EAI Endorsed Transactions on Energy Web

Issue 10, 2016

Editor(s)-in-Chief: Mihaela Ulieru and Valeriy Vyatkin

Articles
Information

Analysis of the Energy-Performance Tradeoff for Delayed Mobile Offloading
Appears in:
ew 16(10): e1

Authors:
Huaming Wu, Katinka Wolter

Abstract:
Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices for higher performance and energy saving. Different applications usually give different relative importance t…Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices for higher performance and energy saving. Different applications usually give different relative importance to the factors of response time and energy consumption. In this paper, we investigate two types of delayed offloading policies, the partial model where jobs can leave from the slow phase of the offloading process and then executed locally on the mobile device, and the full offloading model, where jobs can abandon the WiFi Queue and then offloaded via the Cellular Queue. In both models we minimise the Energy-Response time Weighted Product (ERWP) metric. We find that jobs abandon the queue very often especially when the availability ratio (AR) of the WiFi network is relatively small. We can optimally choose the reneging deadline to achieve different energy-performance tradeoff by optimizing the ERWP metric. The amount of delay a job can tolerate closely depends on the application type and the potential energy saving for the mobile device. In general one can say that for delay-sensitive applications, the partial offloading model is preferred when having a suitable reneging rate, while for delay-tolerant applications, the full offloading model shows very good results and outperforms the other offloading models when setting the deadline a large value.
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Centralized Mobile Users Assignment Strategies for Energy Management in Het-LTE Networks
Appears in:
ew 16(10): e2

Authors:
Dominique Barth, Amira Choutri, Leila Kloul, Olivier Marce

Abstract:
Power consumption control paradigm constitutes an incentive solution for the serious energy issues faced by operators. In this work, we investigate two mobile users assignment strategies for energy management in heterogeneous LTE networks. Both strategies are based on greedy algorithms which are of…Power consumption control paradigm constitutes an incentive solution for the serious energy issues faced by operators. In this work, we investigate two mobile users assignment strategies for energy management in heterogeneous LTE networks. Both strategies are based on greedy algorithms which are of low complexity and allow on-line approaches suitable for femtocells management issue. Moreover, as the femtocells can be switched off and the macrocells have to remain always on, both strategies aim at selecting the femtocells to deactivate. That is, the mobile user may be moved from a femtocell to a macrocell or the other way around. The move is legitimate if the power consumption is lower and only if the QoS of the user is not sacrificed. Using simulation and real network data, we assess the performances of proposed strategies, in terms of energy consumption and efficiency of the network, the percentage of served mobile users, the real allocated capacity and the number of deactivated femtocells.
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A tool based on traffic traces and stochastic monotonicity to analyze data centers and their energy consumption
Appears in:
ew 16(10): e3

Authors:
Marziyeh Bayati, Mohammed Dahmoune, Jean-Michel Fourneau, Nihal Pekergin, Dimitrios Vekris

Abstract:
We present a tool to study the trade-off between energy consumption and performance evaluation. The tool uses real traffic traces to model arrivals, and it allows to consider general discrete arrival processes. Some servers are switched on (resp. off) if the monitored QoS becomes less (resp. m…We present a tool to study the trade-off between energy consumption and performance evaluation. The tool uses real traffic traces to model arrivals, and it allows to consider general discrete arrival processes. Some servers are switched on (resp. off) if the monitored QoS becomes less (resp. more) than the {\it up} (resp. {\it down}) threshold. A set of threshold couples and the cost function taking into account both the performance measure and the energy consumption are provided by the user. The tool determines the best one for this cost function among the analyzed scenarios. Our method is numerically based but it takes into account some stochastic properties of the model to speed up the computation.
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Univariate Interpolation-based Modeling of Power and Performance
Appears in:
ew 16(10): e4

Authors:
Jóakim von Kistowski, Samuel Kounev

Abstract:
Performance and power scale non-linearly with device utilization, making characterization and prediction of energy efficiency at a given load level a challenging issue. A common approach to address this problem is the creation of power or performance state tables for a pre-measured subset of all po…Performance and power scale non-linearly with device utilization, making characterization and prediction of energy efficiency at a given load level a challenging issue. A common approach to address this problem is the creation of power or performance state tables for a pre-measured subset of all possible system states. Approaches to determine performance and power for a state not included in the measured subset use simple interpolation, such as nearest neighbor interpolation, or define state switching rules. This leads to a loss in accuracy, as unmeasured system states are not considered. In this paper, we compare different interpolation functions and automatically configure and select functions for a given domain or measurement set. We evaluate our approach by comparing interpolation of measurement data subsets against power and performance measurements on a commodity server. We show that for non-extrapolating models interpolation is significantly more accurate than regression, with our automatically configured interpolation function improving modeling accuracy up to 43.6%.
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Average Case Analysis of the MST-heuristic for the Power Assignment Problem: Special Cases
Appears in:
ew 16(10): e5

