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Tail-Latency Aware and Resource-Efficient Bin Pack Autoscaling for distributed Event Queues 14

Tail-Latency Aware and Resource-Efficient Bin Pack Autoscali...

引用

14th international conference on Cloud Computing and Services Science, CLOSER 2024

作者： Ezzeddine, Mazen Baude, Françoise Huet, Fabrice Université Côte d’Azur CNRS I3S Nice France HighTech Payment Systems HPS Aix en Provence France

ISBN: (纸本)9789897587016

distributed event queues are currently the backbone for many large-scale real-time cloud applications including smart grids, intelligent transportation, and health care monitoring. Applications (event consumers) that process events from distributed event queue are latency-sensitive. They require that a high percentile of events be served in less than a desired latency. Meeting such desired latency must be accomplished at low cost in terms of resources used. In this research, we first express the problem of targeting resource-efficient and latency-aware event consuming from distributed event queues as a bin pack problem. This bin pack depends on the arrival rate of events, the number of events in the queue backlog, and the maximum consumption rate of event consumers. We show that the proposed bin pack solution outperforms a linear autoscaling solution by 3.5% up to 10% in terms of latency SLA. Furthermore, we discuss how dynamic event consumers provisioning in distributed event queues necessitates a blocking synchronization protocol. We show that this blocking synchronization protocol is at conflict with meeting a desired latency for high percentile of events. Hence, we propose an extension to the bin pack autoscaler logic in order to reduce the tail latency caused by the events accumulated during the blocking synchronisation protocol. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Queueing theory

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Chameleon: An Adaptive System for Overlapping Keystroke Signal Separation and Identification 30

Chameleon: An Adaptive System for Overlapping Keystroke Sign...

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30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Zhao, Jiayi Huang, Yongzhi Xie, Qipeng Wang, Weizheng Wang, Lu Wu, Kaishun Thrust Information Hub Guangzhou China / Guangzhou China Hong Kong University of Science and Technology Hong Kong City University of Hong Kong Computer Science Engineer Hong Kong Shenzhen University China

ISBN: (纸本)9798331515966

Keystroke dynamics has proven to be highly effective, with its applications expanding significantly over the years in areas such as preventing transaction fraud, account takeovers, and identity theft. Key-positioning and feature-learning methods are commonly used to identify keystroke signals. However, the existing methods face challenges in detecting overlapping keystrokes and environmentally changed signals. We propose a solution called Chameleon to address these limitations. Unlike previous signal separation and deep learning methods that are ineffective in keystroke signals and computationally demanding, Chameleon employs a low-computation Ranking Model to separate overlapping keystroke signals. Moreover, our experiments demonstrate that Chameleon separated signals can be recognized with an average accuracy of 92.69%, surpassing the commonly used FastICA method, which only reaches 25% accuracy. To account for environmental changes, we utilize the Fréchet Inception Distance (FID) as a guiding metric for model migration. Additionally, we introduce the Inductive Vector, which enables our key-identifying model to adapt to altered environmental conditions such as environment, phone location, and user variety. The Inductive Vector adjusts the model parameters based on the shift in FID. In scenarios with various phone locations, the Inductive Vector significantly improves recognition accuracy from 61% to 98%, outperforming the best existing keystroke recognition algorithm. In other dynamic environmental conditions, our approach achieves an average accuracy rate of 81.7%, which is at least 1.6 times better than the current state-of-the-art keystroke recognition algorithm. © 2024 IEEE.

关键词： Deep learning

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Revisiting the Distortion of distributed Voting 22

Revisiting the Distortion of Distributed Voting

引用

22nd international conference on Autonomous Agents and Multiagent systems, AAMAS 2023

作者： Filos-Ratsikas, Aris Voudouris, Alexandros A. School of Informatics University of Edinburgh Edinburgh United Kingdom School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

We consider a setting with agents that have preferences over alternatives and are partitioned into disjoint districts. The goal is to choose one alternative as the winner using a mechanism which first decides a representative alternative for each district based on a local election with the agents therein as participants, and then chooses one of the district representatives as the winner. Previous work showed bounds on the distortion of a specific class of deterministic plurality-based mechanisms depending on the available information about the preferences of the agents in the districts. In this paper, we first consider the whole class of deterministic mechanisms and show asymptotically tight bounds on their distortion. We then initiate the study of the distortion of randomized mechanisms in distributed voting and show bounds based on several informational assumptions, which in many cases turn out to be tight. Finally, we also experimentally compare the distortion of many different mechanisms of interest using synthetic and real-world data. © 2023 international Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Multi agent systems

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SOC Estimation of Supercapacitor Balancing systems: A Switching Kalman Filter Approach 21

SOC Estimation of Supercapacitor Balancing Systems: A Switch...

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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： Li, Heng He, Peinan Chang, Taozhen Li, Shuo Central South University School of Electronic Information Changsha China Central South University School of Automation Changsha China Changsha University of Science and Technology School of Electrical & Information Engineering Changsha China

ISBN: (纸本)9798350329223

State-of-charge (SOC) estimation of supercapacitors has been extensively studied in the literature, while most of existing studies are focused on the SOC estimation of a stand-alone supercapacitor cell. In practical applications, however, supercapacitors are connected with balancing circuits to eliminate the imbalance among cells. When the balancing circuit is activated, the system dynamics of the cell switches to a new mode, implying that existing SOC estimation methods cannot be applied directly. In this paper, we propose a switching Kalman filter method for the SOC estimation of supercapacitors balancing systems. The linear switched systems model is established by combining the equivalent circuit model of supercapacitors and the switched resistor balancing circuit. The observability of the switched system is analyzed and its discrete-time form is given. A switching Kalman filter is designed to estimate the SOC of the switched system where the filter gain can be dynamically switched according system mode. A laboratory experimental platform has been built for the supercapacitor balancing system. Extensive experiment results verify that, the proposed method can significantly improve the SOC estimation accuracy when supercapacitors are under the cell balancing mode. © 2023 IEEE.

