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VEC-MOTAG: Vehicular Edge computing Based Moving Target Defense System 11th

VEC-MOTAG: Vehicular Edge Computing Based Moving Target Defe...

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11th international conference on Computer Engineering and Networks, CENet2021

作者： Zhang, Bingchi Yang, Shujie Zhang, Tao Ji, Weixiao Ding, Zhongyi Shen, Jiahao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811665530

Nowadays, with the rising of intelligent vehicles and the gradual maturity of the concept of intelligent transportation, the demand of vehicles for communication and computing power in VANET is rapidly increasing. Therefore, a new network computing mode, vehicular edge computing, is used in the VANET environment to make the data processing and analysis of vehicles closer to the edge, so as to improve the network performance and quality of service. However, a large number of distributed edge servers also make the network structure more complex, which will lead to a great increase in the risk of attacks on edge servers distributed near the road. Also, the collapse of network services will bring potential dangers to traffic safety. Moving Target Defense (MTD) as a popular active defense technology can screen out malicious users and block them to protect the system’s service through reconstructing the mapping between agents and customers under shuffling mechanism. However, due to the dynamic VANET environment, the existing MTD technology is difficult to be directly used in it. In this paper, we propose a vehicular edge computing based MOTAG system (VEC-MOTAG), which can protect edge computing nodes from DDoS attacks. Different from the traditional mechanism, we formulated a series of constraints to improve the shuffling algorithm. In this way, the system can distinguish malicious users and guarantee the quality of service of the network. The simulation proves that VEC-MOTAG system has good defense capability and can ensure the quality of network service. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Mitigating Security Challenges in Edge computing: Attacks, Defence Strategies, and Algorithms

Mitigating Security Challenges in Edge Computing: Attacks, D...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Venkata Durga Rao Matta V.V.R. Maheswara Rao Venkata Sai Jayasurya Battula Mahesh Babu Addagatla Computer Science and Engineering Department Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Computer Science and Engineering Department Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Edge computing has emerged as a crucial paradigm to fulfill the increasing demand for rapid data processing, low latency, and efficient resource utilization, particularly in applications such as the Internet of Things (IoT), smart cities, autonomous vehicles, and automated industrial systems. By processing data closer to its source, edge computing minimizes reliance on centralized cloud infrastructures, thereby improving response times and optimizing bandwidth. However, this transition also presents significant security challenges, including heightened risks of distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized physical access to edge nodes. This paper conducts a comprehensive analysis of these security vulnerabilities, presenting a framework that integrates advanced defence mechanisms and algorithms tailored to enhance data protection and resource management in edge environments. Our findings indicate that the proposed strategies effectively mitigate identified threats while maintaining system performance. Furthermore, we outline ongoing challenges related to scalability, interoperability, and data governance, providing insights into future research avenues aimed at fortifying edge computing systems against evolving security threats. This study contributes to a deeper understanding of the critical role edge computing plays in the domain of next-generation computing technologies.

关键词： Technological innovation Scalability Data processing Real-time systems Security Resource management Internet of Things Interoperability Next generation networking Edge computing

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A Multi-Path Orchestration Method for Cloud-Edge Data Transmission Based on DRL

A Multi-Path Orchestration Method for Cloud-Edge Data Transm...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Zhihui Wang Xubing Dou Shuyun Li Huan Ma Xinchang Zhang Maoli Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China QuFu normal university Jining China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In addressing the multi-path problem of cloud-edge data transmission, this paper introduces a multi-path orchestration method based on deep reinforcement learning (DRL) within the context of software-defined networks (SDN), termed Deep Q-Routing (DQR). This method establishes optimal data transmission paths between the cloud and multiple edges, based on available bandwidth and transmission delay, while avoiding path congestion to improve bandwidth utilization and reduce transmission delay. The optimal routing in SDN is defined through the design of state space sets, reward functions, and neural networks. Simulation experiments were conducted to evaluate the DQR method. Compared to existing routing methods, the proposed DQR method, which includes a reward function with weighted parameters, demonstrates superior overall network performance.

