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2012 IEEE 2nd international conference on cloud computing and Intelligence Systems

作者： Zhong Zheng Yanming Chen Jiapeng Xiu Beijing University of Posts and Telecommunications China Mobile Group Design Institute Co. Ltd School of Software Engineering Beijing University of Posts and Telecommunications

With the popularization of cloud services, understanding and analysing the basic characteristics of cloud computing, using it is features to design and optimize cloud services becomes more and more urgent and important. In reality, cloud computing has some irregular features: it is widely individual distributed, its individual flow is uneven, and is widely used, which requires that we first create a cloud model for laboratory studies, and to disseminate to the physical research. The composition of the cloud of individuals are numerous and scattered, makes us unable to go in search of a model from the details of the place, but from the large-scale ,traffic aggregation always follow the law of a particular point of view, and the complex network is the most common is to follow self-similar traffic. Therefore, the establishment of self-similar traffic model analysis of cloud computing network flow model is appropriate, and universal. NS2 is the current ideal simulation of large-scale network traffic software;however, the NS2 simulation environment is not self-similar flow generator. On this basis, the NS2 simulation platform design, implement and validate a self-similar traffic model based on the pareto. Based on the basic cloud computing network platform above, the in-depth analysis of cloud computing traffic, is of great significance for the study regarding the cloud computing performance, management, protocol, and QOS.

关键词： cloud computing Self-similar modeling NS2 Flow generator

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Performance Optimization in distributed Multi-Agent Reinforcement Learning on cloud-Based Platforms

Performance Optimization in Distributed Multi-Agent Reinforc...

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international conference on computing and Networking Technology (ICCNT)

作者： Binod Kumar Choudhary Deepak Kumar Seema Sambargi Department of Electrical & Electronics Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Mathematics Vivekananda Global University Jaipur India Department of Management School of Management - UG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Dispensed multi-agent reinforcement learning (MARL) is an effective tool for tackling tough manage troubles by means of allowing coordination and cooperation among multiple agents. In cloud-based platforms, MARL can benefit from the availability of compute resources for education, but they face precise scalability and optimization-demanding situations. In this paper, we endorse a collection of methods for improving the overall performance of disbursed MARL in cloud-primarily based structures. We attention on two elements of overall performance optimization for scaling out distributed MARL: implementation optimization and optimization across a couple of stages of education. First, we advocate numerous strategies for enhancing the implementation of dispensed MARL, which include optimizing reminiscence usage and introducing size-aware optimizers. 2nd, we propose methods of optimizing allotted MARL across one-of-a-kind phases of education, inclusive of optimization for hyper parameter selection and optimizing education convergence. Our experiments display that our proposed methods improve the performance of distributed MARL in cloud-primarily based platforms, leading to higher scalability and quicker mastering.

关键词： Fault tolerance Scalability Education Fault tolerant systems Reinforcement learning Optimization Programming profession Faces Convergence

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Analysis of WiFi Spatio-Temporal Data for Organic Fingerprinting-based Indoor Positioning System

Analysis of WiFi Spatio-Temporal Data for Organic Fingerprin...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), international conference on

作者： Abdul Halim Ismail Amirah Husna Mohd Hajazi Muhamad Safwan Muhamad Azmi Mohd Sani Mohamad Hashim Mohd Nasir Ayob Hassrizal Hassan Basri Faculty of Electrical Engineering Technology Intelligent Computing and System Informatics Research Group (ICSI) Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP)

ISBN: (纸本)9781665465427

The mobile robot navigation is the next huge topic after positioning utilizing fingerprinting-based Wireless Positioning System (WPS). Many of recent works does not discuss this topic yet since many open problems in positioning topic are not yet solved, for instance the issues on multi-devices heterogeneity, instability of WiFi signals, granularity problems in grid-based indoor environment and many others. However, we anticipate that both positioning and navigation works must run in parallel so that the succession are guaranteed. This paper describes the analysis of spatio-temporal data of the signal obtained from the WiFi Access Point. Initial results suggest that the difference between transmitter heights have an effect on the spatio-temporal data while the handover of maximum signal strengths is inherent when three WiFi APs are used.

