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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

引用

21st IEEE international Symposium on Parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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Securing distributed computing in the Metaverse: A Balanced TGDH Encryption Scheme

Securing Distributed Computing in the Metaverse: A Balanced ...

引用

2023 IEEE international conference on Metaverse computing, networking and Applications, MetaCom 2023

作者： Bose, Anway Nori, John L. Bai, Li Temple University Philadelphia Department of Electrical Engineering PhiladelphiaPA United States

ISBN: (纸本)9798350333336

In this paper, we present a secure communication protocol based on a balanced Tree-based Group Diffie-Hellman (TGDH) key management solution for secure distributed computing in metaverse. The novelty of this work is that our modified TGDH algorithm balances the tree while preserving the distributed, secure architecture of group re-keying. The balanced tree ensures optimized computation regardless of join or leave events. This paper also presents a portable Python application programming interface (API) for the implementation of this secured group key management algorithm. This API addresses a lack of existing open-source implementations of TGDH, specifically in distributed-system architecture. From cloud based data sharing to distributed multi-agent frameworks and any secured computation application in metaverse, our algorithm can be easily and efficiently ported to any OS and used to secure group communications. © 2023 IEEE.

关键词： Multi agent systems

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Proceedings of INDIS 2021: 8th Workshop on Innovating the Network for Data-Intensive Science, Held in conjunction with SC 2021: The international conference for High Performance computing, networking, Storage and Analysis

Proceedings of INDIS 2021: 8th Workshop on Innovating the Ne...

引用

8th IEEE Workshop on Innovating the Network for Data-Intensive Science, INDIS 2021

ISBN: (纸本)9781665411141

The proceedings contain 6 papers. The topics discussed include: the service analysis and network diagnosis data pipeline;NetGraf: an end-to-end learning network monitoring service;exploring the BBRv2 congestion control algorithm for use on data transfer nodes;learning transfers via transfer learning;deploying per-packet telemetry in a long-haul network: the AmLight use case;and bridging network and parallel I/O research for improving data-intensive distributed applications.

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Resource Awareness Mechanism based on Multi-dimensional Identifier in Compute First networking 9

Resource Awareness Mechanism based on Multi-dimensional Iden...

引用

9th international conference on Computer and Communication Systems (ICCCS)

作者： Zhang, Jianchao Gao, Shuai Hou, Xindi Yao, Fangtao Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350350227;9798350350210

CFN(Compute First networking) is an intelligent new network with computation at its core and networking as its foundation, integrating Computational, storage, and transmission resources. Computational resources and network resources in the CFN are ubiquitous and heterogeneous. In the traditional Internet identifier system, IP addresses have limited extension, poor semantic scalability, making it challenging to incorporate information about computational and network resources into the network and hindering unified description. This paper introduces a multi-dimensional identifier system-based mechanism for computational and network resource awareness. It perceives computational network resources and employs service identifier for unified mapping. Additionally, it designs workflows for computational and network resource awareness, accomplishing tasks such as computational resource notification, request, query, update, as well as network resource detection, collection, and feedback. This facilitates functionality for service localization and computational network resource awareness. Finally, a prototype system was constructed based on the programmable data plane to experimentally evaluate the functionality and performance of the resource awareness mechanism. The results indicate that in a CFN scenario, this mechanism can support real-time perception and updates of computational network resources. In the context of distributed computing services, compared to traditional network deployments, the mechanism reduces latency by approximately 10%.

关键词： Multi-dimensional identifier CFN programmable data plane IFIT(In-situ Flow Information Telemetry)

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EdgeCloudAI: Edge-Cloud distributed Video Analytics 30

EdgeCloudAI: Edge-Cloud Distributed Video Analytics

引用

30th international conference on Mobile computing and networking, ACM MobiCom 2024

作者： Ghasemi, Mahshid Kostic, Zoran Ghaderi, Javad Zussman, Gil Columbia University New York CityNY United States

