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![Figure 1. Simple weather forecast workflow [2]](profile/Vassilios-Moussas/publication/280939531/figure/fig1/AS:613900869439492@1523376781400/Simple-weather-forecast-workflow-2_Q320.jpg)
Simulation workflow scheduling becomes an important area as it allows users to process large scale & heterogeneous problems in a more flexible way. In most complex simulation workflows the user has to select the optimal use of local and external resources that will satisfy its requirements under the specific time & cost constraints thus involving m...
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The increased interest on processing large scale & heterogeneous problems in distributed environments created the need of software tools that would support such complex workflows. Especially, simulation workflow scheduling has become an important area as it allows users to process large scale problems in a more flexible way. In most complex simulat...
Citations
... A weather forecast example provided by a previous study indicated the basic workflow when the simulation is decomposed into a process-oriented pipeline (Tsahalis et al. 2013). Weather research shares conceptual similarities to UHI, and thus, their example is applied here as a base version of the conventional workflow. ...
Smart cities evolve rapidly along with the technical advances in wireless and sensor networks, information science, and human–computer interactions. Urban computing provides the processing power to enable the integration of such technologies to improve the living quality of urban citizens, including health care, urban planning, energy, and other aspects. This chapter uses different computing capabilities, such as cloud computing, mobile computing, and edge computing, to support smart cities using the urban heat island of the greater Washington DC area as an example. We discuss the benefits of leveraging cloud, mobile, and edge computing to address the challenges brought by the spatiotemporal dynamics of the urban heat island, including elevated emissions of air pollutants and greenhouse gases, compromised human health and comfort, and impaired water quality. Cloud computing brings scalability and on-demand computing capacity to urban system simulations for timely prediction. Mobile computing brings portability and social interactivity for citizens to report instantaneous information for better knowledge integration. Edge computing allows data produced by in-situ devices to be processed and analyzed at the edge of the network, reducing the data traffic to the central repository and processing engine (data center or cloud). Challenges and future directions are discussed for integrating the three computing technologies to achieve an overall better computing infrastructure supporting smart cities. The integration is discussed in aspects of bandwidth issue, network access optimization, service quality and convergence, and data integrity and security.
... Genetic algorithms (GAs) provide robust search techniques that allow a high-quality solution to be derived from a large search space in polynomial time, by applying the principle of evolution. A successful application of GAs for workflow optimization in the aerospace manufacturing domain is also presented in [9]. ...
Simulation workflow optimization has become an important investigation area, as it allows users to process large scale & heterogeneous problems in distributed environments in a more flexible way. The most characteristic categories of such problems come from the aerospace and the automotive industries. In this work a specially developed algorithm that is based on heuristic optimization techniques (Genetic Algorithms) is applied to deliver an optimized workflow implementation of an initial workflow schedule (PERT). In order to demonstrate its potentials, the algorithm is applied on a sample manufacturing product design problem that requires a lot of time consuming simulations & finite elements analysis under a constrained availability of computer resources.
... Example optimization loop involving heterogeneous simulation tasks in its workflow[4].Workflow description: The perceived comfort is evaluated by a Human Response Model (HRM), provided as a web-service by external partner C. The HRM requires inputs of temperature, air flow, humidity and pressure (ENV) from the Cabin CFD model and noise and vibration (N&V) inputs from the Cabin FEM model. The ENV results of the Cabin CFD model are calculated based on its own operational characteristics in combination with those of the ECS electrical/thermal model from external partner A. The N&V results of the Cabin FEM model are calculated based on its own operational characteristics in combination with those of the power plant FEM model from external partner B. Different settings and configurations for each of the four models (FEM, CFD, electric/thermal) are considered based on the optimization loop algorithm and the available constraints, the results of which are then evaluated by the HRM, leading to selection of new values for calculation and evaluation. ...
The increased interest on processing large scale & heterogeneous problems in distributed environments created the need of software tools that would support such complex workflows. Especially, simulation workflow scheduling has become an important area as it allows users to process large scale problems in a more flexible way. In most complex simulation workflows the user has to select the optimal use of local and external resources that will satisfy its requirements under the specific time & cost constraints. In this work we present a Simulation Workflow Optimization (SWO) algorithm that is based on heuristic optimization techniques (Genetic Algorithms) and delivers an optimized workflow implementation of an initial plan or workflow schedule. The aim of SWO is to address the increased complexity encountered when one or more distributed & heterogeneous processes are involved in a simulation workflow. A heterogeneous simulation workflow contains several virtual tasks that involve completely different software tools, resources, requirements and often contradictory objectives. In addition, the distributed environment of large scale problems requires the software tools to be accessible from anywhere as been local. In order to support remotely the solution of each specific optimization problem, the SWO algorithm is developed as: a) a web based tool designed to function in a distributed environment and invoked using web services, and b) a tool that can be specialized per task, domain, product or application by means of knowledge bases, ontologies and user provided information.
... Under iProd framework a special tool for Simulation Workflow Optimization (SWO) was developed in order to support the optimization of virtual (simulation) workflows in cases of distributed and heterogeneous networks of collaborating systems. The aim is to present a flexible web based tool [3] that will be able to promote a simulation workflow optimization method, make it available to a remote application or another service, and support a wider automated collaboration between heterogeneous design & simulation tools (figure 2). Figure 2. The role of SWO in the PDP process A more detailed description of the SWO module can be found in [4]. In this work we will present the results of ...
Simulation workflow optimization has become an important investigation area as it allows users to process large scale & heterogeneous problems in distributed environments in a more flexible way. The most characteristic category of such problems comes from the aerospace industry. In this work a specially developed Simulation Workflow Optimization (SWO) algorithm that is based on heuristic optimization techniques (Genetic Algorithms) and delivers an optimized workflow implementation of an initial plan or workflow schedule, will be applied on an aerospace manufacturing problem in order to demonstrate its potentials. The algorithm has been developed under the iProd EU project and the application use case refers to the manufacturing of an airplane tail rudder from FAE. The SWO tool helps the user to select the optimal use of local and external resources that will satisfy the product requirements under the specific time & cost constraints. The tool is customized for the specific domain/application and it is remotely invoked via web GUI & services under the iProd collaborative framework.
