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共 1401 条记录，以下是1331-1340 订阅

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A Framework for Big Data Security Using MapReduce in IoT Enabled computing

A Framework for Big Data Security Using MapReduce in IoT Ena...

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Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： Kartikay Kala Kartik Makhloga Anayat Khan Aishwarya Pandey Saksham Mittal Computer Science & Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

With the increase in number of IoT devices day by day, a large amount of unstructured, structured and semi-structured data is being generated, collectively termed as big data. The amount of data stored and processed in the cloud is increasing at a rapid pace. The traditional storage devices cannot meet the requirement of the cloud at both hardware and software levels. There is a need for a mechanism which can handle this issue. Hadoop is a platform which can overcome this big data issue. Hadoop uses HDFS and Map Reduce to process large quantity of data in the cloud system. It is also essential to ensure big data security generated by IoT devices and only the authorized users can access the data. Therefore, this research aims to secure big data using RSA. MapReduce is used to process and encrypt big data as it helps in parallel processing of the data in few seconds. This research paper presents a comparative study focused on the encryption time performance in Hadoop, specifically examining the impact of increasing mappers on the overall encryption process.

关键词： Performance evaluation Scalability Systems architecture Big Data parallel processing Market research Software

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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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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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Optimizing Virtual Machine Live Migration without Shared Storage in Hybrid clouds

Optimizing Virtual Machine Live Migration without Shared Sto...

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IEEE international conference on High Performance computing and Communications

作者： Shan He Chunming Hu Bin Shi Tianyu Wo Bo Li State Key Laboratory of Software Development Environment (NLSDE) School of Computer Science and Engineering Beihang University Beijing China 100191

ISBN: (纸本)9781509042982

Virtual machine live migration technology allows a running VM migrates from one physical host to another with no impact on users. As the scale of distributed computing gets larger and larger, hybrid cloud, which is integrated cloud service utilizing both private and public clouds to perform distinct functions within the same organization becomes a hotspot for both academia and industry. Thus, it will be vital to migrate virtual machines in hybrid clouds. The biggest issue which VM migration in hybrid clouds faces today is how to transfer the large amount of storage data. Storage migration of virtual machine with existing mechanisms of memory migration has been studied for a long time. Unfortunately, no one can perform perfectly without modification. It is compelling to consider a new migration model and optimization methods. In this paper, we analyze the advantage and disadvantage of classical methods, and put forward a method which combines the strengths of pre-copy and post-copy migration model and we also use disk working set to optimize virtual machines storage migration. We have developed and implemented our approach to the QEMU/KVM hypervisor and run a series of experiments. The presented technique shows a reduction of up 20.5% on average of the total transfer time for most of the selected scenario. Our approach can also reduce the migration downtime in most cases.

关键词： working set virtual machine live migration storage

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Two-Factor distributed Authentication Scheme for cloud Storage

Two-Factor Distributed Authentication Scheme for Cloud Stora...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Yunxia Han Chunxiang Xu Shanshan Li Jingjie Lu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information Engineering Zhongnan University of Economics and Law Wuhan China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

distributed authentication plays a crucial role in cloud storage, requiring users to authenticate their identity to a set of key management servers. This process guarantees that only authenticated users gain access to cryptographic keys stored in these servers for decrypting outsourced data. How-ever, existing distributed authentication schemes rely solely on passwords, posing a critical weakness due to the recent surge in password-based attacks. Once passwords are compromised, adversaries can impersonate users, gaining unauthorized access to key management servers and acquiring cryptographic keys. To address this vulnerability, a recent multi-factor scheme has been proposed that introduces a distributed authentication mechanism for each popular factor such as passwords, Universal 2nd Factor (U2F), and email. Users could select multiple factors and utilize their corresponding mechanisms when authenticating to servers. Nevertheless, a simple combination of these authentication mechanisms incurs substantial overheads. In this paper, we propose a combined verification mechanism for passwords and U2F, allowing servers to efficiently verify both factors. Based on the mechanism, we present a two-factor distributed authentication for cloud storage, dubbed TDAC. In TDAC, users could authenticate to key management servers through a single password entry and a touch on their U2F. Experiments show that TDAC is efficient in terms of computation and communication overhead.

关键词： cloud computing Costs Communication systems Authentication Passwords Multi-party computation Computational efficiency

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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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Fast Community Detection in Large Weighted Networks Using GraphX in the cloud

Fast Community Detection in Large Weighted Networks Using Gr...

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IEEE international conference on High Performance computing and Communications

作者： Xiao Ling Jiahai Yang Dan Wang Jinfeng Chen Liyao Li Tsinghua National Laboratory for Information Science and Technology Institute for Network Sciences and Cyberspace Tsinghua University Department of Computing The Hong Kong Polytechnic University School of Electronic and Information Engineering Fuqing Branch of Fujian Normal University

ISBN: (纸本)9781509042982

Identification of different communities in large weighted networks is of crucial importance since it helps to uncover priori unknown functional modules such as topics in information networks or cyber-communities in social networks. However, the typical size of networks, such as social network services or World Wide Web, now counts in millions of nodes and is computationally complex. This urgently demands feasible methods and available computing platforms to retrieve their structure efficiently. To address this problem, we propose an algorithm Fast Community Detection (FastCD) based on modularity optimization. Furthermore, FastCD easily supports parallel computation. We implement FastCD with GraphX, which is an embedded graph processing framework built on top of Apache Spark. After carrying out comprehensive experiments in a 16-nodes cluster (32 vCPU) on Amazon EC2, the results indicate that FastCD not only outperforms the state-of-the-art algorithms in terms of computation time, but also guarantees the accuracy of the solutions under different real-world networks commonly used for efficiency comparison.

