Auswahl der wissenschaftlichen Literatur zum Thema „Edge Computation Offloading“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Inhaltsverzeichnis
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Edge Computation Offloading" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "Edge Computation Offloading"
Patel, Minal Parimalbhai, und Sanjay Chaudhary. „Edge Computing“. International Journal of Fog Computing 3, Nr. 1 (Januar 2020): 64–74. http://dx.doi.org/10.4018/ijfc.2020010104.
Der volle Inhalt der QuelleMan, Junfeng, Longqian Zhao, Bowen Xu, Cheng Peng, Junjie Jiang und Yi Liu. „Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode“. Journal of Database Management 34, Nr. 1 (24.02.2023): 1–29. http://dx.doi.org/10.4018/jdm.318451.
Der volle Inhalt der QuelleXiao, Yong, Ling Wei, Junhao Feng und Wang En. „Two-tier end-edge collaborative computation offloading for edge computing“. Journal of Computational Methods in Sciences and Engineering 22, Nr. 2 (28.03.2022): 677–88. http://dx.doi.org/10.3233/jcm-215923.
Der volle Inhalt der QuelleShan, Nanliang, Yu Li und Xiaolong Cui. „A Multilevel Optimization Framework for Computation Offloading in Mobile Edge Computing“. Mathematical Problems in Engineering 2020 (27.06.2020): 1–17. http://dx.doi.org/10.1155/2020/4124791.
Der volle Inhalt der QuelleLin, Li, Xiaofei Liao, Hai Jin und Peng Li. „Computation Offloading Toward Edge Computing“. Proceedings of the IEEE 107, Nr. 8 (August 2019): 1584–607. http://dx.doi.org/10.1109/jproc.2019.2922285.
Der volle Inhalt der QuelleMaftah, Sara, Mohamed El Ghmary, Hamid El Bouabidi, Mohamed Amnai und Ali Ouacha. „Intelligent task processing using mobile edge computing: processing time optimization“. IAES International Journal of Artificial Intelligence (IJ-AI) 13, Nr. 1 (01.03.2024): 143. http://dx.doi.org/10.11591/ijai.v13.i1.pp143-152.
Der volle Inhalt der QuelleLi, Feixiang, Chao Fang, Mingzhe Liu, Ning Li und Tian Sun. „Intelligent Computation Offloading Mechanism with Content Cache in Mobile Edge Computing“. Electronics 12, Nr. 5 (06.03.2023): 1254. http://dx.doi.org/10.3390/electronics12051254.
Der volle Inhalt der QuelleSheng, Jinfang, Jie Hu, Xiaoyu Teng, Bin Wang und Xiaoxia Pan. „Computation Offloading Strategy in Mobile Edge Computing“. Information 10, Nr. 6 (02.06.2019): 191. http://dx.doi.org/10.3390/info10060191.
Der volle Inhalt der QuelleHuang, Yan-Yun, und Pi-Chung Wang. „Computation Offloading and User-Clustering Game in Multi-Channel Cellular Networks for Mobile Edge Computing“. Sensors 23, Nr. 3 (19.01.2023): 1155. http://dx.doi.org/10.3390/s23031155.
Der volle Inhalt der QuelleAbbas, Aamir, Ali Raza, Farhan Aadil und Muazzam Maqsood. „Meta-heuristic-based offloading task optimization in mobile edge computing“. International Journal of Distributed Sensor Networks 17, Nr. 6 (Juni 2021): 155014772110230. http://dx.doi.org/10.1177/15501477211023021.
Der volle Inhalt der QuelleDissertationen zum Thema "Edge Computation Offloading"
Yu, Shuai. „Multi-user computation offloading in mobile edge computing“. Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS462.