关键词： Wireless communication Electrical engineering Navigation Transmitters Fingerprint recognition Handover Indoor environment

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True 3D Holography: A Communication Service of Tomorrow and Its Requirements for a New Converged cloud and Network Architecture on the Path to 6G

True 3D Holography: A Communication Service of Tomorrow and ...

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6G Networking (6GNet), international conference on

作者： Ingo Friese Mandy Galkow-Schneider Louay Bassbouss Alexander Zoubarev Andy Neparidze Sergiy Melnyk Qiuheng Zhou Hans D. Schotten Tobias Pfandzelter David Bermbach Arndt Kritzner Enrico Zschau Prasenjit Dhara Steve Göring William Menz Alexander Raake Wolfgang Rüther-Kindel Fabian Quaeck Nick Stuckert Robert Vilter Deutsche Telekom AG Berlin Germany Fraunhofer FOKUS Berlin Germany Intelligent Networks German Research Center for Artificial Intelligence (DFKI GmbH) Kaiserslautern Germany Institute for Wireless Communication and Navigation University of Kaiserslautern-Landau Kaiserslautern Germany Mobile Cloud Computing Group Technische Universitat Berlin & ECDF Berlin Germany Logic Way GmbH Schwerin Germany SeeReal Technologies GmbH Dresden Germany Audiovisual Technology Group Technische Universitat Ilmenau Ilmenau Germany Aviation Engineering Group Technical University of Applied Sciences Wildau Wildau Germany

Research project 6G NeXt is considering true 3D holography as a use case, setting requirements on the communication as well as the computing infrastructure. In a future holographic communication service, clients are widely spread in the network and cooperatively interact with each other. Especially for holographic communication high processing power is required as well. This makes a high-speed distributed backbone computing infrastructure, which realizes the concept of split computing, inevitable. Furthermore, tight integration between processing facili-ties and wireless networks is required in order to provide an immersive user experience. This paper illustrates true 3D holographic communication and its requirements. Af- terward, an appropriate solution approach is elaborated. Here, novel technological approaches are discussed based on a proposed overall communication and computing architecture.

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AAWAS: An Application and Workload-Aware Scheduling Technique for Efficient Task Allocation in cloud computing Environments

AAWAS: An Application and Workload-Aware Scheduling Techniqu...

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Advancements and Key Challenges in Green Energy and computing (AKGEC), international conference on

作者： Vidhyasagar Bs M. Arvindhan J. Arunnehru H. Anwarbasha Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Amity School of Engineering & Technology Gwalior Madhya Pradesh India SRM Institute of Science and Technology Chennai India Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

One interesting aspect of distributed systems is cloud computing. It offers its services on an as-needed, pay-per-use basis. Research challenges in cloud computing include the scheduling and balance of tasks. Scheduling tasks entails assigning them to available resources (Virtual Machines), whereas load balancing is spreading out the work over multiple available resources. We offer a job scheduling technique that takes into account anticipated demands on compute nodes. We begin by investigating the reasons for the disparity in workloads and the viability of redistributing computing resources. All of the complex simulations are run in clouds. The process consisted of two distinct parts. The simulation begins with a randomly generated workload. Second, we combine the workload prediction model with an application and workload-aware scheduling algorithm (AAWAS) [14]. We present a parallel job scheduling approach using computational node workload prediction AAWAS to simplify the AAWAS algorithm. Experimental results reveal that AAWA is superior to other algorithms in terms of minimizing makespan and optimizing resource utilization, and the proposed method is compared to FCFS, SJF, Min-Min, and EDF. To make Task scheduling in compute clouds faces various challenges due to the highly dynamic environment, such as availability, access policies, security, and reliability of computing resources. Furthermore, the collaborative nature of cloud computing projects on the internet adds complexity to task scheduling. From simulation results, it is evident that our proposed scheduler AAWAS outperforms A2C, DQN, and PPO across the board by 4.1%, 2.6%, and 3.4%, respectively. Because AAWS relies on policy for learning rather than Q-value, its superiority can be defended. Several environmental factors affect AAWAS and contribute to its consistent learning process and the high chance of accurate predictions. As a result, AAWASC may learn from experiences independently of target networks,

关键词： cloud computing Protocols Scheduling algorithms Computational modeling Throughput Prediction algorithms Virtual machines Resource management Faces Load modeling

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Hierarchical Model of cloud & Client computing based on Multi-agent

Hierarchical Model of Cloud & Client Computing based on Mult...

