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Graph-based Multi-view Clustering for Web services 21

Graph-based Multi-view Clustering for Web services

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： Yang, Wang Zhenzhen, Yuan Guosheng, Kang Buqing, Cao Jianxun, Liu Yong, Xiao Hnust Xiangtan China Hunan University of Science and Technology Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering China

ISBN: (纸本)9798350329223

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process. © 2023 IEEE.

关键词： Web services

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Multi-Agent Reinforcement Learning Reward engineering via Stochastic Game Evaluation 24

Multi-Agent Reinforcement Learning Reward Engineering via St...

引用

24th IEEE/ACIS international Winter Conference on software engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD 2022

作者： Kattepur, Ajay Artificial Intelligence Group Ericsson Research Bangalore India

ISBN: (纸本)9798350310412

With the proliferation of Reinforcement Learning (RL) algorithms across multiple applications, the design of appropriate reward mechanisms that elicit desired behaviours becomes crucial. Reward setting is made more difficult in the multi-agent case where cooperative, competitive or mixed interactions between agents may lead to differing outcomes. In this paper, we formulate the reward engineering of multi-agent reinforcement learning approaches via game theoretic models. This approach is used to analyze the overall team reward when choosing one game theoretic structure over another. An empirical analysis is provided over game theoretic simulators that demonstrate co-operative game rewards improve the rewards by upto 30%. This formulation may be applied to a variety of use cases within logistics, transport and telecommunications domains that employ multi-agent techniques. © 2022 IEEE.

关键词： Stochastic systems

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DW-LRC: A Dynamic Wide-stripe LRC Codes for Blockchain Data Under Malicious Node Scenarios 29

DW-LRC: A Dynamic Wide-stripe LRC Codes for Blockchain Data ...

引用

29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Zhang, Mizhipeng Wu, Chentao Li, Jie Guo, Minyi Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350330717

Blockchain is a decentralized digital ledger system that can be used in many fields. However, the traditional approach of storing blockchain data (full repliction) incurs expensive storage costs. As a result, researchers have proposed erasure coding methods to reduce storage costs. Existing erasure coding methods are all based on RS codes. Since blockchain systems typically have a large number of nodes, it is necessary to use wide-stripe RS codes to store blocks, which results in significant overhead during the recovery processes. Because widestripe RS codes require accessing a large number of nodes for decoding, this means significant network and I/O *** solve the above problem, we propose DW-LRC, which is a dynamic wide-stripe Local Reconstruction Code (LRC) based methods in permissioned blockchain systems. DW-LRC predicts malicious nodes using node reputation, and then selects an appropriate variant of LRC code to lower storage and recovery costs compared to traditional erasure coding. To demonstrate the effectiveness of DW-LRC, we conduct several experiments on the open source blockchain software tendermint. The results show that, compared to the state-of-the-art erasure coding methods, DW-LRC reduced the average recovery latency by 36.4% and improved the block access throughput by 32.3%. © 2023 IEEE.

关键词： Blockchain Consensus Erasure Coding Replication Storage

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ThreadFuser: A SIMT Analysis Framework for MIMD Programs 57

ThreadFuser: A SIMT Analysis Framework for MIMD Programs

引用

57th Annual IEEE/ACM international symposium on Microarchitecture, MICRO 2024

作者： Alawneh, Ahmad Kang, Ni Khairy, Mahmoud Rogers, Timothy G. Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9798350350579

The broad usage of accelerators, such as GPUs, faces two important challenges. Developing code for a new accelerator is expensive and unpredictable. Porting large parallel programs from Multiple Instruction Multiple Data (MIMD) CPUs to Single Instruction Multiple Thread (SIMT) GPUs involves significant effort that may or may not result in improved performance versus the CPU. This high activation energy to create new workloads introduces the second challenge: Architects and systems researchers lack a diverse SIMT codebase to study new designs. To tackle these challenges, we introduce ThreadFuser, an analysis framework that efficiently and accurately predicts the performance of any pre-written MIMD program on SIMT hardware. ThreadFuser conducts thorough control and data flow analysis on dynamic CPU program traces, determining the impact of lock-step execution on CPU binaries. Thread-Fuser efficiently delivers accurate reports on a MIMD program's divergence and synchronization characteristics. Moreover, ThreadFuser seamlessly integrates with state-of-The-Art GPU simulators to conduct detailed analyses and produce fine-grained performance measurements. We evaluate ThreadFuser on a diverse set of 36 CPU workloads, demonstrating the potential and challenges of executing MIMD code on a SIMT machine. We demonstrate ThreadFuser's potential to inform software development decisions and open new areas to explore in data-parallel hardware design. © 2024 IEEE.

关键词： Data flow analysis

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Transaction Data Management Optimization Based on Multi-Partitioning in Blockchain systems 21

Transaction Data Management Optimization Based on Multi-Part...

引用

作者： Zhang, Jingyu Hou, Xingxing Teng, Yilong Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods. © 2023 IEEE.

关键词： Information management

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Multi-Objective Optimization Design for DC-Link Capacitor of Motor Driver

Multi-Objective Optimization Design for DC-Link Capacitor of...