Der volle Inhalt der QuelleMobile Edge Computing (MEC) is an emerging computing model that extends the cloud and its services to the edge of the network. Consider the execution of emerging resource-intensive applications in MEC network, computation offloading is a proven successful paradigm for enabling resource-intensive applications on mobile devices. Moreover, in view of emerging mobile collaborative application (MCA), the offloaded tasks can be duplicated when multiple users are in the same proximity. This motivates us to design a collaborative computation offloading scheme for multi-user MEC network. In this context, we separately study the collaborative computation offloading schemes for the scenarios of MEC offloading, device-to-device (D2D) offloading and hybrid offloading, respectively. In the MEC offloading scenario, we assume that multiple mobile users offload duplicated computation tasks to the network edge servers, and share the computation results among them. Our goal is to develop the optimal fine-grained collaborative offloading strategies with caching enhancements to minimize the overall execution delay at the mobile terminal side. To this end, we propose an optimal offloading with caching-enhancement scheme (OOCS) for femto-cloud scenario and mobile edge computing scenario, respectively. Simulation results show that compared to six alternative solutions in literature, our single-user OOCS can reduce execution delay up to 42.83% and 33.28% for single-user femto-cloud and single-user mobile edge computing, respectively. On the other hand, our multi-user OOCS can further reduce 11.71% delay compared to single-user OOCS through users' cooperation. In the D2D offloading scenario, we assume that where duplicated computation tasks are processed on specific mobile users and computation results are shared through Device-to-Device (D2D) multicast channel. Our goal here is to find an optimal network partition for D2D multicast offloading, in order to minimize the overall energy consumption at the mobile terminal side. To this end, we first propose a D2D multicast-based computation offloading framework where the problem is modelled as a combinatorial optimization problem, and then solved using the concepts of from maximum weighted bipartite matching and coalitional game. Note that our proposal considers the delay constraint for each mobile user as well as the battery level to guarantee fairness. To gauge the effectiveness of our proposal, we simulate three typical interactive components. Simulation results show that our algorithm can significantly reduce the energy consumption, and guarantee the battery fairness among multiple users at the same time. We then extend the D2D offloading to hybrid offloading with social relationship consideration. In this context, we propose a hybrid multicast-based task execution framework for mobile edge computing, where a crowd of mobile devices at the network edge leverage network-assisted D2D collaboration for wireless distributed computing and outcome sharing. The framework is social-aware in order to build effective D2D links [...]
Hansson, Gustav. „Computation offloading of 5G devices at the Edge using WebAssembly“. Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85898.
Der volle Inhalt der QuelleBozorgchenani, Arash <1989>. „Energy and Delay Efficient Computation Offloading Solutions for Edge Computing“. Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amsdottorato.unibo.it/9356/1/PhD%20Thesis_Arash%20Bozorgchenani.pdf.
Der volle Inhalt der QuelleSoto, Garcia Victor. „Mobility-Oriented Data Retrieval for Computation Offloading in Vehicular Edge Computing“. Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38836.
Der volle Inhalt der QuelleMessaoudi, Farouk. „User equipment based-computation offloading for real-time applications in the context of Cloud and edge networks“. Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S104/document.
Der volle Inhalt der QuelleComputation offloading is a technique that allows resource-constrained mobile devices to fully or partially offload a computation-intensive application to a resourceful Cloud environment. Computation offloading is performed mostly to save energy, improve performance, or due to the inability of mobile devices to process a computation heavy task. There have been a numerous approaches and systems on offloading tasks in the classical Mobile Cloud Computing (MCC) environments such as, CloneCloud, MAUI, and Cyber Foraging. Most of these systems are offering a complete solution that deal with different objectives. Although these systems present in general good performance, one common issue between them is that they are not adapted to real-time applications such as mobile gaming, augmented reality, and virtual reality, which need a particular treatment. Computation offloading is widely promoted especially with the advent of Mobile Edge Computing (MEC) and its evolution toward Multi-access Edge Computing which broaden its applicability to heterogeneous networks including WiFi and fixed access technologies. Combined with 5G mobile access, a plethora of novel mobile services will appear that include Ultra-Reliable Low-latency Communications (URLLC) and enhanced Vehicle-toeverything (eV2X). Such type of services requires low latency to access data and high resource capabilities to compute their behaviour. To better find its position inside a 5G architecture and between the offered 5G services, computation offloading needs to overcome several challenges; the high network latency, resources heterogeneity, applications interoperability and portability, offloading frameworks overhead, power consumption, security, and mobility, to name a few. In this thesis, we study the computation offloading paradigm for real-time applications including mobile gaming and image processing. The focus will be on the network latency, resource consumption, and accomplished performance. The contributions of the thesis are organized on the following axes : Study game engines behaviour on different platforms regarding resource consumption (CPU/GPU) per frame and per game module; study the possibility to offload game engine modules based on resource consumption, network latency, and code dependency ; propose a deployment strategy for Cloud gaming providers to better exploit their resources based on the variability of the resource demand of game engines and the QoE ; propose a static computation offloading-based solution for game engines by splitting 3D world scene into different game objects. Some of these objects are offloaded based on resource consumption, network latency, and code dependency ; propose a dynamic offloading solution for game engines based on an heuristic that compute for each game object, the offloading gain. Based on that gain, an object may be offloaded or not ; propose a novel approach to offload computation to MEC by deploying a mobile edge application that is responsible for driving the UE decision for offloading, as well as propose two algorithms to make best decision regarding offloading tasks on UE to a server hosted on the MEC
Djemai, Ibrahim. „Joint offloading-scheduling policies for future generation wireless networks“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS007.