关键词： Cloud computing Scalability Neural networks distributed databases Bandwidth Routing Deep reinforcement learning Delays Data communication Software defined networking

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Harnessing Artificial Intelligence to Promote Spiritual Intelligence Via Social Media

Harnessing Artificial Intelligence to Promote Spiritual Inte...

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international conference on Parallel distributed and Grid computing (PDGC)

作者： Rajender Jamwal Aman Sharma Tanu Sharma Humanities & Social Science Jaypee University of Information Technology Solan Computer Science & Engineering Jaypee University of Information Technology Solan

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Artificial Intelligence (AI) is redefining the paradigms of spiritual growth by harnessing the power of social media to promote Spiritual Intelligence (SI) in unprecedented ways. Through sophisticated algorithms and advanced data analytics, AI can curate personalized spiritual content that resonates deeply with individual users, fostering a more profound connection to inner consciousness and transcendent wisdom. By utilizing natural language processing, AI detects and amplifies trending spiritual themes, facilitating vibrant online communities dedicated to spiritual exploration and enlightenment. Machine learning models analyze user behavior to refine content delivery, ensuring that spiritual teachings and practices are accessible, relatable, and engaging. Furthermore, AI-driven chatbots and virtual guides provide round-the-clock support, answering spiritual queries and guiding users on their spiritual journey with unparalleled precision and empathy. This transformative synergy between AI and social media platforms transcends traditional barriers, democratizing spiritual wisdom and making it accessible to diverse audiences worldwide. By predicting and adapting to user needs, AI not only enhances personal spiritual growth but also cultivates a global culture of spiritual intelligence, unity, and collective consciousness, positioning itself as a formidable force in the evolution of human spirituality.

关键词： Machine learning algorithms Video on demand Social networking (online) Navigation Education Force Machine learning Grid computing Web sites Artificial intelligence

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distributed LSTM-based Slice Resource Allocation for Beyond 5G Networks

Distributed LSTM-based Slice Resource Allocation for Beyond ...

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international conference on computing, networking and Communications (ICNC)

作者： Ali Ehsanian Thrasyvoulos Spyropoulos EURECOM Sophia-Antipolis France Technical University of Crete Chania Greece

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

End-to-end network slicing is a new concept for 5G+ networks, dividing the network into slices dedicated to different types of services and customized for their tasks. A key task, in this context, is satisfying service level agreements (SLA) by forecasting how many resources to allocate to each slice. The increasing complexity of the problem setup, due to service, traffic, SLA, and network algorithm diversity, makes resource allocation a daunting task for traditional (model-based) methods. Hence, data-driven methods have recently been explored. Although such methods excel at the application level (e.g., for image classification), applying them to wireless resource allocation is challenging. Not only are the required latencies significantly lower (e.g., for resource block allocation per OFDM frame), but also the cost of transferring raw data across the network to centrally process it with a heavy-duty Deep Neural Network (DNN) can be prohibitive. For this reason, distributed DNN (DDNN) architectures have been considered, where a subset of DNN layers is executed at the edge (in the 5G network), to improve speed and communication overhead. If it is deemed that a “good enough” allocation has produced locally, the additional latency and communication are avoided; if not, intermediate features produced at the edge are sent through additional DNN layers (in a central cloud). In this paper, we propose a distributed DNN architecture for this task based on LSTM, which excels at forecasting demands with long-term dependencies, aiming to avoid under-provisioning and minimize over-provisioning. We investigate (i) joint training (offline) of the local and remote layers, and (ii) optimizing the (online) decision mechanism for offloading samples either locally or remotely. Using a real dataset, we demonstrate that our architecture resolves nearly 50% of decisions at the edge with no additional SLA penalty compared to centralized models.

关键词： Wireless communication Training 5G mobile communication Computational modeling Artificial neural networks Computer architecture Resource management

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Sampling and Output Estimation in distributed Algorithms and LCAs 21

Sampling and Output Estimation in Distributed Algorithms and...

