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international conference on Parallel distributed and Grid computing (PDGC)

作者： Amit Chauhan Rajni Mohana Department of Computer Science and Information Technology Jaypee University of Information Technology Waknaghat Solan India

ISBN: (纸本)9781665454025

Extracting valuable information from unstructured text is a significant difficulty in today's culture due to the ever-increasing proliferation of texts, most of which are generated via online social networks in the form of consumer-generated media. Despite this difficulty, natural language processing algorithms are being developed rapidly, including topic modelling approaches for discovering latent subjects in text documents like online reviews. As a result, topic modelling methodologies have not grown in favour of the tourist industry yet. So Authors decided to work on a tourism dataset. The results show us the importance of topic keywords concerning the tourism dataset the best perplexity score we get us with the D3 Dataset was -7.9947.

关键词： Training Deep learning Sentiment analysis Social networking (online) User-generated content Tourism industry Semantics

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A Solution to the Techno-Economic Generation Expansion Planning Using Modified Remora Optimization Algorithm

A Solution to the Techno-Economic Generation Expansion Plann...

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Smart Generation computing, Communication and networking (SMART GENCON), international conference on

作者： Bimal Kumar Dora Sunil Bhat Sudip Halder Ishan Srivastava Department of Electrical Engineering VNIT Nagpur Nagpur India

In the recent decade Generation Expansion Planning (GEP) has received a lot of attention among academics and energy policymakers, especially to reduce the greenhouse gas (GHG) emissions such as CO 2 , N 2 O and CH 4 . Every country strives to decarbonize its economy by enacting policies that improve the penetration of Renewable Energy Sources (RES) in its power production capacity. GEP is a significant challenge that incorporates technological, economic, financial, geographical, and environmental factors. GEP is most commonly used to tackle large-scale, dynamic, discrete, and non-linear constrained optimization problems. This research provides a multidimensional GEP architecture based on boosting RES integration with the help of Modified Remora Optimization Algorithm. The simulation results are reviewed and compared with the previously reported results in the results section. The proposed technique demonstrates the improved convergence characteristics and resilience when compared to the other compared results.

关键词： Renewable energy sources Simulation Power system dynamics Production Organisms Planning distributed power generation

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Lightweight and Efficient distributed Cooperative Intrusion Detection System for Intelligent Swarms

Lightweight and Efficient Distributed Cooperative Intrusion ...

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2022 international conference on networking and Network Applications, NaNA 2022

作者： Li, Zhaoyang Zhang, Zhiwei Chen, Zehan Duan, Hao Li, Hongjun Ren, Baoquan School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Institute of Network Information Academy of Systems Engineering Academy of Military Sciences Beijing100141 China Institute of Systems General Academy of Systems Engineering Academy of Military Sciences Beijing100101 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Intrusion Detection Systems (IDSs) have been wildly used in various environments to actively detect internal and external attacks with high accuracy. Unfortunately, the traditional IDSs cannot distinguish new or unknown attacks from abnormal behaviors effectively, and that makes them infeasible to protect the emerging dynamic open information systems. Subsequently, artificial intelligence (AI) algorithms are introduced into IDSs to support the recognization of unexplored malicious behaviors. However, most of the existing AI-driven IDSs are not able to be directly applied to intelligent swarm scenarios, which are typically employed to aggregate heterogeneous or homogeneous elements (e.g., autonomous vehicles, drones) to solve complex problems that the individual members cannot deal with, due to the characteristics of mobility and complexity of intelligent elements. Therefore, in this paper, we propose a lightweight and efficient distributed cooperative IDS (DCIDS) for intelligent swarms. On one hand, to efficiently detect the malicious behaviors among swarm elements, we design a collaborative detection model which utilizes multi-dimension features including the swarm elements' position, storage-computing resource consuming levels, network traffics, et al. On the other hand, to predict the movement trends and detect attacks of resource-limited swarm elements, we construct a concrete DCIDS scheme by employing the Kalyan Filter algorithm and Long Short Term Memory Network (LSTM) algorithm. Furthermore, our experimental results demonstrate that the proposed DCIDS scheme outperforms the previous IDS schemes on attack detection/classification accuracy and efficiency in intelligent swarm environments and also achieves an accuracy of 98.00%. © 2022 IEEE.

