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Engineering Simulation with SimScale Drone Aerodynamics
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I discuss what career opportunities a PhD opens up with a focus on founding or joining a start-up. I gave this talk on 2016-05-16 at Career Day, organised by NTK at Umeå University, Sweden.
What to focus on during your PhD
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Cristian Klein
An Engineering Organization set up with the sole purpose of Enhancing Engineering in all its infinite ways that it gets executed world over through building up & nurturing “infiDOF Ecosystem” – Universal set of all those who operate in the Engineering Domain, e.g. OEMs, Service Providers, Training Institutes, Colleges, Resources, etc.
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Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either CPU or memory, while an application may need arbitrary combinations of these resources at different stages of its execution. Nonetheless, the existing techniques cannot be readily used as-is without proper orchestration since they may lead to either under- or over-provisioning of resources and consequently result in undesirable behaviors such as performance disparity. The contribution of this paper is the design of an autonomic resource controller using a fuzzy control approach as a coordination technique. The novel controller dynamically adjusts the right amount of CPU and memory required to meet the performance objective of an application, namely its response time. We perform a thorough experimental evaluation using three different interactive benchmark applications, RUBiS, RUBBoS, and Olio, under workload traces generated based on open and closed system models. The results show that the coordination of memory and CPU elasticity controllers using the proposed fuzzy control provisions the right amount of resources to meet the response time target without over-committing any of the resource types. In contrast, with no coordinating between controllers, the behaviour of the system is unpredictable e.g., the application performance may be met but at the expense of over-provisioning of one of the resources, or application crashing due to severe resource shortage as a result of conflicting decisions.
Coordinating CPU and Memory Elasticity Controllers toMeet Service Response T...
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Soodeh Farokhi
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The growth in popularity of the Internet, along with the rapid development of processing and storage technologies, has brought a paradigm shift in the way computing resources are provisioned. The technological trend today is to offer computing resources as services, leased and exposed via the Internet in a pay-as-you-go and on-demand fashion, called cloud computing... Cloud infrastructure providers are trying to reduce their operating costs while offering their services with higher quality; something they strive to do to stand out among other providers. However, this is becoming challenging as providing such services needs operating large-scale and geographically distributed data centers. On the other hand, the main purpose of customers in using clouds is to achieve a high quality of service (QoS) while reducing their overall costs. Given the variety of offered services in terms of quality and cost, customers are encouraged to simultaneously use services from multiple cloud providers, known as multi-cloud. However, utilizing multi-cloud brings a new set of open challenges, such as selecting and composing the most appropriate services. Furthermore, despite the critical need of customers in having predictable service performance, in general cloud providers do not yet offer any performance guarantees. This gap is due to the complexity of practically addressing this issue in a cost-effective way. Such a complexity mainly comes from the dynamic nature of the cloud, unpredictable workloads, and non-linearity of mapping performance measurements into required cloud resources. Hence, controlling the trade-off between QoS and cost is a challenging goal for both cloud infrastructure providers and customers. This thesis investigates models, algorithms, and mechanisms to tackle this trade-off from both perspectives. More specifically, in the scope of this thesis, we first take the cloud provider viewpoint by proposing an approach for virtual machine placement across geographically distributed infrastructures. In this approach, a Bayesian network model is used to address decision making under uncertainty. Then, we address the trade-off between QoS and cost from the cloud customer point of view by facilitating the utilization of the multi-cloud paradigm. We propose a service selection approach using prospect theory to rank the comparable service offerings. Furthermore, to guarantee the performance objectives of customers, we propose autonomic resource provisioning techniques. To this aim, control theory is used to design resource provisioning controllers, and fuzzy control is utilized to coordinate multiple controllers toward meeting the service performance objectives in a cost-effective manner. Finally, the evaluations of these contributions
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Recommended
I discuss what career opportunities a PhD opens up with a focus on founding or joining a start-up. I gave this talk on 2016-05-16 at Career Day, organised by NTK at Umeå University, Sweden.