关键词： GraphX Community Detection Modularity Optimization FastCD

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Real-Time Digital Twin of Residential Energy Storage System for Cyber-Security Study

Real-Time Digital Twin of Residential Energy Storage System ...

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Smart Technologies for Power, Energy and Control (STPEC), IEEE international conference on

作者： Amardeep B. Shitole Nandha Kumar Kandasamy Lin Shen Liew Lewin Sim Anh Khoa Bui Center for Research in Cyber Security Singapore University of Technology and Design Singapore Singapore Institute of Technology Singapore Panasonic R&D Center Singapore

ISBN: (纸本)9781665443203

Modern Residential Energy Storage Systems (RESSs) are monitored and controlled using cloud based Energy Management System (CEMS). Analyzing the cyber-security of such RESSs controlled by CEMS is critical for developing appropriate Intrusion Detection Systems against cyber attacks targeting such RESSs. However, it is impractical and expensive to modify, scale and replicate the physical RESS as per the cyber-security study requirements. This paper presents, a lowcost Real-Time Digital Twin (RTDT) of an interconnected and distributed RESSs controlled and monitored via CEMS. Systematic procedure for the development of RTDT of a Cyber-Physical System (CPS) is described in detail. The proposed RTDT of a physical RESS test-bed gives flexibility to modify, scale and replicate the system without compromising its real-time fidelity. The development procedure can be easily replicated to develop RTDT of any CPS or micro-grid test-beds. To minimize RTDT development cost, single board computer with Simulink Desktop Real-Time is used instead of using expensive real-time simulators. The CEMS graphical user interface of the RTDT gives flexibility to control interconnected RESSs via the internet cloud. An experimental case study is presented to verify the performance of the RTDT. The RTDT is developed for a commercially available RESS and CEMS, and not for a theoretical system.

关键词： cloud computing Systematics Costs Software packages Digital twin Real-time systems Monitoring

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Multi-Task Scheduling Method for Information Security of Heterogeneous Big Data Networks Based on Artificial Intelligence Technology

Multi-Task Scheduling Method for Information Security of Het...

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Sensors, Electronics and Computer engineering (ICSECE), IEEE international conference on

作者： Yiping Chen Yingzi Zheng Department of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

Multi-task scheduling of heterogeneous big data network information is proposed to improve the optimal configuration and application ability of network big data resources. A multi-task scheduling method of heterogeneous big data network information security based on artificial intelligence technology is proposed. A distributed storage and transmission structure model of heterogeneous big data is established under the task scheduling framework of cloud computing, and the heterogeneous mapping relationship is described according to the task submitted by users and the network information distribution between virtual machines. This paper establishes a scheduling channel between virtual machines and hosts, reasonably places and migrates heterogeneous big data network information to suitable hosts through artificial intelligence technology, and realizes reasonable allocation of computing resources and multi-task scheduling by using weighted polling scheduling method, so as to realize load-balanced scheduling of heterogeneous network information. The simulation results show that this method has good load balance in information task scheduling of heterogeneous big data networks, and the scheduling method has excellent global search and parallel search capabilities, which can adapt to large-scale information optimization of heterogeneous big data networks.

关键词： Processor scheduling Simulation Information security Big Data Multitasking Data models Resource management Artificial intelligence Optimization Load modeling

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Voltage Profile Enhancement Using Capacitor-less D-STATCOM to Increase PV Integration in Distribution Network Under Transient cloud Conditions

Voltage Profile Enhancement Using Capacitor-less D-STATCOM t...

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Photovoltaic Science and Technologies (PVCon)

作者： Wesam Rohouma Robert S. Balog Aaqib A. Peerzada Miroslav M. Begovic School of Engineering Technology and Industrial Trades College of North Atlantic Qatar Doha Qatar Renewable Energy and Advanced Power Electronics Research Lab (REAPER Lab) Texas A&M University at Qatar Doha Qatar Texas A&M University College Station TX USA

Over the past decade, the emergence of distributed Energy Resources (DERs) has significantly disrupted the traditional approach of having centralized plants that distribute electricity through an extensive transmission and distribution network. Given the proliferation of DERs in recent years, more importantly, the rise of grid-connected distributed photovoltaic (PV) systems and the implementation of the smart-grid concept, the electric utility industry of today is in the midst of an aggressive expansion and an unprecedented evolution. The stochastic nature of PV output has created new challenges in electric distribution systems' operation and maintenance. Of particular significance is the direct correlation between the PV power output and rapid voltage fluctuations. Reactive power compensation can be used to regulate the voltage profile on the network within the allowable limits set by the relevant standards. A capacitor-less distribution static synchronous compensator (D-STATCOM), based on a matrix converter (MC), is proposed to be used in low-voltage distribution networks. This technology is controlled using finite-set model predictive control (MPC); the compensator device can operate in various modes and can be deployed as needed in the low-voltage distribution network. Simulation results is presented to show the proposed technology's effectiveness in improving voltage profile in low-voltage distribution networks under transient cloud conditions.

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