Relevant bibliographies by topics / Process Offloading

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Process Offloading'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Process Offloading"

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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 (May 13, 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 movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space–Air–Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself.
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Yao, Bingxin, Bin Wu, Siyun Wu, Yin Ji, Danggui Chen, and Limin Liu. "An Offloading Algorithm based on Markov Decision Process in Mobile Edge Computing System." International Journal of Circuits, Systems and Signal Processing 16 (January 5, 2022): 115–21. http://dx.doi.org/10.46300/9106.2022.16.15.

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In this paper, an offloading algorithm based on Markov Decision Process (MDP) is proposed to solve the multi-objective offloading decision problem in Mobile Edge Computing (MEC) system. The feature of the algorithm is that MDP is used to make offloading decision. The number of tasks in the task queue, the number of accessible edge clouds and Signal-Noise-Ratio (SNR) of the wireless channel are taken into account in the state space of the MDP model. The offloading delay and energy consumption are considered to define the value function of the MDP model, i.e. the objective function. To maximize the value function, Value Iteration Algorithm is used to obtain the optimal offloading policy. According to the policy, tasks of mobile terminals (MTs) are offloaded to the edge cloud or central cloud, or executed locally. The simulation results show that the proposed algorithm can effectively reduce the offloading delay and energy consumption.
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Thakur, Pawan Kumar, and Amandeep Verma. "Process Batch Offloading Method for Mobile-Cloud Computing Platform." Journal of Cases on Information Technology 17, no. 3 (July 2015): 1–13. http://dx.doi.org/10.4018/jcit.2015070101.

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Mobile cloud applications transfers the computational power and data storage outside the mobile device and into the mobile cloud, getting mobile computing and mobile applications to not handheld devices users but a wider choice of mobile subscribers. Process offloading is the technique in which some part of the application is transferred into the mobile cloud for execution. Many applications like GPS, face recognition, video editing etc. consumes more battery of the mobile devices. By offloading the power hunger part to the cloud is one of the approach to elongate the battery lifetime of mobile devices. The major goal of the proposed model is to combine the similar processes into single batch and offload the batch into cloud rather than offloading a single process into cloud. A MPCEPGM (Multilevel Process Cost Evaluation with Process Group Merging) algorithm is proposed for application partitioning and offloading to the cloud. MPCEPGM will predict the overall execution cost of the whole batch or process tree as single entity. This will help the mobile offloading procedure to organize the processes according to their delivery time. Proposed model is energy efficient to deliver the data effectively to the mobile cloud. The performance of the proposed system is assessed on the basis of total execution time and communication cost using Matlab simulations.
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Pereira, Felipe R., Carlos H. Fucatu, and Andrey AssumpçÃo. "Evaluation of the FPSO Polvo Offloading Process." IFAC Proceedings Volumes 42, no. 18 (2009): 152–56. http://dx.doi.org/10.3182/20090916-3-br-3001.0070.

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Kelle, Holger, Mikhail Santosa, and Anne Barthelemy. "Project integrated LNG offloading availability assessment for FLNG." APPEA Journal 54, no. 2 (2014): 542. http://dx.doi.org/10.1071/aj13115.

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This extended abstract explains a combined heuristic, analytical, and probabilistic process to evaluate LNG offshore offloading availability in combination with facility uptime and commercial drivers such as LNG sales/supply contracts. The heuristic assessment is informed by facility operators’, LNGC masters’, and tug operators’ experiences in offshore offloading and berthing operations. The analytical process includes assessment of met-ocean, mooring, manoeuvrability simulation, model testing and event forecasting methods. Gaps about uncertainties for future predictions are filled by probabilistic Monte-Carlo simulations. The heuristic, analytical, and probabilistic approach, combined with commercial drivers, is put together into uptime assessment to forecast the techno-commercial performance of the facility. The uptime assessment enables: confidence on achievable LNG throughput, the best for facility configuration and size, the best for facility location and facility’s operational expenditures; contractual viability—for LNG supplier and gas off-taker; and, key to terminal performance guarantee to gas off-takers. This process has been developed within INTECSEA during the past six years and has been applied to more than 15 LNG offshore offloading facilities at varying geographical locations. This extended abstract explores the key drivers and describes the effect on those key drivers due to varying location, varying technology, or LNG sales/supply contracting strategy. The key drivers include: achievable LNG throughput, uptime, downtime, demurrage, cargo cancellation, facility downturn, and partial LNG offloading. The process described is specific to side-by-side offloading operations; however, it can also be adapted to standard jetty offloading operations and tandem offloading operations.
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Bai, Wenle, Zhongjun Yang, Jianhong Zhang, and Rajiv Kumar. "Randomization-Based Dynamic Programming Offloading Algorithm for Mobile Fog Computing." Security and Communication Networks 2021 (August 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/4348511.