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22nd international conference on distributed computing and networking (ICDCN)

作者： Barenboim, Leonid Maimon, Tzali Open Univ Israel Raanana Israel Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781450389334

We consider the distributed message-passing model and the Local Computational Algorithms (LCA) model. In both models a network is represented by an n-vertex graph G = (V, E). We focus on labeling problems, such as vertex-coloring, edge-coloring, maximal independent set (MIS) and maximal matching. In the distributed model the vertices of v perform computations in parallel, in order to compute their parts in the solution for G. In the LCA model, on the other hand, probes are performed on certain vertices in order to compute their labels in a solution to a given problem. We study the possibility of estimating a solution produced by an algorithm, much before the algorithm terminates. This estimation not only allows for size estimation of a solution, but also for an early detection of failure in randomized algorithms, so that a correcting procedure can be executed. To this end, we propose a sampling technique, in which the labels in the sampling are distributed proportionally to the distribution in the algorithm's output. However, the sampling running time is significantly smaller than that of the algorithm in hand. We achieve the following results, in terms of the maximum degree Delta and the arboricity a of the input graph. The running time of our procedures is O(log a + log log n), for sampling vertex-coloring, edge-coloring, maximal matching and MIS. This significantly improves upon previous sampling techniques, which incur additional dependency on the maximum degree Delta that can be much higher than the arboricity, as well as more significant dependency on n. Our techniques for sampling in the distributed model provide a powerful and general tool for estimation in the LCA model. In this setting the goal is estimating the size of a solution to a given problem, by making as few vertex probes as possible. For the above-mentioned problems, we achieve estimations with probe complexity d(O(log a+log log n)), where d = min(Delta, a . poly(log(n)).

关键词： distributed Algorithms Randomized Scheme Graph Coloring Maximal Independent Set LCA

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Super-Cloudlet: Rethinking Edge computing in the Era of Open Optical Networks 30

Super-Cloudlet: Rethinking Edge Computing in the Era of Open...

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30th international conference on Computer Communications and Networks (ICCCN)

作者： Mirkhanzadeh, Behzad Zhang, Tianliang Razo-Razo, Miguel Tacca, Marco Fumagalli, Andrea UT Dallas Open Networking Adv Res OpNeAR Lab Richardson TX 75080 USA

ISBN: (纸本)9780738113302

Edge computing is an attractive architecture to efficiently provide compute resources to many applications that demand specific QoS requirements. The edge compute resources are in close geographical proximity to where the applications' data originate from and/or are being supplied to, thus avoiding unnecessary back and forth data transmission with a data center far away. This paper describes a federated edge computing system in which compute resources at multiple edge sites are dynamically aggregated together to form distributed super-cloudlets and best respond to varying application-driven loads. In its simplest form a super-cloudlet consists of compute resources available at two edge computing sites or cloudlets that are (temporarily) interconnected by dedicated optical circuits deployed to enable low-latency and high-rate data exchanges. A super-cloudlet architecture is experimentally demonstrated over the largest public OpenROADM optical network testbed up to date consisting of commercial equipment from six suppliers. The software defined networking (SDN) PROnet Orchestrator is upgraded to both concurrently manage the resources offered by the optical network equipment, compute nodes, and associated Ethernet switches and achieve three key functionalities of the proposed super-cloudlet architecture, i.e., service placement, auto-scaling, and offloading.

关键词： Integrated optics Optical interconnections Optical switches Integrated circuit interconnections Computer architecture Quality of service IEEE 802.3 Standard

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Maxwell’s Demon in Tail-tolerant, Resource-efficient Serverless computing

