2020
-
Avoiding local interference in IEEE 802.15.4 TSCH networks using a scheduling function with distributed blacklists
24. ITG-Symposium on Mobile Communication - Technologies and Applications: 46-51 (2020)
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Analytic study of packet delay from 4G and 5G system ARQs using Signal Flow Graphs
IEEE Vehicular Technology Conference: 9128411 (2020-05)
Open Access | Publisher DOI -
Online teaching of project-based learning courses : issues, challenges and outcomes
Open Access -
Predictive scheduling and opportunistic medium access for shared-spectrum radio systems in aeronautical communication
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.: Deutscher Luft- und Raumfahrtkongress 2020. - Dokument 530331 (2020)
Open Access | Publisher DOI
[137446] |
Title: Towards Prediction of Power Consumption of Virtual Machines for Varying Loads 28th International Telecommunication Networks and Applications Conference |
Written by: Humaira Abdul Salam and Franco Davoli and Alessandro Carrega and Andreas Timm-Giel |
in: 2019 |
Volume: Number: |
on pages: 1--6 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: 978-153867177-1 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: |
URL: http://hdl.handle.net/11420/2191 |
ARXIVID: |
PMID: |
Note:
Abstract: Power management and load balancing in data centers are becoming critical with the growing size of the infrastructure. In front of an increasing number of cloud networks, virtual machines (VMs) and containers, smart management and control decisions are required, in order to instantiate or mobilize these virtual components. As regards reducing power consumption, it is also essential to consolidate virtual resources on the minimum possible number of servers compatible with performance requirements. In such a dynamic scenario, estimating the power that can be ascribed to a specific virtual component and its relation with the offered workload could be beneficial for optimized resource scheduling. Predicting the power consumption caused by a specific virtual component is however challenging. In this paper, two power models that relate a VM workload with the fraction of power consumed attributable to the VM are developed based on power profiling of a server. The accuracy of the proposed models is improved approximately by 3% compared to other existing models. © 2018 IEEE.
[137446] |
Title: Towards Prediction of Power Consumption of Virtual Machines for Varying Loads 28th International Telecommunication Networks and Applications Conference |
Written by: Humaira Abdul Salam and Franco Davoli and Alessandro Carrega and Andreas Timm-Giel |
in: 2019 |
Volume: Number: |
on pages: 1--6 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: 978-153867177-1 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: |
URL: http://hdl.handle.net/11420/2191 |
ARXIVID: |
PMID: |
Note:
Abstract: Power management and load balancing in data centers are becoming critical with the growing size of the infrastructure. In front of an increasing number of cloud networks, virtual machines (VMs) and containers, smart management and control decisions are required, in order to instantiate or mobilize these virtual components. As regards reducing power consumption, it is also essential to consolidate virtual resources on the minimum possible number of servers compatible with performance requirements. In such a dynamic scenario, estimating the power that can be ascribed to a specific virtual component and its relation with the offered workload could be beneficial for optimized resource scheduling. Predicting the power consumption caused by a specific virtual component is however challenging. In this paper, two power models that relate a VM workload with the fraction of power consumed attributable to the VM are developed based on power profiling of a server. The accuracy of the proposed models is improved approximately by 3% compared to other existing models. © 2018 IEEE.
[137446] |
Title: Towards Prediction of Power Consumption of Virtual Machines for Varying Loads 28th International Telecommunication Networks and Applications Conference |
Written by: Humaira Abdul Salam and Franco Davoli and Alessandro Carrega and Andreas Timm-Giel |
in: 2019 |
Volume: Number: |
on pages: 1--6 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: 978-153867177-1 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: |
URL: http://hdl.handle.net/11420/2191 |
ARXIVID: |
PMID: |
Note:
Abstract: Power management and load balancing in data centers are becoming critical with the growing size of the infrastructure. In front of an increasing number of cloud networks, virtual machines (VMs) and containers, smart management and control decisions are required, in order to instantiate or mobilize these virtual components. As regards reducing power consumption, it is also essential to consolidate virtual resources on the minimum possible number of servers compatible with performance requirements. In such a dynamic scenario, estimating the power that can be ascribed to a specific virtual component and its relation with the offered workload could be beneficial for optimized resource scheduling. Predicting the power consumption caused by a specific virtual component is however challenging. In this paper, two power models that relate a VM workload with the fraction of power consumed attributable to the VM are developed based on power profiling of a server. The accuracy of the proposed models is improved approximately by 3% compared to other existing models. © 2018 IEEE.