Der volle Inhalt der QuelleThe challenges posed by the increasing number of connected devices, high energy consumption, and environmental impact in today's and future wireless networks are gaining more attention. New technologies like Mobile Edge Computing (MEC) have emerged to bring cloud services closer to the devices and address their computation limitations. Enabling these devices and the network nodes with Energy Harvesting (EH) capabilities is also promising to allow for consuming energy from sustainable and environmentally friendly sources. In addition, Non-Orthogonal Multiple Access (NOMA) is a pivotal technique to achieve enhanced mobile broadband. Aided by the advancement of Artificial Intelligence, especially Reinforcement Learning (RL) models, the thesis work revolves around devising policies that jointly optimize scheduling and computational offloading for devices with EH capabilities, NOMA-enabled communications, and MEC access. Moreover, when the number of devices increases and so does the system complexity, NOMA clustering is performed and Federated Learning is used to produce RL policies in a distributed way. The thesis results validate the performance of the proposed RL-based policies, as well as the interest of using NOMA technique
Krishna, Nitesh. „Software-Defined Computational Offloading for Mobile Edge Computing“. Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37580.
Der volle Inhalt der QuelleSilva, Joaquim Magalhães Esteves da. „Adaptive Computation Offloading in Mobile Edge Clouds“. Doctoral thesis, 2021. https://hdl.handle.net/10216/139189.
Der volle Inhalt der QuelleMaurício, Bruno Alexandre de Salabert. „Modelling edge computation offloading for automotive video analytics“. Master's thesis, 2021. https://hdl.handle.net/10216/135579.
Der volle Inhalt der QuelleIntelligent vehicles are becoming more common and affordable, and with each new model come complex and resource-intensive applications, starting at simple sensors, into assistant AI, and more recently full vehicle automation. These applications can be mostly segmented into two categories, infotainment and driving assistance. The latter category requires strict adherence to time limits, lest they become useless or even dangerous to the driver, and is the focus of the present work. The spread and availability of powerful computation devices throughout city streets as a result of a variety of factors, including the emergence of new technologies that demand higher node density like the fifth generation of mobile networks, raises the question as to whether there is an effective way that vehicles can take advantage of this spread out capacity, instead of depending solely on the conventional on board computing unit (OBU). Furthermore, given the complexity of these systems, how can one model them and perform simulations that are both valid and credible, as well as liable to verification through real world experiments. According to IEEExplore, even though the study of vehicular ad-hoc networks (VANET) goes all the way back to 2005, computation offloading within VANETs is a much more recent focus of general study (~2017). Nevertheless, dozens of different approaches with respective algorithms have been proposed. In terms of communication, both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication have been considered, with the technologies in use ranging from mobile and wifi, to VANET specific such as Dedicated Short Range Communications (DSRC / 802.11p). The most differentiating factor is the chosen parameters for the algorithms, that can be categorized in communication (available/used bandwidth), load (size, delay requirements), computation (required CPU cycles) and car movement (cell stay time). The main goals of this work are twofold. First, to provide a realistic and verifiable simulation environment with mathematical models for the load (based on a real video stream) and for the computation (based on a simple object detection engine). Second, to provide a simple proof-of-concept computation offloading algorithm that takes advantage of the information in the models to perform sensible offloading decisions.
