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Relevant bibliographies by topics / Offloading tasks

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Academic literature on the topic 'Offloading tasks'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Offloading tasks"

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Zhang, Rui, Libing Wu, Shuqin Cao, et al. "Task Offloading with Task Classification and Offloading Nodes Selection for MEC-Enabled IoV." ACM Transactions on Internet Technology 22, no. 2 (2022): 1–24. http://dx.doi.org/10.1145/3475871.

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The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available computing resources, has attracted interest in recent years. The MEC-based task offloading can achieve low latency and low operational cost under the tasks delay constraints. However, most existing research generally focuses on how to divide and migrate these tasks to the other devices. This research ignores delay constraints and offloading node selection for different tasks. In this article, we design the

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Zou, Jing, Zhaoxiang Yuan, Peizhe Xin, et al. "Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks." Electronics 12, no. 16 (2023): 3484. http://dx.doi.org/10.3390/electronics12163484.

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Through offloading computing tasks to visible satellites for execution, the satellite edge computing architecture effectively issues the high-delay problem in remote grids (e.g., mountain and desert) when tasks are offloaded to the urban terrestrial cloud (TC). However, existing works are usually limited to offloading tasks in pure satellite networks and make offloading decisions based on the predefined models. Additionally, runtime consumption for offloading decisions is rather high. Furthermore, privacy information may be maliciously sniffed since computing tasks are transmitted via vulnerab

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Zhang, Ruipeng, Yanxiang Feng, Yikang Yang, Xiaoling Li, and Hengnian Li. "Dynamic Delay-Sensitive Observation-Data-Processing Task Offloading for Satellite Edge Computing: A Fully-Decentralized Approach." Remote Sensing 16, no. 12 (2024): 2184. http://dx.doi.org/10.3390/rs16122184.

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Satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, it is hard to gather complete information from all satellites. Moreover, the dynamic arriving tasks will also influence the obtained offloading assignment. Therefore, one daunting challenge in SEC is achieving optimal offloading assignments with consideration of the dynamic delay-se

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Lv, Dan, Peng Wang, Qubeijian Wang, Yu Ding, Zeyang Han, and Yadong Zhang. "Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System." Electronics 13, no. 12 (2024): 2332. http://dx.doi.org/10.3390/electronics13122332.

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With the exploration of next-generation network technology, visual internet of things (VIoT) systems impose significant computational and transmission demands on mobile edge computing systems that handle large amounts of offloaded video data. Visual users offload specific tasks to cloud or edge computing platforms to meet strict real-time requirements. However, the available scheduling and computational resources for offloading tasks constantly destroy the system’s reliability and efficiency. This paper proposes a mechanism for task offloading and resource optimization based on predictive perc

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Li , Deng, Chengqin Yu, Ying Tan, and Jiaqi Liu. "Optimization Method of Fog Computing High Offloading Service Based on Frame of Reference." Mathematics 12, no. 5 (2024): 621. http://dx.doi.org/10.3390/math12050621.

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The cost of offloading tasks is a crucial parameter that influences the task selection of fog nodes. Low-cost tasks can be completed quickly, while high-cost tasks are rarely chosen. Therefore, it is essential to design an effective incentive mechanism to encourage fog nodes to actively participate in high-cost offloading tasks. Current incentive mechanisms generally increase remuneration to enhance the probability of participants selecting high-cost tasks, which inevitably leads to increased platform costs. To improve the likelihood of choosing high-cost tasks, we introduce a frame of referen

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Fu, Shuang, Chenyang Ding, and Peng Jiang. "Computational Offloading of Service Workflow in Mobile Edge Computing." Information 13, no. 7 (2022): 348. http://dx.doi.org/10.3390/info13070348.

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Mobile edge computing (MEC) sinks the functions and services of cloud computing to the edge of the network to provide users with storage and computing resources. For workflow tasks, the interdependency and the sequence constraint being among the tasks make the offloading strategy more complicated. To obtain the optimal offloading and scheduling scheme for workflow tasks to minimize the total energy consumption of the system, a workflow task offloading and scheduling scheme based on an improved genetic algorithm is proposed in an MEC network with multiple users and multiple virtual machines (VM

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Liu, Jun, Xiaohui Lian, and Chang Liu. "Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network." Future Internet 13, no. 5 (2021): 128. http://dx.doi.org/10.3390/fi13050128.

