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ECTSA: An Efficient Charging Time Scheduling Algorithm for Wireless Rechargeable UAV network 20

ECTSA: An Efficient Charging Time Scheduling Algorithm for W...

引用

IFIP networking conference and Workshops (IFIP networking)

作者： Li, Mengqi Liu, Liang Xi, Jie Ma, Zuchao Xu, Xiangyu Wang, Lisong Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9783903176393

With the development of airborne equipment and integrated avionics technology, the unmanned aerial vehicle (UAV) network replaces human beings in many fields. To improve the durability of the UAV network, we propose a nondisruptive wireless rechargeable UAV network (WRUN) model, in which UAVs can be charged by wireless static chargers (WSCs) without returning back to the charging platform. Under the nondisruptive WRUN model, a baseline algorithm is proposed to solve the nondisruptive charging time schedule problem (nCTSP), in which chargers do not release energy all the time and can ensure UAVs do not run out of energy. Then to improve the energy utilization rate of WSCs, we propose an efficient charging time scheduling algorithm (ECTSA), in which the flight time and paths of UAVs are discretized and nCTSP is transformed into a linear binary integer programming (LBIP) problem to calculate the efficient charging time periods of WSCs. Finally, experiments are conducted to verify that ECTSA can improve the energy utilization of WSCs.

关键词： UAV networks wireless charging UAV charging nondisruptive charging partial charging

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VLEO Satellite Radio Access network Slicing: A Hierarchical Deep Reinforcement Learning Approach

VLEO Satellite Radio Access Network Slicing: A Hierarchical ...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Juan Luo Ruoyu Xiao Peng Sun Ying Qiao College of Computer Science and Electronic Engineering Hunan University Changsha China

network slicing is a key technology of Radio Access networks (RAN), enabling support for heterogeneous service with various quality of service (QoS) requirements. However, effective RAN slicing solution remains a significant challenge in the time-varying satellite networks. Therefore, in this paper, a satellite RAN slicing framework with two time-scales and resource granularity is designed. In a large time-scale, radio resources are coarsely allocated to Very Low Earth Orbit (VLEO) satellites in the software-defined networking (SDN) level. In a small time-scale, radio resources and transmit power are finely allocated to ground users. An optimization problem is formulated to maximize ground users’ QoS satisfaction of slices and system Spectrum Efficiency (SE). So we propose a satellite network slicing algorithm based on a hierarchical deep reinforcement learning framework (SNS-HRL). In a large time-scale, the resource allocation problem in the SDN allocation level is modeled as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). QMIX algorithm is utilized to allocate radio resources to different VLEO satellites. In a small time-scale, the resource allocation problem among slices is modeled as a Markov Decision Process (MDP), and the Proximal Policy optimization (PPO) algorithm is employed to schedule radio resources in the satellite allocation level. Additionally, resource allocation among users is achieved through a combination of the Proportional Fairness (PF) algorithm and the Lagrange multiplier method. Furthermore, simulation results demonstrate the effectiveness of the proposed satellite RAN slicing algorithm.

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IO Challenges for Human Brain Atlasing using Deep Learning Methods - An in-depth analysis 27

IO Challenges for Human Brain Atlasing using Deep Learning M...

引用

27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Oden, Lena Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Pleiter, Dirk Forschungszentrum Julich JSC Julich Germany Forschungszentrum Julich Inst Neurosci & Med INM 1 Julich Germany Fernuniv Fak Math & Informat Hagen Germany

ISBN: (纸本)9781728116440

The use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficiently high rate is a known challenge. Facilitating processing of these datasets thus requires the ability to store petabytes of data as well as to access the data with very high bandwidth. In this work, we look at two Deep Learning use cases for cytoarchitectonic brain mapping. These applications are very challenging for the underlying IO system. We present an in depth analysis of their IO requirements and performance. Both applications are limited by the IO performance, as the training processes often have to wait several seconds for new training data. Both applications read random patches from a collection of large HDF5 datasets or TIFF files, which result in many small non-consecutive accesses to the parallel file systems. By using a chunked data format or storing temporally copies of the required patches, the IO performance can be improved significantly. These leads to a decrease of the total runtime of up to 80%.