关键词： Kalman filters

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Performance comparison of distributed in memory databases in dynamic fog environments

Performance comparison of distributed in memory databases in...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： George Vlahavas Anna-Valentini Michailidou Theodoros Toliopoulos Vasileios G. Psomiadis Anastasios Gounaris Athena Vakali School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

Fog computing is an extension of cloud computing to overcome limitations of the latter. It brings data processing and storage closer to edge devices, while offering higher processing power and storage space compared to the edge. In edge or fog environments, data is often ephemeral, so storing data in memory provides considerable performance benefits. Edge or fog nodes may also fail or disconnect, so distributed database systems are used in order to provide durability and availability. Database performance is an important factor when designing a fog platform. We assess the performance of two distributed, in memory databases, Apache Ignite and Redis, measured as throughput, with respect to different parameters: number of nodes, number of replicas, persistency, network bandwidth, network latency and number of operations. In all cases, Redis outperforms Apache Ignite, however, Apache Ignite scales very well when the number of operations is increased.

关键词： Performance evaluation Degradation Cloud computing distributed databases Bandwidth Benchmark testing Throughput

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy

Application Research of Vertical Federated Learning Technolo...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Yong Luo Zhi Lu Xiaofei Yin Songfeng Lu Yiting Weng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound.

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Kaki: Efficient Concurrent Update Synthesis for SDN

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FORMAL ASPECTS OF computing 2023年第3期35卷 20-20页

作者： Johansen, Nicklas S. Kaer, Lasse B. Madsen, Andreas L. Nielsen, Kristian O. Srba, Jiri Tollund, Rasmus G. Aalborg Univ Dept Comp Sci Selma Lagerlofs Vej 300 Aalborg Denmark

Modern computer networks based on the software-defined networking (SDN) paradigm are becoming increasingly complex and often require frequent configuration changes in order to react to traffic fluctuations. It is essential that forwarding policies are preserved not only before and after the configuration update but also at any moment during the inherently distributed execution of such an update. We present Kaki, a Petri game based tool for automatic synthesis of switch batches which can be updated in parallel without violating a given (regular) forwarding policy like waypointing or service chaining. Kaki guarantees to find the minimum number of concurrent batches and supports both splittable and nonsplittable flow forwarding. In order to achieve optimal performance, we introduce two novel optimisation techniques based on static analysis: decomposition into independent subproblems and identification of switches that can be collectively updated in the same batch. These techniques considerably improve the performance of our tool Kaki, relying on TAPAAL's verification engine for Petri games as its backend. Experiments on a large benchmark of real networks from the Internet Topology Zoo database demonstrate that Kaki outperforms the state-of-the-art tools Netstack and FLIP. Kaki computes concurrent update synthesis significantly faster than Netstack and compared to FLIP, it provides shorter (and provably optimal) concurrent update sequences at similar runtimes.

关键词： Computer networks software defined networking concurrent update synthesis security policies

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distributed Deep Reinforcement Learning Based Deterministic Task Offloading in End-Edge-Cloud Collaborative computing Networks 3rd

Distributed Deep Reinforcement Learning Based Deterministic ...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Yao, Yingfei Yao, Shunchun Zhou, Nan Liang, Xiaojun Yang, Chunhua Gui, Weihua South China University of Technology Guangzhou510640 China Pengcheng Laboratory Shenzhen518000 China Central South University Changsha410083 China

ISBN: (纸本)9789819645022

In the industrial field, the requirements for deterministic and reliable communication cannot be satisfied by current end-edge-cloud collaborative computing (EECC) networks. Time-sensitive networking (TSN) technology not only provides bounded latency guarantees but also gives support to integrate different types of traffic on a single network. However, the development of TSN-based EECC networks remains in its early stage. In light of this, we propose an intelligent end-edge-cloud collaborative deterministic task offloading framework that combines EECC and TSN networks. Based on distributed Deep Reinforcement Learning (DDRL), a deterministic task offloading algorithm named DDRL-E2CDTO is introduced to address the joint optimization problem of TSN traffic scheduling and end-edge-cloud task offloading. This algorithm employs TSN traffic scheduling (TTS) networks and deterministic task offloading (DTO) networks to train distributed agents to make instant and efficient deterministic decisions of task offloading. Simulation experiments demonstrate that our proposed approaches can obtain significant performance achievements in acceleration of convergence speed and computation rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Digital Twin Assisted DAG Task Scheduling Via Evolutionary Selection MARL in Large-Scale Mobile Edge Network

Digital Twin Assisted DAG Task Scheduling Via Evolutionary S...

