Programming Networks of Vehicles
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User Equipment Based-Computation Offloading for Real-Time Applications in the Context of Cloud and Edge Networks Farouk Messaoudi
User equipment based-computation offloading for real-time applications in the context of Cloud and edge networks Farouk Messaoudi To cite this version: Farouk Messaoudi. User equipment based-computation offloading for real-time applications inthe context of Cloud and edge networks. Networking and Internet Architecture [cs.NI]. Université Rennes 1, 2018. English. NNT : 2018REN1S104. tel-02268196 HAL Id: tel-02268196 https://tel.archives-ouvertes.fr/tel-02268196 Submitted on 20 Aug 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ANNÉE 2018 THÈSE / UNIVERSITÉ DE RENNES 1 sous le sceau de l’Université Bretagne Loire pour le grade de DOCTEUR DE L’UNIVERSITÉ DE RENNES 1 Mention : Informatique Ecole doctorale MathStic Farouk MESSAOUDI Préparée à l’unité de recherche (IRISA – UMR 6074) Institut de Recherche en Informatique et Système Aléatoires Université de Rennes 1 User equipment Thèse à soutenir à Rennes le 16 avril 2018 based computation devant le jury composé de : Houda LABIOD Professeur, Telecom ParisTech / examinateur Offloading for real- Riccardo -
A Smartphone Perspective on Computation Offloading
A Smartphone Perspective on Computation Offloading – A Survey Quang-Huy Nguyen1 , Falko Dressler2∗ 1Heinz Nixdorf Institute and Dept. of Computer Science, Paderborn University, Germany 2School of Electrical Engineering and Computer Science, TU Berlin, Germany Abstract Computation offloading has emerged as one of the promising approaches to address the issues of restricted resources, leading to poor user experiences on smartphones in terms of battery life and performance when executing CPU intensive tasks. Meanwhile, an entire research domain has been initiated around this topic and several concepts have been studied touching both the smartphone and the cloud side. In this paper, we develop a categorization of fundamental aspects regarding computation offloading in heterogeneous cloud computing from the perspective of smartphone applications. We refer to heterogeneity in terms of the multitude of smartphone applications, the various uplink channels, and the variety of cloud solutions. We also survey state-of-the-art solutions for the identified categories. Finally, we conclude with a summary of the most important research challenges in making computation offloading reality. Key words: Computation offloading, mobile edge computing, smartphones, energy consumption 1. Introduction the authors present brief overviews of the concept and summarize basic techniques, issues, and solutions regarding Smartphones are still considered to be resource-bounded to computation offloading. Besides, several studies have mobile computing devices despite a number of new de- been published, which only focused on single aspect of velopments and the evolution of their hardware. At the computation offloading problem. same time, user demands keep increasing rapidly with a For example, Hoque et al. [34] provide a taxonomy of lot of resource-intensive and power-hungry applications. -
A Practical Hardware Implementation of Systemic Computation
UNIVERSITY COLLEGE LONDON A Practical Hardware Implementation of Systemic Computation by Christos Sakellariou A thesis submitted in partial fulfilment for the degree of Doctor of Engineering in the Faculty of Engineering Department of Computer Science December 2013 2 Declaration of Authorship I, Christos Sakellariou, declare that this thesis A Practical Hardware Implementation of Systemic Computation and the work presented in it are my own. I confirm that: This work was done wholly or mainly while in candidature for a research degree at University College London. Where any part of this thesis has previously been submitted for a degree or any other qualification at University College London or any other institution, this has been clearly stated. Where I have consulted the published work of others, this is always clearly attributed. Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work. I have acknowledged all main sources of help. Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself. Signed: Date: 3 Abstract Faculty of Engineering, Department of Computer Science UNIVERSITY COLLEGE LONDON Doctor of Engineering by Christos Sakellariou It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. -
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Finding, Measuring, and Reducing Inefficiencies in Contemporary Computer Systems Melanie Kambadur Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2016 c 2016 Melanie Kambadur All Rights Reserved ABSTRACT Finding, Measuring, and Reducing Inefficiencies in Contemporary Computer Systems Melanie Kambadur Computer systems have become increasingly diverse and specialized in recent years. This complexity supports a wide range of new computing uses and users, but is not without cost: it has become difficult to maintain the efficiency of contemporary general purpose comput- ing systems. Computing inefficiencies, which include nonoptimal runtimes, excessive energy use, and limits to scalability, are a serious problem that can result in an inability to ap- ply computing to solve the world's most important problems. Beyond the complexity and vast diversity of modern computing platforms and applications, a number of factors make improving general purpose efficiency challenging, including the requirement that multiple levels of the computer system stack be examined, that legacy hardware devices and soft- ware may stand in the way of achieving efficiency, and the need to balance efficiency with reusability, programmability, security, and other goals. This dissertation presents five case studies, each demonstrating different ways in which the measurement of emerging systems can provide actionable advice to help keep general purpose computing efficient. The first of the five case studies is Parallel Block Vectors, a new profiling method for understanding parallel programs with a fine-grained, code-centric perspective aids in both future hardware design and in optimizing software to map better to existing hardware.