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In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the moveme

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Wu, Qiong, Hongmei Ge, Qiang Fan, Wei Yin, Bo Chang, and Guilu Wu. "Efficient Task Offloading for 802.11p-Based Cloud-Aware Mobile Fog Computing System in Vehicular Networks." Wireless Communications and Mobile Computing 2020 (September 9, 2020): 1–12. http://dx.doi.org/10.1155/2020/8816090.

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Various emerging vehicular applications such as autonomous driving and safety early warning are used to improve the traffic safety and ensure passenger comfort. The completion of these applications necessitates significant computational resources to perform enormous latency-sensitive/nonlatency-sensitive and computation-intensive tasks. It is hard for vehicles to satisfy the computation requirements of these applications due to the limit computational capability of the on-board computer. To solve the problem, many works have proposed some efficient task offloading schemes in computing paradigm

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Li, Xianwei, and Baoliu Ye. "Latency-Aware Computation Offloading for 5G Networks in Edge Computing." Security and Communication Networks 2021 (September 22, 2021): 1–15. http://dx.doi.org/10.1155/2021/8800234.

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With the development of Internet of Things, massive computation-intensive tasks are generated by mobile devices whose limited computing and storage capacity lead to poor quality of services. Edge computing, as an effective computing paradigm, was proposed for efficient and real-time data processing by providing computing resources at the edge of the network. The deployment of 5G promises to speed up data transmission but also further increases the tasks to be offloaded. However, how to transfer the data or tasks to the edge servers in 5G for processing with high response efficiency remains a c

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Mohammed, Mostafa Abdulghafoor, Aya Ahkam Kamil, Raed Abdulkareem Hasan, and Nicolae Tapus. "An Effective Context Sensitive Offloading System for Mobile Cloud Environments using Support Value-based Classification." Scalable Computing: Practice and Experience 20, no. 4 (2019): 687–98. http://dx.doi.org/10.12694/scpe.v20i4.1570.

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Mobile cloud computing (MCC) has drawn significant research attention recently due to the popularity and capability of portable devices. This paper presents an MCC offloading system based on internet offloading choices. This system guarantees the conservation of battery life and reduced execution time. The proposed effective context sensitive offloading approach using support value-based classification is processed in different steps. Initially, the context data of the input tasks is extracted through the energy consumption model, cost model, execution model, communication model and stored. Th

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Dissertations / Theses on the topic "Offloading tasks"

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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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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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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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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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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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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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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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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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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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Books on the topic "Offloading tasks"

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K, Kokula Krishna Hari, and K. Saravanan, eds. Task Offloading to the Cloud by Using Cuckoo Model for Minimizing Energy Cost. Association of Scientists, Developers and Faculties, 2016.

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Accelerating Unity Through Automation: Powerup Your Unity Workflow by Offloading Intensive Tasks and Getting More Done. Apress L. P., 2023.

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Book chapters on the topic "Offloading tasks"

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Kaur, Parmeet, and Shikha Mehta. "Improvement of Task Offloading for Latency Sensitive Tasks in Fog Environment." In Lecture Notes on Data Engineering and Communications Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3448-2_3.

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Kanemitsu, Hidehiro, Masaki Hanada, and Hidenori Nakazato. "Multiple Workflow Scheduling with Offloading Tasks to Edge Cloud." In Cloud Computing – CLOUD 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23502-4_4.

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Sun, Yu-Jie, Hui Wang, Yu-Chen Shan, and Chen-bin Huang. "Online Offloading of Delay-Sensitive Tasks in Fog Computing." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8174-5_15.

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Chung, Minh Thanh, Josef Weidendorfer, Karl Fürlinger, and Dieter Kranzlmüller. "Proactive Task Offloading for Load Balancing in Iterative Applications." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30442-2_20.

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AbstractLoad imbalance is often a challenge for applications in parallel systems. Static cost models and pre-partitioning algorithms distribute the load at the beginning. Nevertheless, dynamic changes during execution or inaccurate cost indicators may lead to imbalance at runtime. Reactive work-stealing strategies can help monitor the execution and perform task migration to balance the load. However, the benefits depend on migration overhead and assumption about future execution.Our proactive approach further improves existing solutions by applying machine learning to online load prediction. F

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Xia, Haiying, Yingji Liu, Xinlei Wei, and Guoliang Dong. "Offloading Strategy of Computing Tasks in Cooperative Vehicle Infrastructure Systems." In Advances in Wireless Communications and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2255-8_13.

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Tabuchi, Akihiro, Hitoshi Murai, Masahiro Nakao, Tetsuya Odajima, and Taisuke Boku. "XcalableACC: An Integration of XcalableMP and OpenACC." In XcalableMP PGAS Programming Language. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7683-6_4.