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Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a set of randomized offloading decisions. If some in these sets improve the decisions in the DP table, then they will be merged into the table. The iterated DP table is also used to improve the set of decisions generated in the iteration to obtain the optimal offloading approximate solution. Extensive simulations show that the proposed DPOA can generate decisions within 3 ms and the benefit is especially significant when users are in multitask offloading scenarios.
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Dash, Sanjit Kumar, Aiswaryalaxmi Pradhan, Sasmita Mishra, and Jibitesh Mishra. "Lightweight Opportunistic Mobile Data Offloading." International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics 9, no. 1 (January 2018): 1–15. http://dx.doi.org/10.4018/ijmdwtfe.2018010101.

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Current cellular networks are overloaded due to the increasing number of smartphones and demands for bandwidth-eager multimedia content. Upgrading the existing infrastructure of the cellular system is the most straight forward solution to meet the growing demand. Apart from this, offloading mobile data through Wi-Fi can be a feasible solution. Mobile offloading via Wi-Fi is the latest emerging trend in research and industry. In this article, the authors have proposed a framework for mobile data offloading for both cellular and Wi-Fi networks. The authors have introduced a daemon process-based approach to make the entire process lightweight by using a suitable offloading decision algorithm. This article then formulates a mathematical model to evaluate its feasibility and accuracy for achieving optimum performance.
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Liu, Bin, Qi Zhu, Weiqiang Tan, and Hongbo Zhu. "Congestion-Optimal WiFi Offloading with User Mobility Management in Smart Communications." Wireless Communications and Mobile Computing 2018 (August 1, 2018): 1–15. http://dx.doi.org/10.1155/2018/9297536.

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We study the WiFi offloading problem in smart communications and adaptively seek for the optimal offloading strategies with the consideration of the mobility management and the dynamical nature of network state. With users mobility management, we formulate the offloading ratio optimization problem based on Markov process. Then, we propose a novel Congestion-Optimal WiFi Offloading (COWO) algorithm based on subgradient method, which aims to obtain the optimal offloading ratio for each access point (AP) to maximize the throughput and minimize the network congestion. Due to the computational complexity of subgradient method, we further improve the COWO algorithm by the equivalent transformation. By viewing all the APs as one virtual WiFi network, we try to optimize the identical offloading ratio for virtual WiFi network and develop a Virtualized Congestion-Optimal WiFi Offloading (VCOWO) algorithm with lower complexity. Under the equivalent conditions, the performance of the VCOWO algorithm could well approximate the optimal results obtained by the COWO algorithm. It is found that the VCOWO algorithm could obtain the upper bound of multiple APs WiFi offloading performance. Moreover, we investigate the impacts of user mobility on the WiFi offloading performance. Simulation results show that the proposed algorithm could achieve higher throughput with lower network congestion compared with other current offloading schemes.
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Kou, Jinfeng, Yang Xiao, and Dong Wang. "An Economic User-Centric WiFi Offloading Algorithm for Heterogeneous Network." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/341292.

