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Task Distribution offloading Algorithm Based on DQN for Sustainable Vehicle Edge Network 7

Task Distribution Offloading Algorithm Based on DQN for Sust...

IEEE 7th International Conference on Network Softwarization (NetSoft) - Accelerating Network Softwarization in the Cognitive Age

作者： Feng, Tianyi Wang, Bin Zhao, Hai-tao Zhang, Tangwei Tang, Jiawen Wang, Zhenkun Nanjing Univ Posts & Telecommun Nanjing 210003 Peoples R China Hangzhou Hikvis Digital Technol CO LTD Hangzhou Peoples R China

ISBN: (纸本)9781665405225

The edge access component of the Internet of Vehicles has a high computational rate and energy consumption. This paper proposes a distribution offloading algorithm based on deep Q-learning network (DQN) to achieve the best latency and sustainable scheduling. Firstly, the computational tasks of various vehicles are prioritized using the analytic hierarchy process (AHP) to assign different weights to the task processing rate in order to establish a relationship model. Secondly, by introducing edge computing based on DQN, the task offloading model is established by using the weighted sum of task processing rate as the optimization goal, which realizes the long-term utility of offloading strategies. The performance evaluation results show that, when compared to the Q-learning algorithm, the proposed method can reduce the average task processing delay by 17%, effectively improving the sustainable task offload efficiency.

关键词： Internet of vehicle mobile edge computing computational offloading deep Q-learning network computational rate

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Neural Architecture Search for Computation offloading of DNNs from Mobile Devices to the Edge Server 12

Neural Architecture Search for Computation Offloading of DNN...

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12th International Conference on ICT Convergence (ICTC) - Beyond the Pandemic Era with ICT Convergence Innovation

作者： Lee, KyungChae Le Vu Linh Kim, Heejae Youn, Chan-Hyun Korea Adv Inst Sci & Technol KAIST Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781665423830

With the rapid development of modern deep learning technology, deep neural network (DNN)-based mobile applications have also been considered for various areas. However, since mobile devices are not optimized to run the DNN applications due to their limit of computational resources, several computation offloading-based approaches have been introduced to overcome the issue;for DNN models, it was reported that, their elaborate partitioning, which allows that input samples are partially executed on mobile devices and then the edge server processes the rest of the execution, can be effective in improving runtime performance. In addition, to improve communication-efficiency in the offloading scenario, there have been also studies to reduce transmitted data from a mobile device and the edge server by leveraging model compression. However, the existing approaches have the root limitation that the performance eventually depend on that of the architecture of original DNN models. To overcome this, we propose a novel neural architecture search (NAS) method to consider the computation offloading cases. On the top of the existing NAS approaches, we additionally introduce resource and channel selection mask. The resource selection mask effectively divides the operations in the target model into those for a mobile device and the edge server;the channel selection mask allows to transmit only selected channels to the edge server without the reduction of task performance (e.g., accuracy). Based on the two additional masks, for the NAS procedure we introduce a new loss function to take into account end-to-end inference time as well as the task performance which is the original goal of NAS. In the evaluation, the proposed method is compared to existing approaches;we see from the experimental results that our method outperforms both the previous NAS and pruning-based model partitioning approaches.

关键词： computational offloading deep neural network inference edge computing neural architecture search

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offloading Deep Learning Empowered Image Segmentation from UAV to Edge Server 44

Offloading Deep Learning Empowered Image Segmentation from U...

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44th International Conference on Telecommunications and Signal Processing (TSP)

作者： Ilhan, Huseyin Enes Ozer, Sedat Kurt, Gunes Karabulut Cirpan, Hakan Ali Istanbul Tech Univ Dept Elect & Telecommun Engn Istanbul Turkey Bilkent Univ Dept Comp Sci Ankara Turkey Polytech Montreal Dept Elect Engn Polygrames Res Ctr Montreal PQ Canada

ISBN: (纸本)9781665429337

Image and video analysis in unmanned aerial vehicle (UAV) systems have been a recent interest in many applications since the images taken by UAV systems can provide useful information in many domains including maintenance, surveillance and entertainment. However, a constraint on UAVs is having limited battery power and recent developments in the artificial intelligence (AI) domain encourages many applications to run computationally heavy algorithms on the taken UAV images. Such applications drain the power from the on-board battery rapidly, while requiring strong computationally strong resources. An alternative to that approach is offloading heavy tasks such as object segmentation to a remote (edge) server and perform the heavy computation on that server. However, the effect of the communication system and the used channel introduce noise on the transferred data and the effect of the noise due to the use of such LTE communication system on pre-trained deep networks has not been previously studied in the literature. In this paper, we study one such scenario where the images taken by UAVs and (the same images) transferred to an edge server via an LTE communication system under different scenarios. In our case, the edge server runs an off-the-shelf pretrained deep learning algorithm to segment the transmitted image. We provide an analysis of the effect of the wireless channel and the communication system on the final segmentation of the transmitted image on such a scenario.

