models in the cloud

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Models in the Cloud – Prospects and Risks of Cloud-Based Modeling and Simulation Services

Thomas Neidhold, Olaf Oelsner and Christian Kehrer ITI, Dresden, Germany

KurzfassungCloud Computing gehört aktuell zu den interessantesten aber auch strittigsten The-men in der IT-Branche. Zum einen stellt diese Technologie einen wichtigen Faktor in einer immer stärker globalisierten und vernetzten Wirtschaft dar. Dem gegenüber stehen massive Sicherheitsbedenken, die bei potentiellen Anwendern, gerade nach den Ereignissen der letzten Monate, nur schwer auszuräumen sind. Gemeinsam mit Partnern aus Industrie und Forschung untersucht ITI innerhalb zweier Projekte, die durch das Bundesministerium für Wirtschaft (Cloud4E [1]) bzw. durch die Europäische Union (CloudFlow [2]) gefördert werden und die erforschen, wie diese Technologie im CAE-Bereich erfolgreich eingesetzt werden kann. Neben den technischen Grund-lagen stehen dabei vor allem Sicherheitsaspekte im Vordergrund. Aber auch die mög-lichen Anwendungsfälle sowie beiderseitig akzeptable Geschäftsmodelle werden im Rahmen dieser Projekte untersucht.

AbstractCloud computing today is one of the top topics widely discussed – not without con-troversy – throughout the IT industry. On the one hand, this technology is an import-ant factor in an increasingly globalized and interconnected world. On the other hand, there are almost insurmountable security concerns with potential users, especially after recent events. Together with partners from various industries and research insti-tutions, ITI is involved in two projects that are funded by the German Federal Ministry of Economics and Technology (Cloud4E [1]) and the European Union (CloudFlow [2]) in order to assess the potential of how this technology can be used in the area of CAE. Apart from technological basics, it is primarily security aspects that are at the heart of those projects. But also potential applications as well as viable business models are under evaluation.

Introduction“Cloud computing is the dynamic provision, utilization and billing of IT services on demand over a network. Such services are offered and used via defined technological interfaces and protocols. The spectrum of cloud-based services covers all IT areas including infrastructure (e.g. computing capacity, storage), platforms and software.” (Federal Office for Information Security (BMI), 2013).

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1 Keynotes and Plenary lectures

Common platforms for uploading and sharing pictures and videos make as much use of this technology as various other offerings on the internet. Pictures uploaded in social networks can be viewed, managed and edited in any web browser without the need for an installation of additional software applications and are accessible from anywhere in the world, on any computer and by any user. In order to prevent un-authorized access to those pictures, registration and authentication are required for each service provider.

This principle can also be applied to other areas where data is transferred and rele-vant services for processing such data are offered. In regard to CAE, this means that computing capacities as well as infrastructure are provided for storing and managing models, parameters and results over a network. Based on this consideration, the idea of “Model in the Cloud” was born.

Model in the Cloud The goal is to save simulation models on a network and to offer services that let users specify parameters, run simulations and analyze results. For the time being, the modeling process remains a typical desktop scenario with SimulationX. In order to transfer models into a platform independent format, the code export feature in Simu-lationX comes into play. It permits generating executable code from any simulation model. During code export, it is possible to define parameters for later editing and the required result variables. The code export functionality supports a wide range of target systems. Executable model as well as Functional Mockup Unit (FMU) are the basic options for code export. The latter comes already with an API for FMI [3] that improves the integration of generated code into a cloud environment.

Figure 1: Model in the Cloud

Services that are intended for models stored in the cloud must cover several scenarios some of which are outlined below.

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ProsPects and risks of cloud-Based Modeling and siMulation services

Single simulationThe definition of a single simulation includes a parameter set and a specified number of results. No further input is required, and the simulation can run unattended.

Interactive single simulationInteractive simulations communicate with the user over defined inputs and outputs during the computation process. This requires the user to always be connected to the simulation service in the cloud throughout the simulation. Interactive single simula-tions determine additional connections between the model inputs and data sources (e.g. actuators) and between outputs and data sinks (e.g. sensors).

Variation calculationVariation calculations define the parameter variations for a model that are to be simu-lated. Simultaneous simulations of different parameter variations on multi-core CPUs or on multiple computers can reduce the computation speed significantly. This is an-other advantage of cloud computing. It is not necessary to manage resources locally. They can be leased as required.

Iterative simulationThe definition of an iterative simulation (e.g. for optimization) comprises the itera-tion method and the abort criteria. The iteration method describes the definition of parameter sets. Parameter sets are generated until an abort criterion is met. Simulta-neous computations can deliver results in a much shorter time than local simulations.