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An economic user-centric WiFi offloading algorithm is proposed to satisfy the major concerns of wireless users, who wish to have better network performance with even less network expense. Thus in this paper both system throughput and network expense are considered, and the goal of the proposed offloading algorithm is to obtain an optimal offloading ratio, which can both maximize the system throughput and minimize the network expense. Firstly, a practical system model is set up on the basis of a typical scenario of heterogeneous network. In this model, the average throughput of both cellular network and WiFi network is analyzed carefully. Then an economic user-centric WiFi offloading algorithm is proposed with an evaluation function to evaluate the system, and the optimal offloading ratio can be obtained by minimizing the evaluation function. At last, numerical results represent a direct calculating process of the optimal offloading ratio. These results in return validate the efficiency of the proposed offloading algorithm as well.
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Wang, Qian, Juan Fang, Bei Gong, Xiaojiang Du, and Mohsen Guizani. "An Intelligent Data Uploading Selection Mechanism for Offloading Uplink Traffic of Cellular Networks." Sensors 20, no. 21 (November 4, 2020): 6287. http://dx.doi.org/10.3390/s20216287.

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Wi-Fi uploading is considered an effective method for offloading the traffic of cellular networks generated by the data uploading process of mobile crowd sensing applications. However, previously proposed Wi-Fi uploading schemes mainly focus on optimizing one performance objective: the offloaded cellular traffic or the reduced uploading cost. In this paper, we propose an Intelligent Data Uploading Selection Mechanism (IDUSM) to realize a trade-off between the offloaded traffic of cellular networks and participants’ uploading cost considering the differences among participants’ data plans and direct and indirect opportunistic transmissions. The mechanism first helps the source participant choose an appropriate data uploading manner based on the proposed probability prediction model, and then optimizes its performance objective for the chosen data uploading manner. In IDUSM, our proposed probability prediction model precisely predicts a participant’s mobility from spatial and temporal aspects, and we decrease data redundancy produced in the Wi-Fi offloading process to reduce waste of participants’ limited resources (e.g., storage, battery). Simulation results show that the offloading efficiency of our proposed IDUSM is (56.54×10−7), and the value is the highest among the other three Wi-Fi offloading mechanisms. Meanwhile, the offloading ratio and uploading cost of IDUSM are respectively 52.1% and (6.79×103). Compared with other three Wi-Fi offloading mechanisms, it realized a trade-off between the offloading ratio and the uploading cost.
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Dissertations / Theses on the topic "Process Offloading"

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Garber, Marcos Fernando. "Estruturas flutuantes para a exploração de campos de petróleo no mar(FPSO): apoio à decisão na escolha do sistema." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/3/3135/tde-31052003-180222/.

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Freqüentemente os profissionais da construção naval tomam decisões para selecionar os elementos que devem ser especificados na composição de determinado projeto. Além da experiência e do conhecimento necessários para optar por um caminho adequado, a escolha deve atender eficientemente ao problema proposto e às preferências do projetista. A seleção de componentes do projeto de estruturas flutuantes para exploração de campos de petróleo no mar envolve aspectos objetivos e subjetivos. O trabalho apresenta e aplica alguns métodos de auxílio à tomada de decisão possibilitando ao projetista aprimorar sua sensibilidade. O objetivo da pesquisa é apresentar de forma sumária as duas bases para a decisão sobre o projeto, que são os métodos de apoio e os requisitos que as instalações FPSO devem atender, fornecendo como resultado um procedimento que permitirá aos usuários a melhor escolha dos componentes e aumentar a sensibilidade dos projetistas na seleção entre as possíveis opções (fazer totalmente novo ou aproveitar o existente). O trabalho apresenta uma revisão dos princípios da análise da decisão, informações sobre métodos de apoio à decisão, os dados de entrada do problema proposto, o método de projetos navais, os requisitos básicos para construção de estruturas flutuantes de exploração de petróleo no mar e os requisitos básicos para uma instalação FPSO. Para a solução do problema se aplicaram os métodos de árvore de decisão, para a parte correspondente às decisões sob risco e o método de análise hierárquica AHP para as decisões tomadas sob certeza.
Frequently, naval construction engineers take decisions to select the elements which must specified for the composition of a determined project. Besides the necessary experience and the knowledge to choose a proper way, the option must fulfil efficiently the problem requirements and the preferences of the designer. The choice of components in the design off-shore structures for production in sea oilfields, involves objective and subjective aspects. This work introduces and uses a few methods of decision aids, helping the designer to improve his sensibility. The objective of this research is to present in a simple way two foundations to decide about the project, which are the aid decision methods and the requirements which the FPSO (Floating Production Storage and Offloading System) must fulfil, supplying a procedure that enables the designer to take the best choice of components and to increase his sensibility referring to the selection among the possible options, wich are to make a completely new FPSO or to use an existing one. This work offers a revision of the principles of the decision analysis theory, the data input of the problem, the classic naval design method, the basic requirements to build floating structures to off-shore oil extraction, and the basic requirements for a FPSO installation. To solve the problem the decision tree method, for decision under risk, and the AHP (Analytic Hierarchy Process), for decision under certainty, were applied.
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Rosi, Gianluca. "Monitoraggio dei processi produttivi nell' industria 4.0: un approccio Open-Source." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21509/.

