrete and multi-compartmental rule-based models, imbued with a stochastic execution semantics. This framework was developed to meet the needs of real systems biology problems: hormone transport and signalling in the root of Arabidopsis thaliana, and quorum sensing in the pathogenic bacterium Pseudomonas aeruginosa. Our tools have also been used to prototype a novel synthetic biological system for pattern formation, that has been successfully implemented in vitro. Taken together these novel software platforms provide a complete toolchain, from design to wet-lab implementation, of synthetic biological circuits, enabling a step change in the scale of biological investigations that is orders of magnitude greater than could previously be performed in one in silico “pot”

Infobiotics : computer-aided synthetic systems biology

Until very recently Systems Biology has, despite its stated goals, been too reductive in terms of the models being constructed and the methods used have been, on the one hand, unsuited for large scale adoption or integration of knowledge across scales, and on the other hand, too fragmented. The thesis of this dissertation is that better computational languages and seamlessly integrated tools are required by systems and synthetic biologists to enable them to meet the significant challenges involved in understanding life as it is, and by designing, modelling and manufacturing novel organisms, to understand life as it could be. We call this goal, where everything necessary to conduct model-driven investigations of cellular circuitry and emergent effects in populations of cells is available without significant context-switching, “one-pot” in silico synthetic systems biology in analogy to “one-pot” chemistry and “one-pot” biology. Our strategy is to increase the understandability and reusability of models and experiments, thereby avoiding unnecessary duplication of effort, with practical gains in the efficiency of delivering usable prototype models and systems. Key to this endeavour are graphical interfaces that assists novice users by hiding complexity of the underlying tools and limiting choices to only what is appropriate and useful, thus ensuring that the results of in silico experiments are consistent, comparable and reproducible. This dissertation describes the conception, software engineering and use of two novel software platforms for systems and synthetic biology: the Infobiotics Workbench for modelling, in silico experimentation and analysis of multi-cellular biological systems; and DNA Library Designer with the DNALD language for the compact programmatic specification of combinatorial DNA libraries, as the first stage of a DNA synthesis pipeline, enabling methodical exploration biological problem spaces. Infobiotics models are formalised as Lattice Population P systems, a novel framework for the specification of spatially-discrete and multi-compartmental rule-based models, imbued with a stochastic execution semantics. This framework was developed to meet the needs of real systems biology problems: hormone transport and signalling in the root of Arabidopsis thaliana, and quorum sensing in the pathogenic bacterium Pseudomonas aeruginosa. Our tools have also been used to prototype a novel synthetic biological system for pattern formation, that has been successfully implemented in vitro. Taken together these novel software platforms provide a complete toolchain, from design to wet-lab implementation, of synthetic biological circuits, enabling a step change in the scale of biological investigations that is orders of magnitude greater than could previously be performed in one in silico “pot”