Co-simulationConnecting inputs and outputs of several models creates a more complex structure that can be simulated via co-simulation. And also here, simultaneous computation of the sub-models results in an advantage in computational speed.

Simulation serviceThe simulation service controls an application’s simulation. The client runs on the user’s computer and communicates with the simulation service in the cloud.

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Figure 2: Architecture of a simulation application in the cloud

During the start, the worker service loads the model, initiates the simulation parame-ters and commences the simulation. The number of results during the simulation is saved in the cloud. After the computation, the worker services are terminated. Hence, worker services only need resources when a simulation is actually running.

The client can access results during and after the simulation by selecting the corre-sponding session and simulation instance. As each start of an application generates a new session, multiple users can simulate simultaneously within one application.

Graphical user interface (GUI)Accessing simulation services is meant to be possible with any browser. It is therefore important to develop web clients that allow for entering and analyzing all necessary data in an intelligible and efficient way. Due to the rise of mobile devices in the work-place, it is advisable to also support those systems. This enables sales staff in the field to run and discuss scenarios with clients by the help of models stored in the cloud, which requires only a mobile device that is connected to internet. The sales engineer does not need to be highly experienced with SimulationX, nor does this method re-quire a license for the software.

SecurityIt is understood that access to simulation models and model data in the cloud can and must be restricted. Consequently, users of such services are required to register and authenticate themselves. An integrated user and rights management system controls access to and transfer of shared data.

Saving data in a provider’s random data center at an unknown location is a particular-ly delicate topic. Users of cloud services are usually unable to verify the integrity and protection of their data against loss or unauthorized access. It is therefore necessary to establish a mutual trust between the software developer, the infrastructure provider(s) and the client. Regional IT service providers may have an edge over big players, such as Amazon or Microsoft, since transfer channels and storage locations are easier to

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manage and to check. And there is, of course, always the option to set up a privat cloud environment instead. This has the advantage that all relevant data remains in the company’s own data center. The only vulnerable points left in this scenario are the transfer channels which can be shielded by encrypted connections.

Business modelIt is a given that any cloud-based solution must be profitable for CAE service provi-ders. For software developers like ITI, offering further modeling and simulation ser-vices beyond the traditional business model is a challenge and opportunity alike. A decisive factor is the level of acceptance by customers with respect to technical as-pects and the billing process for the purchased services. Costs are incurred by both using the infrastructure (processors, storage and computing capacity) and using the actual service itself. All costs are paid directly to the service provider as the customer’s contractual business partner. Similar to payment methods of mobile carriers, various approaches are possible:

• Pay per use: Customers pay exactly for what they use, e.g. numbers of CPUs, computation time in minutes, storage in gigabyte

• Flat rate: Customers are guaranteed a certain service for a fixed fee. This could also be part of a maintenance agreement.

A comprehensive consideration of these and other potential payment methods, ho-wever, must include all relevant factors. There are several evaluation tools that are well established for assessing new business models. The so-called Business Model Canvas (see Figure 3) calculates the financial profitability of a business model as the quotient of revenue stream and cost structure. While the right side illustrates the value of a particular service or good, the left side is focused on the efficiency of ser-vice provision. The frame is then filled with answers to questions about offers (value propositions), target groups (segments, relations, sales channels) and the required infrastructure (activities, resources, partnerships). The final step is to choose the best business case and specify cost and billing models, warranty and other factors.

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Figure 3: Business Model Canvas by A. Osterwalder, 2010 „Business Model Generation“

On this basis, it is possible to quantify the added value of cloud solutions compared to traditional on-premises license models while paving the way to new target groups without interfering with existing revenue streams.

Outlook and SummaryCloud solutions for CAE permit greater flexibility in using modeling and simulation tools. Applications based on models that are stored in the cloud can reach a wider audience while keeping the price below full-blown desktop solutions. Tool developers gain access to new markets where customers are more open to newly developed cloud solutions rather than pricy traditional tools. Despite these advantages, the suc-cess of cloud computing for engineering applications in the long run depends heavily on aspects of security, reliability and usability.

Sources[1] http://www.cloud4e.de/

[2] http://www.eu-cloudflow.eu

[3] https://www.fmi-standard.org/

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Cloud4E – Trusted Cloud Computing for EngineeringCloudFlow

The research leading to these results has received funding from the European Com-munity‘s Seventh Framework Programme (FP7-2013-NMP-ICT-FoF) under grant agreement n° 609100.