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In questo studio si è posto sotto osservazione il comportamento di un prodotto software open source ancora in fase di sviluppo: posizionato a metà tra OT e IT in ambito Indutrial IoT, si prefigge di uniformare le comunicazioni tra le due parti. Si è quindi aggiunta una funzionalità di inoltro, programmabile dinamicamente attraverso una interfaccia utente, che ne agevoli l'utilizzo da parte degli operatori. La conseguente verifica delle prestazioni ha permesso di stilare una prima visione sull'applicabilità sia in termini di vantaggi nell'occupazione di risorse e di conseguenza economici, sia in termini di semplicità di implementazione. Ci si è focalizzati infatti nell'osservare la quantità di dati che si riescono ad aggregare e inoltrare in proporzione al relativo impegno computazionale, rispetto a varie configurazioni, rispettive a diverse tipologie di implementazione (che siano su dispositivi con capacità limitate, non dedicati e su un'architettura distribuita). Una visione finale d'insieme restituisce un prodotto sì completo ma che lascia diversi punti di approfondimento aperti a nuove personalizzazioni e incrementi in efficienza.
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Liu, Dongqing. "Mobile data and computation offloading in mobile cloud computing." Thèse, 2019. http://hdl.handle.net/1866/22660.

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ARIYAPALA, DALUWATHUMULLA GAMAGE KANISHKA. "Smartphones, Drones and IoT: Security and Privacy in Heterogeneous Smart Devices." Doctoral thesis, 2017. http://hdl.handle.net/2158/1079068.

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Heterogeneous computing devices are surrounding us in our day-to-day life at an unprecedented rate and they are showing promising capabilities. For example, the drone Loon Copter is one such device providing an unrestricted mobility in air, surface and underwater. Similarly, smartphones and IoT are also enabling to sense our environment. Many of these devices are embedded with processing, sensing, software and communication capabilities, allowing many services to be built on top. In the near future these connected devices will be everywhere from smart cities, factories to our homes and even on our bodies. In order to reach the full potential of these emerging devices, a prominent requirement, “security by design", must be fulfilled to make the technology ready for mass adaptation. Following this direction, we focus on security and privacy issues of three heterogeneous devices: (i) Smartphone related security and privacy issues; (ii) Application of drones for secure localization; and (iii) IoT related security issues. In the first part of this dissertation, we look into security and privacy challenges in smartphones. Smartphones are taking a leading role in binding most of the heterogeneous computing devices and also getting cluttered with a lot of personal data. We investigated three security related issues in smartphones: i) malware detection, ii) preserving anonymity in mobile cloud communications, and iii) analyzing the energy consumption of cryptographic protocols to improve user experiences. As for point i), in most attack scenarios an adversary takes local or remote control of a mobile device (by leveraging system vulnerabilities via malicious apps), and sends the collected information from the smartphone to a remote web server. This undermines the users security and privacy, and we propose a new approach for detecting malware by focusing on network communications. As for point ii), Smartphone applications are increasingly relying on cloud services such as online banking, instant messaging and file exchange. For an external observer, this communication side channel may reveal a lot of information. Strong adversaries like government agencies are also proposing these channels as a means to monitor their surveillance targets. Similarly, if the mobile network providers and cloud service providers collude together, they can violate the privacy of the users. We propose an end-to-end anonymous communications protocol for delay-tolerant applications (similar to Whatsapp or Email), to protect user privacy and prove the security properties of the protocol under this strong attack model. Finally, as for point iii), we analyze the energy consumption of cryptographic protocols running on smartphones. The number of web services accessed over encrypted traffic is rapidly growing, especially via SSL/TLS. In our investigation, we focused on TLS and show how TLS session resume can greatly save energy by avoiding asymmetric cryptographic operations. We further propose Cloud aided TLS (CaT). In the second part of this dissertation, we explore the possibility of using the emerging drone technology to solve the secure location verification problem. Many innovations are emerging using drones such as last mile delivery and emergency response. Many dependable distributed systems are vulnerable to node displacement attacks. For example, a hostile actor physically moving few sensors in a pollution monitoring system can easily disrupt the monitoring. This displacement attack is simple, but difficult to detect. Current solutions require several fixed anchor nodes with trusted positions. We propose VerifierBee, which replace all the fixed anchors with a single drone that flies through a sequence of waypoints. VerifierBee, finds a good approximation of the shortest path, and at the same time it respects a set of requirements about drone controllability, localization precision and communication range. The third part of this dissertation focuses on IoT related security issues. In many scenarios, IoT systems comprise of widely deployed sensors and actuators with connectivity. Many of these sensors are battery operated, with low processing power and left unattended after deployment. Therefore, lightweight low-power security protocols are needed. In this part of the dissertation we propose a framework to detect IoT sensor node actions by observing the encrypted communication traffic. In particular, IETF standardized DTLS encrypted traffic. There are many recent incidents about DDoS attacks using compromised IoT devices and our work steps in this direction to detect any compromised nodes.
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Book chapters on the topic "Process Offloading"