ISBN: (纸本)9798400704895

Recent advances in Visual Language Models (VLMs) have significantly enhanced video analytics. VLMs capture complex visual and textual connections. While Convolutional Neural Networks (CNNs) excel in spatial pattern recognition, VLMs provide a global context, making them ideal for tasks like complex incidents and anomaly detection. However, VLMs are much more computationally intensive, posing challenges for large-scale and real-time applications. This paper introduces EdgeCloudAI, a scalable system integrating VLMs and CNNs through edge-cloud computing. Edge-CloudAI performs initial video processing (e.g., CNN) on edge devices and offloads deeper analysis (e.g., VLM) to the cloud, optimizing resource use and reducing latency. We have deployed EdgeCloudAI on the NSF COSMOS testbed in NYC. In this demo, we will demonstrate EdgeCloudAI's performance in detecting user-defined incidents in real-time. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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Workload Prediction for Edge computing 24

Workload Prediction for Edge Computing

引用

25th international conference on distributed computing and networking, ICDCN 2024

作者： Shah, Kavish Gandhi, Ayush Gandhi, Kalgi Bhise, Minal Distributed Database Group Gujarat Gandhinagar India

ISBN: (纸本)9798400716737

In large-scale edge systems, along with the number of edge devices, the amount of data that needs to be processed has been increasing. Edge systems have limited processing power, memory, and network bandwidth. For such systems, handling resource utilization and data management during query processing is tricky. Moreover, joins being the costliest operations, when accelerated, can improve query processing substantially. The state-of-the-art technique for accelerating hash join, Column Imprint-Hash Join, is a data-aware approach which uses equi-height binning. Proposed Predicted Workload-Aware Column Imprint-Hash Join enhances the query performance and addresses real-time query processing by predicting the upcoming workload in advance. The algorithm is demonstrated on the TPC-H benchmark dataset. It shows an improvement of about 10% for the Percentage of Cachelines Read over CI-HJ, making the algorithm energy-efficient. An overall gain in Query Execution Time of 11% is observed. Although, Algorithm Execution Time shows an overhead of 6% as compared to the state-of-the-art which is due to an additional prediction phase. The proposed technique showcases the effectiveness of query prediction in optimizing edge system performance. © 2024 ACM.

关键词： Forecasting

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Smart-Hop: Low-Latency Multi-hop networking for LoRa 18

Smart-Hop: Low-Latency Multi-hop Networking for LoRa

引用

18th Annual international conference on distributed computing in Sensor Systems (DCOSS)

作者： Ahmar, Absar-Ul-Haque Joosen, Wouter Hughes, Danny Katholieke Univ Leuven Comp Sci Imec DistriNet Leuven Belgium

ISBN: (纸本)9781665495127

Low Power Wide Area Networks (LPWANs) provide the Internet of Things (IoT) with energy-efficient, low cost, and long-range networking. LoRaWAN is one of the most widely deployed and studied LPWAN technologies due to its license-free operation and open standard, which has enabled 3rd parties to deploy millions of networks worldwide. LoRa allows transmission range and speed to be traded off by configuring the Spreading Factor (SF) setting. By tuning this parameter, the performance envelope of the network can be configured to favor a higher speed or longer range. While the SF of LoRA provides a simple and elegant means to tailor performance, at the highest spreading factor, maximum throughput drops to a few bits per second, dramatically increasing data extraction times. In this paper, we address this problem by proposing Smart-Hop, a novel multi-hop MAC layer protocol for LoRa which implements low-overhead routing. In contrast to prior work, Smart-Hop exploits the variable range afforded by different SFs to eliminate much of the complexity and overhead of traditional multi-hop mesh networking. Smart-Hop reduces the number of end-devices operating at high SFs while implementing low overhead routing in order to reduce data extraction times. These improvements are achieved while preserving the maximum range offered by LoRa in its highest SF configuration.

关键词： Low Power Wide Area Networks (LPWAN) LoRa Spreading Factor (SF) allocation Wireless Sensor Networks (WSN) mesh networking.

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Analysis of DDoS Attacks in Software Defined networking using Machine Learning

Analysis of DDoS Attacks in Software Defined Networking usin...