Maxwell’s Demon in Tail-tolerant, Resource-efficient Server...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Huanyu Zhang Wenhao Huang Laiping Zhao Keqiu Li College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking(TANKLAB) Tianjin China

computing systems always face a “resource allocation dilemma” that shows the great difficulties in trading off resource efficiency for tail latency, due to the internal uncertainty of cluster status and execution behavior. Inspired by the imaginary “Maxwell’s demon” in thermodynamics who can reduce the uncertainty through a per-gas molecule-level control policy, we consider the “one-to-one mapping” feature of serverless computing and build a novel resource allocator, named Maxwell, that can achieve low tail latency and high resource efficiency in serverless simultaneously. Like the “Maxwell’s demon Maxwell is able to optimize the resource allocation for every request. It observes the state of each request and makes decisions about the minimum resource allocation through a reinforcement learning predictor. As the per-request-grained control incurs significant overhead, we further design a pipeline for avoiding the accumulated effect on a workflow. Experimental results show that Maxwell not only saves up to 31% CPU resources but also reduces the standard deviation of latency by 1.9×. Its time overhead is negligible and the resource overhead is also limited when the query per second $\leq$500.

关键词： Thermodynamics Uncertainty Buildings Serverless computing Tail Reinforcement learning Behavioral sciences

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Optimizing microservices with hyperparameter optimization 17

Optimizing microservices with hyperparameter optimization

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17th IEEE international conference on Mobility, Sensing and networking (MSN)

作者： Hai Dinh-Tuan Katsarou, Katerina Herbke, Patrick Tech Univ Berlin Serv Ctr Networking Berlin Germany

ISBN: (纸本)9781665406680

In the last few years, the cloudification of applications requires new concepts and techniques to fully reap the benefits of the new computing paradigm. Among them, the microservices architectural style, which is inspired by service-oriented architectures, has gained attention from both industry and academia. However, decomposing a monolith into multiple microservices also creates several challenges across the application's lifecycle. In this work, we focus on the operation aspect of microservices, and present our novel proposal to enable self-optimizing microservices systems based on grid search and random search techniques. The initial results show our approach is able to optimize the latency performance of microservices to up to 10.56%.

关键词： microservices optimization hyperparameter optimization grid search random search

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16th EAI international conference on Communications and networking in China, ChinaCom 2021

16th EAI International Conference on Communications and Netw...

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16th EAI international conference on Communications and networking in China, ChinaCom 2021

ISBN: (纸本)9783030991999

The proceedings contain 41 papers. The special focus in this conference is on Communications and networking in China. The topics include: A Complex Neural Network Adaptive Beamforming for Multi-channel Speech Enhancement in Time Domain;Robust Transmission Design for IRS-Aided MISO Network with Reflection Coefficient Mismatch;distributed Deep Reinforcement Learning Based Mode Selection and Resource Allocation for VR Transmission in Edge Networks;joint Optimization of D2D-Enabled Heterogeneous Network Based on Delay and Reliability Constraints;Channel Allocation for Medical Extra-WBAN Communications in Hybrid LiFi-WiFi Networks;cache Allocation Scheme in Information-Centric Satellite-Terrestrial Integrated Networks;resource Allocation in Massive Non-Orthogonal Multiple Access System;Distortionless MVDR Beamformer for Conformal Array GNSS Receiver;HEVC Rate Control Optimization Algorithm Based on Video Characteristics;millimeter Wave Hybrid Precoding Based on Deep Learning;Performance Analysis of Radar Communication Shared Signal Based on OFDM;Joint Power Allocation and Passive Beamforming Design for IRS-Assisted Cell-free Networks;text Error Correction Method in the Construction Industry Based on Transfer Learning;a Beam Tracking Scheme Based on Deep Reinforcement Learning for Multiple Vehicles;a Dynamic Transmission Design via Deep Multi-task Learning for Supporting Multiple Applications in Vehicular Networks;sub-carrier Spacing Detection Algorithm in 5G New Radio Systems;pre-handover Mechanism in the Internet of Vehicles Based on Named Data networking;selective Modulation and Cooperative Jamming for Secure Communication in Untrusted Relay Systems;Deep CSI Feedback for FDD MIMO Systems;joint Computation Offloading and Wireless Resource Allocation in Vehicular Edge computing Networks;open-Set Recognition Algorithm of Signal Modulation Based on Siamese Neural Network.

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