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Dissertations / Theses on the topic 'Offloading tasks'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Veltri, Joshua. "Computational Offloading for Sequentially Staged Tasks: A Dynamic Approach Demonstrated on Aerial Imagery Analysis." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1512659238133199.

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2

Zhu, Jixiang. "Computation Offloading and Task Scheduling among Multi-Robot Systems." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219618.

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In a Multi-Robot System (MRS), robots perform some collaborative behaviors so that some goals that are impossible for a single robot to achieve become feasible and attainable. Developing rapidly and exploited widely, cloud further extends the resources a robot can access thereby bringing significant potential benefits to robotic and automation systems. One of the potential benefits is Computation Offloading that moves the computational heavily parts of an application onto a server to reduce the execution time. However, to enable the computation offloading, the question must be answered when, w

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3

Zhang, Kaiyi. "Task Offloading and Resource Allocation Using Deep Reinforcement Learning." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41525.

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Rapid urbanization poses huge challenges to people's daily lives, such as traffic congestion, environmental pollution, and public safety. Mobile Internet of things (MIoT) applications serving smart cities bring the promise of innovative and enhanced public services such as air pollution monitoring, enhanced road safety and city resources metering and management. These applications rely on a number of energy constrained MIoT units (MUs) (e.g., robots and drones) to continuously sense, capture and process data and images from their environments to produce immediate adaptive actions (e.g., trigg

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4

Singh, Ajeet. "GePSeA: A General-Purpose Software Acceleration Framework for Lightweight Task Offloading." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/34264.

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Hardware-acceleration techniques continue to be used to boost the performance of scientific codes. To do so, software developers identify portions of these codes that are amenable for offloading and map them to hardware accelerators. However, offloading such tasks to specialized hardware accelerators is non-trivial. Furthermore, these accelerators can add significant cost to a computing system. <p> Consequently, this thesis proposes a framework called GePSeA (General Purpose Software Acceleration Framework), which uses a small fraction of the computational power on multi-core architectures

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5

Liu, Sige. "Bandit Learning Enabled Task Offloading and Resource Allocation in Mobile Edge Computing." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29719.

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The Internet-of-Things (IoT) is envisioned as a promising paradigm for carrying the interconnections of massive devices through various communications protocols. With the rapid development of fifth-generation (5G), IoT has incentivized a large number of new computation-intensive applications and bridges diverse technologies to provide ubiquitous services with intelligence. However, with billions of devices anticipated to be connected in IoT systems in the coming years, IoT devices face a series of challenges from their inherent features. For instance, the IoT devices are usually densely depl

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6

Khizar, Sadia. "Metrology for 5G edge networks (MEC). Leveraging mobile devices beyond the edge toward task offloading." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS069.

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L'omniprésence des dispositifs mobiles équipés d'une connectivité Internet et de systèmes de positionnement, nous pousse à les considérer comme une ressource précieuse à exploiter. Dans cette thèse, nous abordons l'utilisation des dispositifs mobiles sous un nouvel angle. Nous considérons l'extension de la capacité du MEC en utilisant les ressources disponibles des dispositifs mobiles au-delà de la bordure du réseau d'infrastructure. L'objectif est de tirer parti de leurs ressources inexploitées pour traiter les tâches de calculs au profit du MEC de manière distribuée. Pour pouvoir s'appuyer s

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Rahafrouz, Amir. "Distributed Orchestration Framework for Fog Computing." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77118.

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The rise of IoT-based system is making an impact on our daily lives and environment. Fog Computing is a paradigm to utilize IoT data and process them at the first hop of access network instead of distant clouds, and it is going to bring promising applications for us. A mature framework for fog computing still lacks until today. In this study, we propose an approach for monitoring fog nodes in a distributed system using the FogFlow framework. We extend the functionality of FogFlow by adding the monitoring capability of Docker containers using cAdvisor. We use Prometheus for collecting distribut

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8

Alam, Md Zahangir. "Reliable Cooperative Communications for Highly Mobile Internet-of-Vehicles (IoV) Environments." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29507.

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The ultimate challenge of the network designer is the resource allocation for both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications due to the dynamic environment. The optimal best path can improve network quality of service (QoS) over the time-varying channel using less transmission power. The joint power allocation of V2V and V2I is the most challenging aspect due to the association of its multi-variables objective function. Alternative optimization can be used to make the global problem into a series of sub-problems. Then a semi-definite programming (SDP)-based it

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9

Mahadevan, Soumya. "Performance Analysis of Offloading Application-Layer Tasks to Network Processors." 2007. https://scholarworks.umass.edu/theses/50.