Maurício, Bruno Alexandre de Salabert. „Modelling edge computation offloading for automotive video analytics“. Dissertação, 2021. https://hdl.handle.net/10216/135579.
Der volle Inhalt der QuelleIntelligent vehicles are becoming more common and affordable, and with each new model come complex and resource-intensive applications, starting at simple sensors, into assistant AI, and more recently full vehicle automation. These applications can be mostly segmented into two categories, infotainment and driving assistance. The latter category requires strict adherence to time limits, lest they become useless or even dangerous to the driver, and is the focus of the present work. The spread and availability of powerful computation devices throughout city streets as a result of a variety of factors, including the emergence of new technologies that demand higher node density like the fifth generation of mobile networks, raises the question as to whether there is an effective way that vehicles can take advantage of this spread out capacity, instead of depending solely on the conventional on board computing unit (OBU). Furthermore, given the complexity of these systems, how can one model them and perform simulations that are both valid and credible, as well as liable to verification through real world experiments. According to IEEExplore, even though the study of vehicular ad-hoc networks (VANET) goes all the way back to 2005, computation offloading within VANETs is a much more recent focus of general study (~2017). Nevertheless, dozens of different approaches with respective algorithms have been proposed. In terms of communication, both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication have been considered, with the technologies in use ranging from mobile and wifi, to VANET specific such as Dedicated Short Range Communications (DSRC / 802.11p). The most differentiating factor is the chosen parameters for the algorithms, that can be categorized in communication (available/used bandwidth), load (size, delay requirements), computation (required CPU cycles) and car movement (cell stay time). The main goals of this work are twofold. First, to provide a realistic and verifiable simulation environment with mathematical models for the load (based on a real video stream) and for the computation (based on a simple object detection engine). Second, to provide a simple proof-of-concept computation offloading algorithm that takes advantage of the information in the models to perform sensible offloading decisions.
Bücher zum Thema "Edge Computation Offloading"
Chen, Ying, Ning Zhang, Yuan Wu und Sherman Shen. Energy Efficient Computation Offloading in Mobile Edge Computing. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16822-2.
Der volle Inhalt der QuelleZhang, Ning, Ying Chen, Yuan Wu und Sherman Shen. Energy Efficient Computation Offloading in Mobile Edge Computing. Springer International Publishing AG, 2022.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Edge Computation Offloading"
Taheri, Javid, Schahram Dustdar, Albert Zomaya und Shuiguang Deng. „AI/ML for Computation Offloading“. In Edge Intelligence, 111–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22155-2_4.
Der volle Inhalt der QuelleZhang, Yan. „Mobile Edge Computing“. In Simula SpringerBriefs on Computing, 9–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83944-4_2.
Der volle Inhalt der QuelleMa, Xiao, Mengwei Xu, Qing Li, Yuanzhe Li, Ao Zhou und Shangguang Wang. „Edge Computing Based Computation Offloading“. In 5G Edge Computing, 63–79. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0213-8_4.
Der volle Inhalt der QuellePeng, Kai, Yiwen Zhang, Xiaofei Wang, Xiaolong Xu, Xiuhua Li und Victor C. M. Leung. „Computation Offloading in Mobile Edge Computing“. In Encyclopedia of Wireless Networks, 216–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_331.
Der volle Inhalt der QuellePeng, Kai, Yiwen Zhang, Xiaofei Wang, Xiaolong Xu, Xiuhua Li und Victor C. M. Leung. „Computation Offloading in Mobile Edge Computing“. In Encyclopedia of Wireless Networks, 1–5. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_331-1.
Der volle Inhalt der QuelleCha, Narisu, Celimuge Wu, Tsutomu Yoshinaga und Yusheng Ji. „Virtual Edge: Collaborative Computation Offloading in VANETs“. In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 79–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64002-6_6.
Der volle Inhalt der QuelleChen, Ying, Ning Zhang, Yuan Wu und Sherman Shen. „Dynamic Computation Offloading for Energy Efficiency in Mobile Edge Computing“. In Energy Efficient Computation Offloading in Mobile Edge Computing, 27–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16822-2_2.
Der volle Inhalt der QuelleChen, Ying, Ning Zhang, Yuan Wu und Sherman Shen. „Energy-Efficient Multi-Task Multi-Access Computation Offloading via NOMA“. In Energy Efficient Computation Offloading in Mobile Edge Computing, 123–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16822-2_5.