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Melo, Sávio, Cícero Silva, and Gibeon Aquino. "Classification Aspects of the Data Offloading Process Applied to Fog Computing." In Computational Science and Its Applications – ICCSA 2021, 340–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87013-3_26.

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Sukhpreet Kaur and Harwinder Singh Sohal. "Hybrid Application Partitioning and Process Offloading Method for the Mobile Cloud Computing." In Advances in Intelligent Systems and Computing, 87–95. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2035-3_10.

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Zalat, Mohamed S., Saad M. Darwish, and Magda M. Madbouly. "An Effective Offloading Model Based on Genetic Markov Process for Cloud Mobile Applications." In Advances in Intelligent Systems and Computing, 38–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58669-0_4.

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Zhang, 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.

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AbstractMobile edge computing is a promising paradigm that brings computing resources to mobile users at the network edge, allowing computing-intensive and delay-sensitive applications to be quickly processed by edge servers to satisfy the requirements of mobile users. In this chapter, we first introduce a hierarchical architecture of mobile edge computing that consists of a cloud plane, an edge plane, and a user plane. We then introduce three typical computation offloading decisions. Finally, we review state-of-the-art works on computation offloading and present the use case of joint computation offloading.
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Mavromoustakis, Constandinos X., George Mastorakis, Athina Bourdena, Evangelos Pallis, Dimitrios Stratakis, Emmanouil Perakakis, Ioannis Kopanakis, et al. "A Social-Oriented Mobile Cloud Scheme for Optimal Energy Conservation." In Resource Management of Mobile Cloud Computing Networks and Environments, 97–121. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8225-2.ch005.

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This chapter elaborates on energy usage optimization issues by exploiting a resource offloading process based on a social-oriented mobile cloud scheme. The adoption of the proposed scheme enables for increasing the reliability in services provision to the mobile users by guaranteeing sufficient resources for the mobile application execution. More specifically, this chapter describes the process to improve the energy consumption of the mobile devices through the exploitation of a social-oriented model and a cooperative partial process offloading scheme. This research approach exploits social centrality, as the connectivity model for the resource offloading, among the interconnected mobile devices to increase the energy usage efficiency, the mobile nodes availability, as well as the process of execution reliability. The proposed scheme is thoroughly evaluated to define the validity and the efficiency for the energy conservation increase of future mobile computing devices.
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Khan, Atta ur Rehman, and Abdul Nasir Khan. "High Performance Computing on Mobile Devices." In Innovative Research and Applications in Next-Generation High Performance Computing, 334–48. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0287-6.ch013.