关键词： Training Task analysis Deep learning Graphics processing units Brain Bandwidth Image resolution

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Proof of Clustering: an efficient and reliable blockchain-based clustering framework

Proof of Clustering: an efficient and reliable blockchain-ba...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Yamina Khenfouci Yacine Challal Laboratoire des Méthodes de Conception de Systèmes (LMCS) Ecole Nationale Supérieure d’Informatique (ESI) Algiers Algeria College of Computing and IT University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

We propose a novel blockchain-based collaborative clustering framework, designed to enhance the efficiency and reliability of data clustering by decentralizing the clustering process across multiple nodes within the blockchain network. The system incorporates a consensus mechanism called Proof of Clustering to achieve the accuracy and consistency of clustering. This results thereby in ensuring confidence and reliability of the final clusters and prevents any breaches or corruption. To improve the performance of clustering process in the decentralized blockchain environment, data parallelism is employed. Through extensive experimentation and performance evaluation, we demonstrate the effectiveness and efficiency of proof of clustering consensus in various scenarios. Comparative studies with traditional centralized and distributed clustering methods showcase the superiority of our proposed solution by in terms of computational speed and resource utilization. PoC improves the Silhouette Score by 5.05% and reduces the Davies-Bouldin Index by 22.93%, indicating better cluster cohesion and separation in the PoC framework.

关键词： Performance evaluation Data analysis Accuracy Collaboration Machine learning parallel processing Reliability engineering Consensus protocol Indexes Resource management

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Trace-based Workload Generation and Execution 27th

Trace-Based Workload Generation and Execution

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27th International European conference on parallel and distributed Computing (Euro-Par)

作者： Sfakianakis, Yannis Kanellou, Eleni Marazakis, Manolis Bilas, Angelos Fdn Res & Technol Hellas FORTH Inst Comp Sci Iraklion Greece Univ Crete Dept Comp Sci Iraklion Greece

ISBN: (纸本)9783030856656;9783030856649

Although major cloud providers have captured and published workload executions in the form of traces, it is not clear how to use them for workload generation on a wide range of existing platforms. A methodological challenge that remains is to generate and execute realistic datacenter workloads on any infrastructure, using information from available traces. In this paper, we propose Tracie, a methodology addressing this challenge, and introduce the tool supporting its implementation. We present all the necessary steps starting from a trace up to workload execution: analysis of datacenter traces, extraction of parameters, application selection, and scaling of a workload to match the capabilities of the underlying infrastructure. Our evaluation validates that Tracie can generate executable workloads that closely resemble their trace-based counterparts. For validation, we correlate the recorded system metrics of a trace against the actual execution. We find that the average system metrics of synthetic workloads differ at most 5% compared to the trace and that they are highly correlated at 70% on average.

关键词： Trace Workload Simulation Cloud computing Benchmarking

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ST-PINN: A Self-Training Physics-Informed Neural network for Partial Differential Equations

ST-PINN: A Self-Training Physics-Informed Neural Network for...

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International Joint conference on Neural networks (IJCNN)

作者： Junjun Yan Xinhai Chen Zhichao Wang Enqiang Zhoui Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x.

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Nature Inspired Meta-Heuristic Algorithms based Load Balancing in Fog Computing Environment

Nature Inspired Meta-Heuristic Algorithms based Load Balanci...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Vijaita Kashyap Rakesh Ahuja Ashok Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara Punjab India

ISBN: (纸本)9781665454025

LB is simply the methodical distribution of load among many servers. To expedite the handling of customer requests, the fog server manages the substantial data on the cloud server. Data requirements are increasing, and Fog computing (FC) has made data more readily available. Despite the fact that FC overcomes many issues based on user requirements, other challenges still necessitate further effort. The challenge that FC confronts is Load Balancing(LB), which causes an increase in network layer traffic. The cloud layer has previously offered several LB solutions, but development on the fog layer is currently underway. Inadequate LB can lower service quality by delaying response time, processing time, security, and other concerns. This paper describes nature-based meta-heuristic algorithms that are based on LB and are used to the problem of overloaded data on the network. This review has explored some of the unresolved issues of the researchers problems and also a brief review on the advantages and disadvantages of the existing literature’s. Some of the challenges has also been discussed. Also, when it comes to LB, writers have concentrated on bandwidth, response time, latency, cost, security, and execution time.