Authors:
Maurits de Graaf, Richard Boucherie, Johann Hurink, Jan-Kees van Ommeren

Abstract:
We present an average case analysis of the minimum spanning tree heuristic for the power assignment problem. The worst-case approximation ratio of this heuristic is 2. We have the following results: (a) In the one-dimension\-al case, with uniform $\left[ 0,1 \right]$-distributed distances, the expe…We present an average case analysis of the minimum spanning tree heuristic for the power assignment problem. The worst-case approximation ratio of this heuristic is 2. We have the following results: (a) In the one-dimension\-al case, with uniform $\left[ 0,1 \right]$-distributed distances, the expected approximation ratio is bounded above by $2 - 2/(\myp+2)$, where $\myp$ denotes the distance power gradient. (b) For the complete graph, with uniform $[0,1]$ distributed edge weights, the expected approximation ratio is bounded above by $2-1/2\zeta(3)$, where $\zeta$ denotes the Riemann zeta function.
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Scope

The International Energy Web Journal is positioned at the forefront of these efforts as a forum for the most forward looking frontier research undertaken by the international scientific community. We will publish original works reporting on prominent advances that challenge traditional thinking to …

Indexing

Editors-in-Chief
Albert Lam, University of Hong Kong, HK
Area Editors
Mikhail Medvedev (Southern Federal University, Russia)
Vladimir Dyo (University of Bedfordshire, UK)
Andrey Mazalov (South Federal University, Russia)
Elena Gebel (Omsk State Technical University, Russia)
Irina Se…

Editors-in-Chief
Albert Lam, University of Hong Kong, HK
Area Editors
Mikhail Medvedev (Southern Federal University, Russia)
Vladimir Dyo (University of Bedfordshire, UK)
Andrey Mazalov (South Federal University, Russia)
Elena Gebel (Omsk State Technical University, Russia)
Irina Semykina (T. F. Gorbachev Kuzbass State Technical University, Russia)
Vyacheslav Potekhin (Peter the Great St. Petersburg Polytechnic University, Russia)
Sabu Thampi (Indian Institute of Information Technology and Management-Kerala (IIITM-K), India)
Manik Sharma (DAV University, India)
Amando P. Singun JR. (University of Technology and Applied Sciences, Muscat, Oman)
Irina Semykina (Sevastopol State University, Ukraine)
Abel C. Chen (Chunghwa Telecom Laboratories, Taiwan)
Vitor Monteiro (University of Minho, Portugal)
Joao L. Afonso (University of Minho, Portugal)
Abdul Shabeer (IBM India Pvt Ltd, Chennai, India)
Anandakumar H (Sri Eshwar College of Engineering, India)
Marta Chinnici (ENEA, Italy)
Howard NJOKU (University of Nigeria, Nigeria)
José Gabriel Oliveira Pinto (University of Minho, Portugal)
P Krishna (VIT University, India)
Jacinth Poornima (Hindusthan College of Engineering and Technology, India)
Karthikumar S (Anna University)
Duckki Lee (Yonam Institute of Technology, South Korea)
A. Hnatov (Kharkiv National Automobile and Highway University, Ukraine)
Dr. P. Karthikeyan (Jain University, India)
Sagheer Abbas (National College of Business Administration and Economics, Pakistan)
Ahmed Elngar (Beni-Suef University, Egypt)
Debnath Bhattacharyya (KL University, India)
Jatinder Manhas (University of Jammu, India)
Michele Mastroianni (University of Salerno, Italy)
Hitesh Mohapatra (KIIT University, India)
Brojo Kishore Mishra (NIST Berhampur, India)
Chin-Ling Chen (Chaoyang University of Technology, Taiwan)
Sudhakar Sengan (PSN College of Engineering and Technology, India)
Haris M. Khalid (University of Dubai, United Arab Emirates)
Emeritus Editors-in-Chief
Ghazanfar Safdar, University of Bedfordshire, UK

Journal Blurb

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/ew/index

Publisher: EAI
ISSN: 2032-944X
Volume: 3

Published: 2016-12-12

Analysis of the Energy-Performance Tradeoff for Delayed Mobile Offloading

Centralized Mobile Users Assignment Strategies for Energy Management in Het-LTE Networks

A tool based on traffic traces and stochastic monotonicity to analyze data centers and their energy consumption

Univariate Interpolation-based Modeling of Power and Performance

Average Case Analysis of the MST-heuristic for the Power Assignment Problem: Special Cases

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