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AbstractXcalableACC (XACC) is an extension of XcalableMP for accelerated clusters. It is defined as a diagonal integration of XcalableMP and OpenACC, which is another directive-based language designed to program heterogeneous CPU/accelerator systems. XACC has features for handling distributed-memory parallelism, inherited from XMP, offloading tasks to accelerators, inherited from OpenACC, and two additional functions: data/work mapping among multiple accelerators and direct communication between accelerators.

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Zhou, Bingyan, Long Chen, and Jigang Wu. "Available Time Aware Offloading for Dependent Tasks with Cooperative Edge Servers." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85928-2_38.

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Zhang, Yan. "Mobile Edge Computing for the Internet of Vehicles." In Simula SpringerBriefs on Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83944-4_5.

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AbstractThe advancement of cyber physical information has led to the pervasive use of smart vehicles while enabling various types of powerful mobile applications, which usually require high-intensity processing under strict delay constraints. Given their limited on-board computing capabilities, smart vehicles can offload these processing tasks to edge servers for execution. However, a highly dynamic topology, a complex vehicular communication environment, and edge node heterogeneity pose significant challenges in vehicular edge computing management. To address these challenges, in this chapter

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Tu, Jiaxue, Dongge Zhu, Yunni Xia, et al. "DQN-Based Applications Offloading with Multiple Interdependent Tasks in Mobile Edge Computing." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54521-4_5.

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Xu, Huanhuan, Jingya Zhou, and Fei Gu. "Computation Offloading for Multi-user Sequential Tasks in Heterogeneous Mobile Edge Computing." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92635-9_41.

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Conference papers on the topic "Offloading tasks"

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Puligheddu, Corrado, Nancy Varshney, Tanzil Hassan, Jonathan Ashdown, Francesco Restuccia, and Carla Fabiana Chiasserini. "OffloaDNN: Shaping DNNs for Scalable Offloading of Computer Vision Tasks at the Edge." In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2024. http://dx.doi.org/10.1109/icdcs60910.2024.00064.

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Wang, Yihang, Tao Jing, Xuehan Li, et al. "Joint Task Offloading and Power Allocation for Delay-Sensitive Tasks in IIoT." In 2024 3rd International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT). IEEE, 2024. https://doi.org/10.1109/ccpqt64497.2024.00077.

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Cai, Yuhan, Yilong Hui, Mengqiu Tian, Weifeng Qin, Nan Cheng, and Changle Li. "Mobile Tasks in STIVNs: A Multi-hop-transmission Assisted On-demand Task Offloading Scheme." In 2024 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2024. http://dx.doi.org/10.1109/iccc62479.2024.10682001.

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Wang, Yanxiang, Furong Chai, Qi Zhang, et al. "Intensive Tasks Offloading Decision Algorithms in Satellite Edge Computing Systems." In 2024 22nd International Conference on Optical Communications and Networks (ICOCN). IEEE, 2024. http://dx.doi.org/10.1109/icocn63276.2024.10648505.

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Afrasiabi, Seyedeh Negar, Diala Naboulsi, and Razvan Stanica. "Energy-Efficient Task Offloading Using Reinforcement Learning for Dependent Tasks in Cloud-Edge-Device Systems." In 2025 28th Conference on Innovation in Clouds, Internet and Networks (ICIN). IEEE, 2025. https://doi.org/10.1109/icin64016.2025.10943000.

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Ding, Nan, and Jianping Wu. "UCB Algorithm Based Decision Making Method for Edge Computing Tasks Offloading." In 2024 8th International Conference on Communication and Information Systems (ICCIS). IEEE, 2024. https://doi.org/10.1109/iccis63642.2024.10779409.

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Lei, Haochun, Yuben Qu, Lei Zhang, et al. "DNN Tasks Offloading and Bandwidth Optimization for Satellite-Terrestrial Collaborative Intelligence." In 2024 20th International Conference on Mobility, Sensing and Networking (MSN). IEEE, 2024. https://doi.org/10.1109/msn63567.2024.00061.

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Yuan, Haitao, Shen Wang, Yaofei Ma, et al. "Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831549.

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Bernard, Léo, Sonia Yassa, and Lylia Alouache. "D-NPGA : a new approach for tasks offloading in fog/cloud environment." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708605.

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Cao, Dun, Dan Cai, Bo Peng, and Jin Wang. "An Offloading Scheme for Diverse Inter-Dependent Tasks in Vehicle Edge Computing." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00062.

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