关键词： Data analysis Metaheuristics Bandwidth Load management Grid computing Delays Servers

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The application of BP neural network algorithm to risk evaluation of high-tech entrepreneurial projects 4

The application of BP neural network algorithm to risk evalu...

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4th International conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021

作者： Li, Xiaofeng Guo, Pan Business School Sichuan University Department of Management Science and System Science Chengdu China

ISBN: (纸本)9781665415965

Back propagation (BP) neural network is characterized by the advantages of large-scale parallel, distributed processing, self-organization, and self-learning. It is suitable for handling problems that need to consider multiple conditions and fuzzy information simultaneously. Firstly, this paper expounds on the construction and algorithm of the BP neural network. Then, the risk index system of high-tech entrepreneurial projects was designed, and the evaluation levels and scoring method for the risk factor indicators were determined. Finally, we developed a model for risk evaluation of high-tech entrepreneurial projects based on the BP neural network. The empirical results show that the BP neural network has strong feasibility and effectiveness, and can accurately fit the training values to derive accurate prediction results. This method provides a new approach for risk evaluation of entrepreneurial projects. © 2021 IEEE.

关键词： Neural networks

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parallel Deployment of VNFs in Service Function Chain: Benefit or Not?

Parallel Deployment of VNFs in Service Function Chain: Benef...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Kaiwen Ning Hao Wang Zhiheng Zhang Zhou Xu Xiaowei Shu School of Software Engineering Dalian University of Technology China

network Function Virtualization (NFV) technology facilitates flexible and fast service provisioning by deploying network functions in wished positions and chaining order as Service Function Chain (SFC). Empirically, if the processing inside some Virtual network Functions (VNFs) does not relate to each other, parallel chaining them can further improve the performance, such as reducing the end-to-end hops of SFCs. However, it is often ignored that parallel deploy VNFs will also introduce additional VNFs to replicate, distribute, and merge data. This will add hops and increase resource consumption. Will parallel deployment of VNFs in SFCs necessarily bring benefits? Regarding multiple parallel forms of an SFC, which one is the best? Further, how does the service process of the NFV network affect the parallel decisions of VNFs? In this paper, we design an NFV orchestrator, DP-NFVO, to maximize the revenue of NFV Service Providers and provide quantitative insights into the impact of parallel VNFs deploy-ment. Precisely, we first refine existing VNF parallelism rules and discuss the impact of VNF parallelism on latency and resource consumption, and analyze the optimal form of parallelism for SFCs. Second, we decompose the service request process in NFV networks into multiple stages and provide a joint model of VNF parallelism and deployment. Further, we design a VNF online deployment and parallelism decision policy for DP-NFVO based on Proximal Policy Optimization (PPO) to maximize revenue. Finally, we demonstrate the effectiveness and adaptability of DP-NFVO on parallel processing and the requests service process with extensive experiments.

关键词： Adaptation models Service function chaining parallel processing network function virtualization Delays Optimization

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Greedy Multi-cloud Selection Approach to Deploy an Application based on Microservices 27

Greedy Multi-cloud Selection Approach to Deploy an Applicati...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Carvalho, Juliana Vieira, Dario Trinta, Fernando Fed Univ Piaui UFPI Picos Brazil EFREI PARIS Paris France Fed Uniers Ceara UFC Fortaleza Ceara Brazil

ISBN: (纸本)9781728116440

The multi-cloud delivery model allows the deployment of a distributed application in a manner in which the software architect chooses the services that better meet the application and the software architect's requirements. However, choosing cloud services is a complex task for a software architect, as there are several cloud providers that offer many services, which makes the selection process from the user perspective a relevant research problem. Therefore, in this paper, we propose a multiple providers selection approach to host a distributed application based on microservices to facilitate the software architect's decision-making. Our approach differs from others because each microservice is deployed in a single provider that better meets requirements from the software architect's perspective. Hence, our approach selects various services of a single provider for a microservice, and a distinct provider for each microservice, which makes this a complex task. We also use multi-criteria decision-making to rank the cloud services, and we map the selection process to multi-choice knapsack problem, which we develop using a greedy algorithm. Furthermore, we implement a tool to evaluate our approach performance, and the results show our approach feasibility, varying the number of cloud providers and services, microservices and requirements.

关键词： multi-cloud selection microservices multi-requirements multi-choice knapsack problem greedy algorithm

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