What to focus on during your PhD
What to focus on during your PhD
Cristian Klein
An Engineering Organization set up with the sole purpose of Enhancing Engineering in all its infinite ways that it gets executed world over through building up & nurturing “infiDOF Ecosystem” – Universal set of all those who operate in the Engineering Domain, e.g. OEMs, Service Providers, Training Institutes, Colleges, Resources, etc.
infiDOF - An Overview
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Abhinay Kalburgi
Welcome to SimScale
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Sijia Ma
welcometosimscale-companypresentationv2-160420145834 (1)
welcometosimscale-companypresentationv2-160420145834 (1)
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F1 Certificate SimScale - Aero_april2016-78
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Venkat Praveen Gambhir
Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either CPU or memory, while an application may need arbitrary combinations of these resources at different stages of its execution. Nonetheless, the existing techniques cannot be readily used as-is without proper orchestration since they may lead to either under- or over-provisioning of resources and consequently result in undesirable behaviors such as performance disparity. The contribution of this paper is the design of an autonomic resource controller using a fuzzy control approach as a coordination technique. The novel controller dynamically adjusts the right amount of CPU and memory required to meet the performance objective of an application, namely its response time. We perform a thorough experimental evaluation using three different interactive benchmark applications, RUBiS, RUBBoS, and Olio, under workload traces generated based on open and closed system models. The results show that the coordination of memory and CPU elasticity controllers using the proposed fuzzy control provisions the right amount of resources to meet the response time target without over-committing any of the resource types. In contrast, with no coordinating between controllers, the behaviour of the system is unpredictable e.g., the application performance may be met but at the expense of over-provisioning of one of the resources, or application crashing due to severe resource shortage as a result of conflicting decisions.
Coordinating CPU and Memory Elasticity Controllers toMeet Service Response T...
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Soodeh Farokhi
オープンソースソルバを用いたクラウドCAEであるSimScaleについての概要説明
SimScale概要
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Code Lazward
The growth in popularity of the Internet, along with the rapid development of processing and storage technologies, has brought a paradigm shift in the way computing resources are provisioned. The technological trend today is to offer computing resources as services, leased and exposed via the Internet in a pay-as-you-go and on-demand fashion, called cloud computing... Cloud infrastructure providers are trying to reduce their operating costs while offering their services with higher quality; something they strive to do to stand out among other providers. However, this is becoming challenging as providing such services needs operating large-scale and geographically distributed data centers. On the other hand, the main purpose of customers in using clouds is to achieve a high quality of service (QoS) while reducing their overall costs. Given the variety of offered services in terms of quality and cost, customers are encouraged to simultaneously use services from multiple cloud providers, known as multi-cloud. However, utilizing multi-cloud brings a new set of open challenges, such as selecting and composing the most appropriate services. Furthermore, despite the critical need of customers in having predictable service performance, in general cloud providers do not yet offer any performance guarantees. This gap is due to the complexity of practically addressing this issue in a cost-effective way. Such a complexity mainly comes from the dynamic nature of the cloud, unpredictable workloads, and non-linearity of mapping performance measurements into required cloud resources. Hence, controlling the trade-off between QoS and cost is a challenging goal for both cloud infrastructure providers and customers. This thesis investigates models, algorithms, and mechanisms to tackle this trade-off from both perspectives. More specifically, in the scope of this thesis, we first take the cloud provider viewpoint by proposing an approach for virtual machine placement across geographically distributed infrastructures. In this approach, a Bayesian network model is used to address decision making under uncertainty. Then, we address the trade-off between QoS and cost from the cloud customer point of view by facilitating the utilization of the multi-cloud paradigm. We propose a service selection approach using prospect theory to rank the comparable service offerings. Furthermore, to guarantee the performance objectives of customers, we propose autonomic resource provisioning techniques. To this aim, control theory is used to design resource provisioning controllers, and fuzzy control is utilized to coordinate multiple controllers toward meeting the service performance objectives in a cost-effective manner. Finally, the evaluations of these contributions
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