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2012 2nd international conference on Advanced Materials and Information Technology Processing(AMITP 2012)

作者： Xu Xiao-long Cheng Chun-Ling Xiong Jing-Yi College of Computer Nanjing University of Posts and Telecommunications

In order to explore the potential ability of cloud computing utilizing resources further,a new model of cloud computing is proposed in this paper,which divides the cloud & client computing environment into several layers in accordance with the type of node and adopts agent to encapsulate the behaviors and resources of node to achieve more efficient resource sharing and collaboration *** model takes full account of a variety of potential resources available owned by terminal nodes and deploys a variety of tasks divided from a job in an orderly manner to the core nodes,the general server nodes and terminal nodes in order to achieve the goal of maximizing resource *** further assure tasks can be completed efficiently and reliably,a specific cloud-coins-based incentive mechanism is also proposed,which can stimulate terminal nodes contributing its resources and working together ***,in this paper we present how to construct the experimental system and application demonstration of the hierarchical model of cloud & client computing based on multi-agent for reference,which also shows that our model is effective and feasible.

关键词： distributed computing cloud computing Multi-agent Task allocation Incentive

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Seagull-optimized Deep Convolutional Neural Network for Detecting distributed Denial of Service Attack in cloud computing

Seagull-optimized Deep Convolutional Neural Network for Dete...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), international conference on

作者： Yogesh B. Sanap Pushpalata G. Aher Computer Science and Engineering Sandip University Nashik Sandip University Nashik Maharashtra

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Due to the enormous benefits and applications of cloud computing, there is a lot of interest from researchers on cloud computing. The most significant security risk associated with cloud computing is insider distributed denial of service (DDoS), which affects the entire deactivation of services due to its dispersed architecture and utter dependency on the internet for service supply. Machine learning and Deep learning have appeared as a potent tool in cyber security recently, and numerous attack detection methods have been put out in recent studies. Nevertheless, existing studies on attack detection encounter several challenges, including data imbalance, interpretability issues, inaccurate detection, overfitting, and time complexity issues. Therefore, in order to overcome these difficulties, a deep learning model, Seagull optimized Synthetic Minority Over-sampling Technique enabled Deep Convolutional Neural Network (SgO-SCNN) is developed in this research to detect DDoS attacks. The optimized SMOTE employed in the model addresses the imbalanced data issue and improves the model’s performance by effectively rebalancing the data. The DCNN classifier can detect the attacks more efficiently and reduces the computation of the model. The seagull optimization tunes the DCNN classifier’s parameters and improves the model’s performance in attack detection with fast convergence, which reduces the model’s time complexity. The experimental outcomes indicate that the developed $\mathrm{SgO}-\mathrm{SCNN}$ model outperforms the existing approaches by attaining enhanced values of accuracy 96.05%, F1-score 94.66%, precision 94.84%, and recall 94.48%.

关键词： cloud computing Accuracy Computational modeling Denial-of-service attack Data models Convolutional neural networks Computer crime Time complexity Optimization Overfitting

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A Decentralized Vehicle-to-Vehicle Energy Trading System Based on Efficient Sharding Services

A Decentralized Vehicle-to-Vehicle Energy Trading System Bas...