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IEEE international conference on Communications Workshops, ICC

作者： Jiayu Huang Fanqin Zhou Lei Feng Wenjing Li Mingyu Zhao Xueqiang Yan Yan Xi Jianjun Wu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Huawei Technologies Co. Ltd. Shanghai China

6G is envisioned to serve many compute-intensive and latency-sensitive applications which are now usually formed by functional components in the directed acyclic graphic (DAG) form. Mobile edge computing (MEC) is proven a promising way to offload computing tasks and reduce execution latency. However, the ever-increasing number and heterogeneity of edge computing nodes, as well as the dynamically changing resource state, hinder any centralized scheduler from getting the accurate and timely resource state in the huge number of edge nodes. In this paper, facing a large-scale MEC scenario, we propose a digital twin (DT) assisted multiagent-based task scheduling scheme to jointly optimize task offloading and resource allocation strategy for DAG tasks. The scheme exploits the evolutionary selection multi-agent reinforcement learning (ES-MARL) framework to expedite the training of a large number of agents, while the DT of the mobile edge network keeps the latest state data and enables the centralized training of these distributed agents. The simulation result shows that the proposed scheme can achieve the goals of reducing latency and energy consumption while improving the scheduling success rate, load balancing index, and training efficiency.

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HRC-CLSS: An Hybrid Resource Constrained Certificate-Less Signcryption Solution for Securing Crowdsourced IIoT Environments

HRC-CLSS: An Hybrid Resource Constrained Certificate-Less Si...

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international conference on Inventive Research in computing Applications (ICIRCA)

作者： B Yamini A Srinivasan M. Selvi S. Sumithra T. Sathya G S Uthayakumar Department of Networking and Communication SRM Institute of Science and Technology Kattangulathur Chennai Tamil Nadu India Department of AI & DS Dhanalakshmi College of Engineering Manimangalam Chennai Tamil Nadu India Department of CSE Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of CSE Panimalar Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology OMR Chennai Tamilnadu India

The Industrial Internet of Things (IIoT) has become an essential component of modern industrial automation. However, the security of IIoT remains a significant challenge due to its distributed and heterogeneous nature. The proposed hybrid approach, called HRC-CLS, integrates symmetric and asymmetric encryption, Certificate-less Signcryption (CLS), and attribute-based encryption to provide confidentiality, integrity, authenticity, and access control in the IIoT environment. The proposed approach supports dynamic access control and fine-grained access policies. The results of the experiments indicate that the suggested scheme outperforms other CLS methods that are already in use in terms of a variety of performance measures. The strategy may find use in a variety of contexts, including the automation of manufacturing processes, the development of smart cities, and medical care.

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Load Balance in Cloud computing using Software Defined networking

Load Balance in Cloud Computing using Software Defined Netwo...

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2020 international conference on Computer, Control, Electrical, and Electronics Engineering, ICCCEEE 2020

作者： Omer, Yassin Abdulkarim Hamdalla Mohammedel-Amin, Mohamed Ayman Mustafa, Amin Babiker A. Al-Neelain University Faculty of Engineering Khartoum Sudan Future University Faculty-of-Telcommunication Khartoum Sudan

ISBN: (纸本)9781728191119

nowadays due to the high demand for cloud services a load balance mechanism for software defined networking (SDN) in a cloud environment appears to solve the availability and latency issues in the HTTP server. This paper presents a load balance mechanism that has been applied for a software defined networking (SDN) in a cloud environment for the HTTP server which received 6 million requests in total with 400 requests per time. The server will not respond to any further requests that have been transmitted from the client because it is overload. hence the solution is to apply a load balancing mechanism to measure the same parameters after applying load balance. In this scenario, HAproxy has been applied to the cloud environment and all of these 6 million requests with 4 connections per time have been received successfully, the HTTP server's traffic can be distributed among the pool of servers instead of one server this would. Eventually, The HAproxy is giving a good response time with no requests dropped after 6 million requests coming at the same time that help to enhance the availability and improve the latency of HTTP requests by having an HTTP load balancer. © 2021 IEEE.

关键词： Software defined networking

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