关键词： computational offloading image segmentation edge computing UAV image processing deep learning

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Machine Learning for Edge-Aware Resource Orchestration for IoT Applications

Machine Learning for Edge-Aware Resource Orchestration for I...

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IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)

作者： Jammal, Manar AbuSharkh, Mohamed York Univ Sch Informat Technol Toronto ON Canada Ferris State Univ Digital Media Software Engn Grand Rapids MI USA

ISBN: (纸本)9781665438414

The market is experiencing a huge avalanche on the Internet-of-Thing (IoT) devices count and their data traffic from wearable personal devices to smart enterprise applications. This sensory data is playing a crucial role in our day-to-day life and enterprise's products and business decision-making process. Although this data provides promising business insights and can enhance applications' performance, it comes with different challenges including connectivity, dynamic resource demands, privacy, and others. Therefore, the infrastructure of the IoT applications must be well-orchestrated and accompanied with intelligence in mind to scale, self-organize, and handle the huge data volume and transmission. The intelligent platform is expected to self-explore workloads and autonomously allocate computing resources at runtime to assist the IoT system in achieving its best intrinsic value. Hence, this paper introduces a novel platform consisting of various machine learning (ML) techniques and optimization model to forecast the IoT applications' behavior and best deploy such applications dynamically on the edge to meet and enhance the overall end-to-end application performance. The comparative analysis has shown that the Random Forest model has promising results for resource forecasting. Also, the proposed deployment optimization model shows the importance of providing a tradeoff between computing, delay/transmission rate, and computational offloading constraints.

关键词： Internet of Things Edge Computing Machine Learning QoS Big Data computational offloading Workload Prediction Optimization Forecasting IoT Sensors

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A Survey of Security in Cloud, Edge, and Fog Computing

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SENSORS 2022年第3期22卷 927-927页

作者： Ometov, Aleksandr Molua, Oliver Liombe Komarov, Mikhail Nurmi, Jari Tampere Univ Fac Informat Technol & Commun Sci Elect Engn Unit Tampere 33720 Finland ITMO Univ Fac Secure Informat Technol Lab Cryptog Methods Informat Secur St Petersburg 191002 Russia Natl Res Univ Higher Sch Econ Grad Sch Business Moscow 101000 Russia

The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.

关键词： computing survey security privacy distributed systems computational offloading

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IoT Clusters for Enhancing Multimedia Applications

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SENSORS 2022年第23期22卷 9077-9077页

作者： Coelho, Jorge Nogueira, Luis Polytech Porto IPP Sch Engn ISEP P-4249015 Porto Portugal Univ Porto LIACC Artificial Intelligence & Comp Sci Lab P-4099002 Porto Portugal

In this paper, we present a framework for exploring the spare capacity of IoT devices for clustered execution of multimedia applications. Applications of this type are usually framed with specific quality parameters that enable a desirable level of service. This means that the IoT cluster must guarantee strict quality ranges of service to work as expected. The framework is totally customizable, and QoS dimensions can be easily added or removed given their relevance in the application scenario. The achieved results clearly demonstrate the utility of using the spare capacity of IoT devices, otherwise unused, to cooperatively execute servies within the desired quality of service levels.

关键词： IoT clustering QoS assurance resource usage optimization edge computing computational offloading

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Single Restart with Time Stamps for Parallel Task Processing with Known and Unknown Processors

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2020年第1期31卷 187-200页

作者： Champati, Jaya Prakash Liang, Ben KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Informat Sci & Engn S-11428 Stockholm Sweden Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

We study the problem of scheduling n tasks on m + m ' parallel processors, where the processing times on m processors are known while those on the remaining m ' processors are not known a priori. This semi-online model is an abstraction of certain heterogeneous computing systems, e.g., with them known processors representing local CPU cores and the unknown processors representing remote servers with uncertain availability of computing cycles. Our objective is to minimize the makespan of all tasks. We initially focus on the case m ' = 1 and propose a semi-online algorithm termed Single Restart with Time Stamps (SRTS), which has time complexity O(nlogn). We derive its competitive ratio in comparison with the optimal offline solution. If the unknown processing times are deterministic, the competitive ratio of SRTS is shown to be either always constant or asymptotically constant in practice, respectively in cases where the processing times are independent and dependent on m. A similar result is obtained when the unknown processing times are random. Furthermore, extending the ideas of SRTS, we propose a heuristic algorithm termed SRTS-Multiple (SRTS-M) for the case m ' > 1. Finally, where tasks arrive dynamically with unknown arrival times, we extend SRTS to Dynamic SRTS (DSRTS) and find its competitive ratio. Besides the proven competitive ratios, simulation results further suggest that SRTS and SRTS-M give superior performance on average over randomly generated task processing times, substantially reducing the makespan over the best known alternatives. Interestingly, the performance gain is more significant for task processing times sampled from heavy-tailed distributions.