引用

2023 IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Sharma, Anshika Chauhan, Harshvardhan Singh Kaur, Harleen Babbar, Himanshi Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9781665462631

The most practical configurable network is Software Defined networking (SDN). SDN delineates the network's control layer from the data layer, while operationally integrating them. With global visibility into network status, the's logical centralized control improves network management. However, centralized management introduces new security risks. One of the most attractive victims of distributed denial of service (DDoS) attacks is the SDN control platform. Due to machine learning (ML) performance and unavoidable advantages against fingerprint vulnerabilities, this article proposes and considers a ML solution for protecting against DDoS attacks in SDN. ML techniques are evaluated in both, a theoretical format and a practical setup such that SDN controllers are disclosed to DDoS-attacks in order to reach significant deductions about ML-based cybersecurity of future networking protocols. © 2023 IEEE.

关键词： Denial-of-service attack

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distributed Learning for Application Placement at the Edge Minimizing Active Nodes 2

Distributed Learning for Application Placement at the Edge M...

引用

2nd international conference on 6G networking, 6GNet 2023

作者： Torres-Pérez, Claudia Coronado, Estefanía Cervelló-Pastor, Cristina Camargo, Juan Sebastian Siddiqui, Muhammad Shuaib i2CAT Foundation Barcelona Spain Universidad de Castilla-La Mancha Albacete Spain Universitat Politécnica de Catalunya Barcelona Castelldefels Spain

ISBN: (纸本)9798350306736

The main goal of application placement in Multi-Access Edge computing (MEC) is to map their requirements to the infrastructure for desired Service Level Agreement (SLA). In highly distributed infrastructures in beyond 5G and 6G networks, meeting this need and minimizing energy use are crucial. Fo-cusing solely on meeting SLA requirements can lead to resource fragmentation and reduced energy efficiency, as nodes utilize only a small portion of their resources. Furthermore, when multiple orchestrators govern MEC nodes, achieving optimal efficiency becomes a more complex challenge. This paper addresses the application placement problem by employing distributed deep reinforcement learning to efficiently minimize the overall cost of active MEC nodes in a distributed scenario involving multiple MEC systems. Our technique reduces the number of active nodes maintaining an average accuracy of up to 98%, meets SLA requirements, and is scalable for hosting several MEC nodes. © 2023 IEEE.

关键词： Edge computing

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Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU Parallel

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale usin...

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2024 Workshops of the international conference for High Performance computing, networking, Storage and Analysis, SC Workshops 2024

作者： Maheshwari, Ketan Arndt, William Karimi, Ahmad Maroof Yin, Junqi Suter, Frederic Johnson, Seth Da Silva, Rafael Ferreira Oak Ridge National Laboratory Oak RidgeTN United States Lawrence Berkeley National Laboratory BerkeleyCA United States

ISBN: (纸本)9798350355543

GNU Parallel is a versatile and powerful tool for process parallelization widely used in scientific computing. This paper demonstrates its effective application in high-performance computing (HPC) environments, particularly focusing on its scalability and efficiency in executing large-scale high-throughput high-performance computing (HT-HPC) workflows. Through real-world examples, we highlight GNU Parallel's performance across various HPC workloads, including GPU computing, container-based workloads, and node-local NVMe storage. Our results on two leading supercomputers, OLCF's Frontier and NERSC's Perlmutter, showcase GNU Parallel's rapid process dispatching ability and its capacity to maintain low overhead even at extreme scales. We explore GNU Parallel's application in massive parallel file transfers using a scheduled Data Transfer Node (DTN) cluster, emphasizing its broad utility in diverse scientific workflows. Beyond its direct application as a viable workflow manager, GNU Parallel can be employed in conjunction with other workflow systems as a "last-mile"parallelizing driver and as a quick prototyping tool to design and extract parallel profiles from application executions. We then argue that the potential for GNU Parallel to transform workflow management at extreme scales is substantial, paving the way for more efficient and effective scientific discoveries. © 2024 IEEE.

关键词： distributed computing GNU Parallel High-Throughput computing Scientific Workflows

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