Der volle Inhalt der QuelleCheng, Xiaolan, Xin Zhou, Congfeng Jiang und Jian Wan. „Towards Computation Offloading in Edge Computing: A Survey“. In High-Performance Computing Applications in Numerical Simulation and Edge Computing, 3–15. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9987-0_1.
Der volle Inhalt der QuelleTefera, Natnael, und Ayalew Belay Habtie. „Mobility Aware Computation Offloading Model for Edge Computing“. In Communications in Computer and Information Science, 54–71. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23606-8_4.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Edge Computation Offloading"
Droob, Alexander, Daniel Morratz, Frederik Langkilde Jakobsen, Jacob Carstensen, Magnus Mathiesen, Rune Bohnstedt, Michele Albano, Sergio Moreschini und Davide Taibi. „Fault Tolerant Horizontal Computation Offloading“. In 2023 IEEE International Conference on Edge Computing and Communications (EDGE). IEEE, 2023. http://dx.doi.org/10.1109/edge60047.2023.00036.
Der volle Inhalt der QuelleWei, Xiaojuan, Shangguang Wang, Ao Zhou, Jinliang Xu, Sen Su, Sathish Kumar und Fangchun Yang. „MVR: An Architecture for Computation Offloading in Mobile Edge Computing“. In 2017 IEEE International Conference on Edge Computing (EDGE). IEEE, 2017. http://dx.doi.org/10.1109/ieee.edge.2017.42.
Der volle Inhalt der QuelleZhang, Letian, und Jie Xu. „Fooling Edge Computation Offloading via Stealthy Interference Attack“. In 2020 IEEE/ACM Symposium on Edge Computing (SEC). IEEE, 2020. http://dx.doi.org/10.1109/sec50012.2020.00062.
Der volle Inhalt der QuelleMa, Weibin, und Lena Mashayekhy. „Truthful Computation Offloading Mechanisms for Edge Computing“. In 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2020. http://dx.doi.org/10.1109/cscloud-edgecom49738.2020.00043.
Der volle Inhalt der QuelleCheng, Lei, Gang Feng, Yao Sun, Mengjie Liu und Shuang Qin. „Dynamic Computation Offloading in Satellite Edge Computing“. In ICC 2022 - IEEE International Conference on Communications. IEEE, 2022. http://dx.doi.org/10.1109/icc45855.2022.9838943.
Der volle Inhalt der QuelleXiong, Jingyu, Hongzhi Guo, Jiajia Liu, Nei Kato und Yanning Zhang. „Collaborative Computation Offloading at UAV-Enhanced Edge“. In GLOBECOM 2019 - 2019 IEEE Global Communications Conference. IEEE, 2019. http://dx.doi.org/10.1109/globecom38437.2019.9013956.
Der volle Inhalt der QuelleZhu, Shichao, Lin Gui, Jiacheng Chen, Qi Zhang und Ning Zhang. „Cooperative Computation Offloading for UAVs: A Joint Radio and Computing Resource Allocation Approach“. In 2018 IEEE International Conference on Edge Computing (EDGE). IEEE, 2018. http://dx.doi.org/10.1109/edge.2018.00017.
Der volle Inhalt der QuelleMeng, Xianling, Wei Wang, Yitu Wang, Vincent K. N. Lau und Zhaoyang Zhang. „Delay-Optimal Computation Offloading for Computation-Constrained Mobile Edge Networks“. In GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8647703.
Der volle Inhalt der QuelleYou, Changsheng, Yong Zeng, Rui Zhang und Kaibin Huang. „Resource Management for Asynchronous Mobile-Edge Computation Offloading“. In 2018 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2018. http://dx.doi.org/10.1109/iccw.2018.8403495.
Der volle Inhalt der QuelleCrutcher, Andrew, Caleb Koch, Kyle Coleman, Jon Patman, Flavio Esposito und Prasad Calyam. „Hyperprofile-Based Computation Offloading for Mobile Edge Networks“. In 2017 IEEE 14th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS). IEEE, 2017. http://dx.doi.org/10.1109/mass.2017.91.
Der volle Inhalt der Quelle