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Mobile devices are gaining high popularity due to support for a wide range of applications. However, the mobile devices are resource constrained and many applications require high resources. To cater to this issue, the researchers envision usage of mobile cloud computing technology which offers high performance computing, execution of resource intensive applications, and energy efficiency. This chapter highlights importance of mobile devices, high performance applications, and the computing challenges of mobile devices. It also provides a brief introduction to mobile cloud computing technology, its architecture, types of mobile applications, computation offloading process, effective offloading challenges, and high performance computing application on mobile devises that are enabled by mobile cloud computing technology.
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Chopra, Nidhi, Anurag Sharma, and Pooja Dhand. "Mobility Management Framework in Edge Computing using Multi Criteria Decision Making." In Emerging Trends in Engineering and Management, 135–40. 2023rd ed. Soft Computing Research Society, 2023. http://dx.doi.org/10.56155/978-81-955020-3-5-15.

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The urge for seamless and ubiquitous connectivity for heterogeneous devices and networks to deliver desired results has gathered a lot of attention of researchers, academicians and industry experts. To a large extent, this issue has been resolved by cloud computing through task offloading either partially or fully but sending so much data on cloud create overheads. So, a particular task has to be uploaded or not and handoff to be initiated or not is a major decision. Through this paper, an algorithm to take the decision for handoff and switching to the most lucrative network has been proposed. The scheme aims to preserve and provide the users with Quality of Service and Quality of Experience while keeping in mind the criteria of offloading traffic to another cell or network based on the number of mobile users, type of application being used on the mobile, available bandwidth, and network load. The proposed mobile edge architecture may improve the timely execution of the handover process, greatly reduces the ping-pong rate and terminal overhead.
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Bedi, Pradeep, S. B. Goyal, and Jugnesh Kumar. "Task Offloading Using Deep Reinforcement Learning for Edge IoT Networks." In Demystifying Federated Learning for Blockchain and Industrial Internet of Things, 43–57. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3733-9.ch003.

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Edge computing is a type of distributed computing that was designed especially for internet of things (IoT) users to provide computational resources and data management nearby to users' devices. By introducing edge computing for IoT, networks have reduced the bandwidth and latency issue while handling real-time applications. The major benefit of edge computing is that it reduces the communication overhead between IoT user and server. With integration of IoT in our daily lives, it has attracted researchers towards its performance management such as complexity minimization, latency minimization, memory management, energy consumption minimization, etc. In this chapter, deep reinforcement learning is focused to minimize the computational complexity at IoT user end. The task offloading decision process is designed using Q-Learning, which minimizes the system cost and curse of high dimensional data. In addition, the proposed methodology will perform better as compared to existing algorithms with respect to system costs.
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Gray, Ian, Andrea Acquaviva, and Neil Audsley. "Designing Resource-Constrained Embedded Heterogeneous Systems to Cope with Variability." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 75–101. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6194-3.ch004.

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As modern embedded systems become increasingly complex, they also become susceptible to manufacturing variability. Variability causes otherwise identical hardware elements to exhibit large differences in dynamic and static power usage, maximum clock frequency, thermal resilience, and lifespan. There are currently no standard ways of handling this variability from the software developer's point of view, forcing the hardware vendor to discard devices that fall below a certain threshold. This chapter first presents a review of existing state-of-the-art techniques for mitigating the effects of variability. It then presents the toolflow developed as part of the ToucHMore project, which aims to build variability-awareness into the entire design process. In this approach, the platform is modelled in SysML, along with the expected variability and the monitoring and mitigation capabilities that the hardware presents. This information is used to automatically generate a customised variability-aware runtime, which is used by the programmer to perform operations such as offloading computation to another processing element, parallelising operations, and altering the energy use of operations (using voltage scaling, power gating, etc.). The variability-aware runtime affects its behaviour according to modelled static manufacturing variability and measured dynamic variability (such as battery power, temperature, and hardware degradation). This is done by moving computation to different parts of the system, spreading computation load more efficiency, and by making use of the modelled capabilities of the system.
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Krueger, Joel. "Music as affective scaffolding." In Music and Consciousness 2, 55–70. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198804352.003.0004.