引用

2024 CPSS and IEEE international symposium on Energy Storage and Conversion, CPSS and ISESC 2024

作者： Liu, Qian Huang, Kangxing Wei, Xueliang Zheng, Yihong Xiang, Hao School of Automation Wuhan University of Technology Wuhan China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong College of Artificial Intelligence China University of Petroleum-Beijing Beijing China Wuhan IActuator Robotics Co. Ltd Software Development Department Wuhan China

ISBN: (纸本)9798350380514

The capacitor is a common weak point in motor drive system design, necessitating targeted selection, design, and optimization of capacitor banks within these systems. Traditional capacitor design often relies on engineering experience, usually meeting specific capacitance and voltage requirements through simple parallel or series connections of capacitors. This approach often fails to fully optimize volume, cost, and reliability. This paper uses comprehensive theoretical modeling to optimize capacitor module design, effectively balancing capacitor lifespan, cost, and volume. For the design and selection of DC-link capacitors in a 1kW permanent magnet synchronous motor drive system, a multi-objective optimization design method for capacitor banks is proposed. The study treats the individual capacitance values of parallel capacitors as variables, significantly enhancing the overall performance of the capacitor module through precise optimization and the introduction of necessary constraints in the selection design. The proposed method is validated with a 1kW permanent magnet synchronous motor drive system. The optimization results show that under the guidance of a multi-objective optimization framework, the designed DC-link capacitor module meets physical size requirements while achieving overall optimization in terms of volume, cost, and lifespan, significantly improving system reliability. © 2024 IEEE.

关键词： Permanent magnets

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Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection 29

Few-shot Message-Enhanced Contrastive Learning for Graph Ano...

引用

29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Xu, Fan Wang, Nan Wen, Xuezhi Gao, Meiqi Guo, Chaoqun Zhao, Xibin University of Science and Technology of China School of Computer Science and Technology Anhui China Beijing Jiaotong University School of Software Engineering Beijing China Tsinghua University School of Software Engineering Beijing China

ISBN: (纸本)9798350330717

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies. © 2023 IEEE.

关键词： Deep-GNN few-shot graph anomaly detection

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Semi-asynchronous Federated Learning Optimized for NON-IID Data Communication based on Tensor Decomposition 21

Semi-asynchronous Federated Learning Optimized for NON-IID D...

引用

作者： Wu, Qidong Fu, Xuemei Yang, Xiangli Zhao, Ruonan Wu, Chengqian Zhang, Tinghua Hainan University School of Computer Science and Technology Haikou China Hainan University School of Information and Communication Engineering Haikou China

ISBN: (纸本)9798350329223

Federated Learning (FL) is a new distributed machine learning framework that enables reliable collaborative training without collecting users' private data, it can successfully address the issue of data silos. However, FL has had trouble scaling to statistically varied data and large-scale models because of its frequent communication and average aggregation procedures. In this paper, we propose a Semi-asynchronous Stochastic Controlled Averaging based on Tensor Decomposition for Federated Learning (SATD-SCAFFOLD), in which we perform tensor decomposition on the client model to reduce communication costs. In addition, we design a semi-asynchronous aggregation approach for the server, which can prevent server from wasting too much time waiting for data from late clients in the case of an unstable network, and the overall model still has excellent performance. Thorough experiments demonstrate that our proposed SATDSCAFFOLD algorithm can reduce both the communication cost and convergence time while maintaining good performance. © 2023 IEEE.

关键词： Stochastic systems

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Improved memory performance through the development of an energy efficient distributed memory Management System

Improved memory performance through the development of an en...

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2022 international Conference on Machine Learning, Big Data, Cloud and parallel Computing, COM-IT-CON 2022

作者： Sundari, K.Siva Narmadha, R. Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

In multiprocessor networks, shared distributed memory is a popular research subject. It has been studied by scientists and engineers from a variety of fields. To construct high-performance, large-scale multiprocessor systems, it is necessary to maximise distributed shared memory (SDM) performance. SDM algorithm, locking shared space, thrashing, concurrent access, page faults, extension, transparency, huge database support and cost have all been the subject of extensive investigation in the past. Memory structure has a substantial impact on the system's power consumption and performance;hence a low-power system is needed. There are a number of problems with the design of shared and private memory systems for multiple processors that this dissertation seeks to address. Using software parameters, these researchers developed a new distributed shared memory design that outperforms conventional structural design in managing a distributed shared address space. The primary goal of this study is to use a software method to create novel distributed shared memory architecture. © 2022 IEEE.

关键词： Structural design

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Large-scale k Edge Server Placement Based on User Clustering and Intelligent Search 21

Large-scale k Edge Server Placement Based on User Clustering...

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作者： Mao, Chengying Hu, Haiquan Jiangxi University of Finance and Economics School of Software and IoT Engineering Nanchang330013 China

ISBN: (纸本)9798350329223

In the field of mobile edge computing, the k Edge Server Placement (kESP) problem has attracted extensive attention. However, existing studies have mainly focused on solving small-scale problems, and the large-scale scenarios need to be explored. Based on this observation, this paper attempts to develop a cost-effective solution for the large-scale kESP problem. The proposed algorithm named UCIS-kESP realizes the selection of a small subset from a large collection of base stations by clustering mobile users, thereby significantly reducing the search cost of the kESP problem. Subsequently, an intelligent search based on genetic algorithm is applied to find the 'optimal' deployment of k edge servers from the candidate subset. The experimental analysis conducted on a public dataset demonstrates that our proposed UCIS-kESP algorithm obtains better deployment solutions than the state-of-the-art algorithms, while also exhibiting fast and stable solving efficiency. On average, our algorithm can save about 40% of the computational time. Even as the size of the kESP problem increases, the computational overhead of the UCIS-kESP algorithm grows relatively slowly. © 2023 IEEE.

关键词： Mobile edge computing

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