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IEEE international conference on Big Data and cloud computing (Bdcloud)

作者： Kun Meng Lijun Sun Xiao Chen Haiqin Wu Shuaiyong Li College of Information Science and Technology Qingdao University of Science and Technology Shandong China School of Computing and Mathematical Sciences University of Leicester Leicester UK Software Engineering Institute (Shanghai Key Laboratory of Trustworthy Computing) East China Normal University Shanghai China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing China

Vehicle-to-vehicle (V2V) energy trading stands as a significant technology, allowing electric vehicles (EVs) to share energy. This balances energy demand and supply, reducing pressure on the power grid. However, two substantial challenges require attention: 1) Creating a standardized protocol universally adoptable by EV manufacturers and energy trading participants to establish a common secure and reliable platform for real-time energy trading between EVs. 2) Determining a fair and transparent pricing and transaction management mechanism, accounting for factors such as energy demand and supply, location, and user preferences. To tackle these hurdles, this paper proposes a decentralized V2V energy trading system (VETS) based on a novel high-performance sharding blockchain framework. VETS incorporates a standard energy trading management protocol, achieving trustless and transparent transaction processing. It employs a blockchain sharding and parallel architecture for high transaction throughput, catering to a large number of EVs. Furthermore, an efficient consensus algorithm is developed to minimize service delays and adapt to real-time transaction demands. A practical testbed operationalizes VETS, facilitating experimental analysis within a real-world cloud environment.

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Customized web user interface for hadoop distributed file system

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Advances in Intelligent Systems and computing 2016年 380卷 567-576页

作者： Krishna, T. Lakshmi Siva Rama Ragunathan, T. Battula, Sudheer Kumar Hyderabad India Department of CSE ACE Engineering College Hyderabad India

ISBN: (纸本)9788132225225

distributed file system (DFS) is one of the main components of a cloud computing system used to provide scalable storage solutions for Big Data applications. hadoop distributed file system (HDFS) is one of the core components of Apache Hadoop project and many IT companies are using HDFS to store and manage Big Data. HDFS provides both command line and web-based interface to the users for storing and accessing data. The web-based user interface (WBUI) is used only for browsing the file system whereas the command line interface (CLI) is used for creating a file and performing read or write operations on the file. The CLI provides many more facilities and note that CLI is not user friendly as the user has to remember and type the commands to access the HDFS. In this paper, we propose a new customized web user interface (CWBUI) for the HDFS. We have developed CWBUI using servlets and java server pages (JSP) and deployed the same in a Hadoop cluster. The CWBUI is found to be very helpful in using the HDFS in an interactive manner without the need of typing commands in the user interface. © Springer India 2016.

关键词： File organization

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Implementing Molecular Dynamics Simulation on Sunway TaihuLight System

Implementing Molecular Dynamics Simulation on Sunway TaihuLi...

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IEEE international conference on High Performance computing and Communications

作者： Wenqian Dong Letian Kang Zhe Quan Kenli Li Keqin Li Ziyu Hao Xiang-Hui Xie College of Information Science and Engineering Hunan University Changsha Hunan 410082 China Department of Computer Science State University of New York New Paltz NY 12561 USA Jiangnan Institute of Computing Technology Wuxi Jiangsu 214000 China

ISBN: (纸本)9781509042982

Molecular dynamics (MD) simulation is a common tool to study the physical movements of atoms and molecules in many research fields. However, it is an extremely time-consuming application which takes researchers weeks or months to run a single simulation when simulation size scales up and computing demands keep growing. In this paper, an improved MD implementation on Sunway TaihuLight supercomputer is developed to solve the above mentioned issues. The new implementation is extended from an existing implementation (i.e., LAMMPS) which widely uses the MD application. Sunway TaihuLight is a heterogeneous supercomputer with a fully customized integration approach and a brand new many-core processor SW26010. The Sunway TaihuLight takes the world's first place with peak performance over 100PFlops. We propose an optimization method of the MD simulation in three steps: paralleled extensions to SW26010, memory-access optimizations, and vectorization. After the optimization process, an 8x speedup is achieved on a single computing node. Superiorly, we also scale up to 95 thousands computing nodes (6 millions cores) with an almost leaner speedup. Besides, the proposed methods can also be applied to many other molecular dynamics codes, or similar applications.

关键词： Sunway TaihuLignt Molecular Dynamics Simulation High performance computing parallel distributed computing

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