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Offloading tasks to a network processor is one of the important ways to increase server performance. Hardware offloading of Transmission Control Protocol/Internet Protocol (TCP/IP) intensive tasks is known to significantly improve performance. When the entire application is considered for offloading, the impact on the server can be significant because it significantly reduces the load on the server. The goal of this thesis is to consider such a system with application-level offloading, rather than hardware offloading, and gauge its performance benefits. I am implementing this project on an Apa

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10

Lin, In-Chen, and 林穎晨. "The Optimization Mechanism of Task Offloading Decision for Fog Computing System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/89a5g9.

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碩士<br>國立屏東大學<br>資訊科學系碩士班<br>107<br>Recently, the Internet of Things has been developing rapidly. Mobile devices, which are dealing with complicated tasks (such as speech recognition and human detection and counting) need higher computational ability. Mobile devices with limited computing and battery power are not enough to cope with the resource-hungry computing tasks mentioned above. Therefore, tasks should be offloaded to the cloud and the cloud will return the result to the user. Then the cloud system will bearing more computation load. In addition, since the cloud system is far away from u

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11

Lin, Chih-Wei, and 林誌偉. "Analysis of Response Time for Task Offloading in Mobile Edge Computing." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/h3yjhx.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>107<br>The prevalence of mobile devices has changed the way that people access the network and process their data. Nevertheless, the computational resources in mobile devices are usually very limited and restrains their capability to handle the increasing complexity of certain applications. A potential solution for the problem is to offload computational-intensive tasks from mobile devices to the edge or cloud servers such that the response time of the offloaded tasks can be reduced. This paper investigates the performance of task offloading in an integrated edge-clo

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12

YEN, CHIA-CHUN, and 顏嘉君. "An Intelligent Decision Method for Task Offloading in Fog Computing System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/sprx5c.

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碩士<br>國立交通大學<br>資訊管理研究所<br>108<br>Nowadays, the use of mobile devices has grown rapidly across the world. More and more applications or services are transferred to mobile devices. However, it will result in some problems and limitations. For example, mobile devices’ resources and battery life are not better than normal machines or they cannot execute complex applications. In this situation, it is beneficial for mobile devices to offload computation-intensive tasks to high-level machines. While choosing a suitable time to do task offloading to the cloud/fog, choosing a better place to offload,

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13

高肇億. "EstiTO: An Efficient Task Offloading Approach based on Node Capability Estimation in Cloudlet." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/64438894925471829763.

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碩士<br>真理大學<br>資訊工程學系碩士班<br>104<br>In recent years, the cloud computing to become a very common technology, because mobile devices are become powerful and almost everyone have, the Internet has become increasingly developed. This paper, we propose an Offloading task based on the effective capacity of the nodes in the cloudlet architectural approach. Because mobile devices is limited by the capacity of the battery, so when the resource-poor mobile device by offloading computation to the near mobile device to reduce the calculated energy consumption, and a group of mobile devices management by De

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14

JHENG, YU-CIAN, and 鄭羽謙. "A Behavior-Driven Trust Management Approach for Securing Task Offloading in Hierarchical Clouds." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/76628974235384225053.

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碩士<br>真理大學<br>資訊工程學系碩士班<br>104<br>Because mobile devices have limits of their resources such as electric power and computational power, the need for complex calculations in order to avoid insufficient resources or the computing time is too long, may need offloading to the cloud. However, nearly everyone have mobile devices and the mobile device has a lot of idle resources, so offloading is not only to cloud this option only, can also offloading to other adjacent and extra resources mobile devices. If the selected mobile device have malicious behavior, such as not help computing, didn't provide

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15

FuChiang, Tsung, and 江宗富. "A High-Efficiency and Progressive Task Offloading Node Selection Method for Dynamic IoT System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3jwf8u.

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16

Xia, Qiufen. "Cost-Effective Resource Allocation and Throughput Maximization in Mobile Cloudlets and Distributed Clouds." Phd thesis, 2017. http://hdl.handle.net/1885/117056.

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With the advance in communication networks and the use explosion of mobile devices, distributed clouds consisting of many small and medium datacenters in geographical locations and cloudlets defined as "mini" datacenters are envisioned as the next-generation cloud computing platform. In particular, distributed clouds enable disaster-resilient and scalable services by scaling the services into multiple datacenters, while cloudlets allow pervasive and continuous services with low access delay by further enabling mobile users to access the services within their proximity. To realize the promises

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