[137446] |
Title: Towards Prediction of Power Consumption of Virtual Machines for Varying Loads 28th International Telecommunication Networks and Applications Conference |
Written by: Humaira Abdul Salam and Franco Davoli and Alessandro Carrega and Andreas Timm-Giel |
in: 2019 |
Volume: Number: |
on pages: 1--6 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: 978-153867177-1 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: |
URL: http://hdl.handle.net/11420/2191 |
ARXIVID: |
PMID: |
Note:
Abstract: Power management and load balancing in data centers are becoming critical with the growing size of the infrastructure. In front of an increasing number of cloud networks, virtual machines (VMs) and containers, smart management and control decisions are required, in order to instantiate or mobilize these virtual components. As regards reducing power consumption, it is also essential to consolidate virtual resources on the minimum possible number of servers compatible with performance requirements. In such a dynamic scenario, estimating the power that can be ascribed to a specific virtual component and its relation with the offered workload could be beneficial for optimized resource scheduling. Predicting the power consumption caused by a specific virtual component is however challenging. In this paper, two power models that relate a VM workload with the fraction of power consumed attributable to the VM are developed based on power profiling of a server. The accuracy of the proposed models is improved approximately by 3% compared to other existing models. © 2018 IEEE.
[137446] |
Title: Towards Prediction of Power Consumption of Virtual Machines for Varying Loads 28th International Telecommunication Networks and Applications Conference |
Written by: Humaira Abdul Salam and Franco Davoli and Alessandro Carrega and Andreas Timm-Giel |
in: 2019 |
Volume: Number: |
on pages: 1--6 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: 978-153867177-1 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: |
URL: http://hdl.handle.net/11420/2191 |
ARXIVID: |
PMID: |
Note:
Abstract: Power management and load balancing in data centers are becoming critical with the growing size of the infrastructure. In front of an increasing number of cloud networks, virtual machines (VMs) and containers, smart management and control decisions are required, in order to instantiate or mobilize these virtual components. As regards reducing power consumption, it is also essential to consolidate virtual resources on the minimum possible number of servers compatible with performance requirements. In such a dynamic scenario, estimating the power that can be ascribed to a specific virtual component and its relation with the offered workload could be beneficial for optimized resource scheduling. Predicting the power consumption caused by a specific virtual component is however challenging. In this paper, two power models that relate a VM workload with the fraction of power consumed attributable to the VM are developed based on power profiling of a server. The accuracy of the proposed models is improved approximately by 3% compared to other existing models. © 2018 IEEE.
[137446] |
Title: Towards Prediction of Power Consumption of Virtual Machines for Varying Loads 28th International Telecommunication Networks and Applications Conference |
Written by: Humaira Abdul Salam and Franco Davoli and Alessandro Carrega and Andreas Timm-Giel |
in: 2019 |
Volume: Number: |
on pages: 1--6 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: 978-153867177-1 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: |
URL: http://hdl.handle.net/11420/2191 |
ARXIVID: |
PMID: |
Note:
Abstract: Power management and load balancing in data centers are becoming critical with the growing size of the infrastructure. In front of an increasing number of cloud networks, virtual machines (VMs) and containers, smart management and control decisions are required, in order to instantiate or mobilize these virtual components. As regards reducing power consumption, it is also essential to consolidate virtual resources on the minimum possible number of servers compatible with performance requirements. In such a dynamic scenario, estimating the power that can be ascribed to a specific virtual component and its relation with the offered workload could be beneficial for optimized resource scheduling. Predicting the power consumption caused by a specific virtual component is however challenging. In this paper, two power models that relate a VM workload with the fraction of power consumed attributable to the VM are developed based on power profiling of a server. The accuracy of the proposed models is improved approximately by 3% compared to other existing models. © 2018 IEEE.
[137446] |
Title: Towards Prediction of Power Consumption of Virtual Machines for Varying Loads 28th International Telecommunication Networks and Applications Conference |
Written by: Humaira Abdul Salam and Franco Davoli and Alessandro Carrega and Andreas Timm-Giel |
in: 2019 |
Volume: Number: |
on pages: 1--6 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: 978-153867177-1 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: |
URL: http://hdl.handle.net/11420/2191 |
ARXIVID: |
PMID: |
Note:
Abstract: Power management and load balancing in data centers are becoming critical with the growing size of the infrastructure. In front of an increasing number of cloud networks, virtual machines (VMs) and containers, smart management and control decisions are required, in order to instantiate or mobilize these virtual components. As regards reducing power consumption, it is also essential to consolidate virtual resources on the minimum possible number of servers compatible with performance requirements. In such a dynamic scenario, estimating the power that can be ascribed to a specific virtual component and its relation with the offered workload could be beneficial for optimized resource scheduling. Predicting the power consumption caused by a specific virtual component is however challenging. In this paper, two power models that relate a VM workload with the fraction of power consumed attributable to the VM are developed based on power profiling of a server. The accuracy of the proposed models is improved approximately by 3% compared to other existing models. © 2018 IEEE.