关键词： computational offloading edge computing mobile cloud computing opportunistic computing unknown processing times task restart semi-online algorithms

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Revenue Maximization in Delay-Aware Computation offloading Among Service Providers With Fog Federation

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IEEE COMMUNICATIONS LETTERS 2020年第8期24卷 1799-1803页

作者： Mukherjee, Mithun Kumar, Vikas Lloret, Jaime Zhang, Qi Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Bharat Sanchar Nigam Ltd Patna 800001 Bihar India Univ Politecn Valencia Integrated Management Coastal Res Inst Valencia 46022 Spain Aarhus Univ Dept Engn DIGIT DK-8000 Aarhus Denmark

In this letter, we study the computational offloading scheme for the delay-aware tasks of the end-users in the fog computing network. We consider a fog federation of different service providers where an individual fog node allocates its computing resources to the end-user in its proximity, while a fog manager coordinates the load balancing among the fog nodes over the entire network. At first, an individual fog node aims to maximize its revenue by selling the computational resources to the end-user in a distributed manner without any global knowledge of the network. To further maximize the overall revenue considering all fog nodes in the fog federation, the fog manager utilizes the remaining computing resources of the underloaded fog nodes. The extensive simulation results show the revenue improvement leveraging fog federation over entire network while maintaining the same and even better delay-performance for the end-users.

关键词： Task analysis Delays computational modeling computational efficiency Pricing Frequency modulation Collaboration Fog computing fog federation pricing computational offloading delay-sensitive tasks

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Design and implementation of an IoT-cloud converged virtual machine system

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JOURNAL OF SUPERCOMPUTING 2020年第7期76卷 5259-5275页

作者： Son, Yunsik Jeong, Junho Lee, YangSun Dongguk Univ Dept Comp Sci & Engn 30 Phildong Ro 1gil Seoul 04620 South Korea Seokyeong Univ Dept Comp Engn 124 Seokyeong Ro Seoul 02713 South Korea

This paper presents an internet-of-things (IoT)-cloud converged virtual machine (VM) system for IoT devices with restricted computing resources. The VM is a software processor that has many advantages in terms of software development, release, maintenance, etc., owing to its platform independence. However, in low-performance devices it has a significant disadvantage because its use is restricted by high execution overheads at the software interpretation level. This paper proposes a VM system for IoT devices that solves this problem while retaining the advantages of VM technology using a lightweight interpreter model and cloud-based computation offloading. The proposed interpreter solves the limited memory/performance problem when running VMs on low-performance devices using two-level instruction-to-native function matching techniques. Furthermore, by solving the low-performance issues of IoT devices using cloud-based offloading, the proposed IoT-cloud VM can run applications that require high-performance computing even when the target hardware system is a low-power IoT device.

关键词： Internet of things Cloud system computational offloading Virtual machine

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The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications

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INTERNET OF THINGS 2020年 9卷

作者： Alli, Adam A. Alam, Muhammad Mahbub Islamic Univ Uganda IUIU Kumi Soroti RdPOB2555 Mbale Uganda Islamic Univ Technol Org Islamic Cooperat OIC Gazipur 1704 Bangladesh

The Fog computing paradigms are becoming popular means of utilizing resources optimally by the IoT devices, extending quality of service to the vicinity of the user, and achieve fast processing in the IoT-cloud ecosystems. Fog models allow fast processing of data, easy to reach storage, and reduce bulky network transition. The inefficiencies of the cloud inspire unnecessarily big data to be sent to the backhaul of the network, which incapacitates the cloud infrastructure. Fog computing addresses the limitation of the cloud systems by improving robustness, efficiency, and performance of cloud infrastructure. The need to process some of the big data produced at the peripheral of the network using keen techniques in the fog-cloud ecosystems is a key to new interesting architectures filed in the recent literature. These architectures provide new business opportunities that drive the Internet of things devices to function according to users' demands. In this paper, we provide an extensive survey on Fog-Edge computing to give a foundation to solutions proposed in studies that involve IoT-Fog-Cloud ecosystems. This is done by providing insights of new research aspects filed, the state-of-art in fog computing architectures, standards, tools and applications. We project the future development trends and provide open issues in fog cloud of things. This will focus developers to develop applications that work well in a cloud-based controlled ecosystem across a range of network terminals. (C) 2020ElsevierB.V. Allrightsreserved.

关键词： Big data analytics computational offloading Content delivery network Fogging Internet of things IoT-Fog-Cloud ecosystems Simulation tools Smart city applications Smart farm applications Web performance

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