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For 4E cognitive science, minds are embodied, embedded, enacted, and extended. Proponents observe that we regularly ‘offload’ our thinking onto body and world: we use gestures and calculators to augment mathematical reasoning, and smartphones and search engines as memory aids. This chapter argues that music is a ‘beyond-the-head’ resource that affords offloading. Via this offloading, music scaffolds access to new forms of thought, experience, and behaviour. The chapter focuses on music’s capacity to scaffold emotional consciousness, including the self-regulative processes constitutive of emotional consciousness. In developing this idea, the chapter considers the ‘material’ and ‘worldmaking’ character of music, applying these considerations to two case studies: music as a tool for religious worship, and music as a weapon for torture.
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Conference papers on the topic "Process Offloading"

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Vakilinia, Shahin, Iman Vakilinia, and Mohamed Cheriet. "Green process offloading in smart home." In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 2017. http://dx.doi.org/10.1109/pimrc.2017.8292694.

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2

Felipe, Pereira,. "Evaluation of the FPSO Polvo Offloading Process." In Manoeuvring and Control of Marine Craft, edited by Donha, Decio, chair Pascoal, Antonio and Donha, Decio. Elsevier, 2009. http://dx.doi.org/10.3182/20090916-3-br-3001.00023.

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They, Yu-Shu, Seong-Yee Phang, Sanggon Lee, Hoon Jae Lee, and Hyotaek Lim. "CPOP: Cryptography Process Offloading Proxy for Resource Constrained Devices." In 2008 International Conference on Information Security and Assurance (ISA '08). IEEE, 2008. http://dx.doi.org/10.1109/isa.2008.107.

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Pawan Kumar Thakur and Amandeep Verma. "Hybrid process cost evaluation method in mobile code offloading." In 2015 1st International Conference on Next Generation Computing Technologies (NGCT). IEEE, 2015. http://dx.doi.org/10.1109/ngct.2015.7375102.

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5

Fadaraliki, David I., and S. Rajendran. "Process offloading from android device to cloud using JADE." In 2015 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 2015. http://dx.doi.org/10.1109/iccpct.2015.7159260.

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Mota, Vinicius F. S., Daniel F. Macedo, Yacine Ghamri-Doudanez, and Jose Marcos S. Nogueira. "Managing the decision-making process for opportunistic mobile data offloading." In NOMS 2014 - 2014 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2014. http://dx.doi.org/10.1109/noms.2014.6838260.

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7

Rohlik, Matej, and Tomas Vanek. "Securing offloading process within small cell cloud-based mobile networks." In 2014 IEEE Globecom Workshops (GC Wkshps). IEEE, 2014. http://dx.doi.org/10.1109/glocomw.2014.7063378.

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8

Sharafian, Amir, Paul Blomerus, and Walter Mérida. "Methane Emissions Reduction in Liquefied Natural Gas Off-Loading Process in Refueling Stations." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11885.

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Abstract:
Abstract Recent research into methane emissions from the liquefied natural gas (LNG) supply chain has revealed uncertainty in the overall greenhouse gas emissions reduction associated with the use of LNG in heavy-duty vehicles. Methane is the main component of natural gas and a potent greenhouse gas. This study investigates the impact of five methods used to offload LNG from a tanker truck to an LNG refueling station and estimate the amount of fugitive methane emissions. The LNG offloading process time, and the final pressures of the tanker truck and refueling station are considered to evaluate the performance of the LNG offloading methods. The modeling results show that the LNG transfer by using a pressure buildup unit has a limited operating range and can increase methane emissions by 10.4% of LNG offloaded from the tanker truck. The results indicate that the LNG transfer by using a pump and an auxiliary pressure buildup unit without vapor return provides the shortest fuel offloading time with the lowest risk of venting methane to the atmosphere.
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Zannat, Hafsa, and Md Shohrab Hossain. "A hybrid framework using Markov decision process for mobile code offloading." In 2016 19th International Conference on Computer and Information Technology (ICCIT). IEEE, 2016. http://dx.doi.org/10.1109/iccitechn.2016.7860163.

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10

Liu, Dongqing, Lyes Khoukhi, and Abdelhakim Hafid. "Data offloading in mobile cloud computing: A Markov Decision Process approach." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7997070.

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