关键词： Kalman filters

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APRG:A Fair information Granule Model Based on Adaptive Probability Replacement Resampling 29

APRG:A Fair Information Granule Model Based on Adaptive Prob...

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

作者： Cai, Jianghe Deng, Yuhui Huang, Jiande Wang, Ke Jinan University Department of Computer Science Guangzhou China

ISBN: (纸本)9798350330717

information granule is a classic mathematical paradigm in the field of data mining. Existing research focuses on improving granule quality to optimize models. However, in these studies, they do not consider that the fairness of the granular model will affect the performance of the granule, especially in some severe social issues (Law, Finance, Education, and more), often due to the participation of sensitive features, the application results of the granule (such as classification) cause population bias. Thus, we construct an adaptive probability replacement resampling model (APR) in Pre-Processors to reduce the bias of sensitive features on granular results. Then, in In-Processors, we add fairness optimizations (FO) to improve the fairness of information granules. Finally, we propose a fair information granule model based on adaptive probability replacement resampling called APRG(APR+FO). We select three loan datasets to verify the feasibility of the granular model according to the current sensitive lending issues in the financial field. The experimental results show that compared with the existing granular models, our proposed method dramatically improves the fairness of the constructed granular models, especially the APRG model, which has an average increase of 46.6% on Demographic Parity (DP) and 77.9% on Equalized Odds (EO). Compared with other existing granular models, the granule quality of APRG is increased by 38.88% on average;in terms of classification accuracy, the average decrease is only 2.03%. © 2023 IEEE.

关键词： Algorithm fairness data modeling granular computing information granule probability resampling

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A Cooperative Co-evolutionary Salp Swarm Feature Selection Algorithm with Reverse Escape Strategy for Classification 29

A Cooperative Co-evolutionary Salp Swarm Feature Selection A...

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

作者： Yu, Zikang Dong, Hongbin Sun, Jing Zhao, Bingxu Harbin Engineering University Department of Computer Science and Technology Heilongjiang China Shijiazhuang Tiedao University Information Science Technology Institute Hebei China

ISBN: (纸本)9798350330717

As one of the swarm intelligence algorithms, Salp Swarm Algorithm (SSA) is widely used in feature selection problems due to its fast convergence speed and simple structure. However, SSA still has the disadvantages of poor population diversity and being prone to fall into local optimum. To solve these problems, this study proposes a cooperative co-evolutionary salp swarm feature selection algorithm (CESSA) for classification. firstly, different populations are divided according to the characteristics of the salps, which solves the drawback of a single chain in the SSA algorithm. Multiple populations are generated from mutual information (MI)-based, random, chaotic map-based initialization corresponding to different search tasks. Learning the habitats of the solitary salps and modeling them ensures the exploration ability of the algorithm. Finally, a cooperative strategy is presented to integrate the advantages of different populations to achieve cooperative co-evolution. CESSA is evaluated on fourteen datasets and compared with six feature selection methods. The results show that CESSA can select smaller feature subsets with higher classification accuracy in most cases. © 2023 IEEE.

关键词： Classification Co-evolution Feature selection Salp swarm algorithm Swarm intelligence

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distributed Smart Multihome Energy Management Based on Federated Deep Reinforcement Learning 29

Distributed Smart Multihome Energy Management Based on Feder...

引用

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

作者： Peng, Liwei He, Fan Hasegawa, Go Cheng, Yanfen Shao, Xun School of Computer Science and Artificial Intelligence Wuhan University of Technology China Research Institute of Electrical Communication Tohoku University Japan Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan

ISBN: (纸本)9798350330717

In recent years, there's been a surge in the popularity and affordability of distributed power generation equipment, such as photovoltaic systems (PV) and energy storage systems. At present, however, most solutions target individual user's energy management. Given the varied energy consumption habits, networking neighboring users and managing energy as a unified system could boost efficiency. Yet, this comes with challenges: unpredictable dynamic demands, significant computational loads, and concerns over data privacy. To tackle these challenges, we introduce a management system that merges deep reinforcement learning (DRL) with federated learning (FL) techniques, named PDDPG-FL. In this setup, each home possesses an agent responsible for decisions like charging/discharging and trading energy with other users. For every agent, we employ a priority-aware deep deterministic policy gradient (PDDPG) algorithm. This not only addresses fluctuating demand adeptly but also offers computational advantages over the conventional DDPG algorithm. Moreover, by incorporating the FL framework, agents can collaborate without risking data privacy breaches. Simulation results show that PDDPG-FL can reduce dependency on main supply grids by up to 9.7% and offers a more streamlined computational process. © 2023 IEEE.

关键词： deep reinforcement learning energy trade Federated learning home energy management system (HEMS)

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Function Clustering to Optimize Resource Utilization on Container Platform 29

Function Clustering to Optimize Resource Utilization on Cont...

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

作者： Lee, Chao-Yu Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan Academia Sinica Institute of Information Science Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan National Taiwan University The Graduate Institute of Networking and Multimedia Taipei Taiwan

ISBN: (纸本)9798350330717

In recent years, container technology has gained significant attention in the software industry, with many businesses opting for its elasticity, cost-effectiveness, and ease of implementation. The most common way to ensure a seamless user experience is to keep a substantial number of containers active throughout the day, which causes resource over-provision. Conversely, closing the container right after handling the requests can reduce memory consumption but generate a cold start whenever the request arrives. Cold start occurrence and resource usage is a trade-off and presents a significant challenge on the container *** address this challenge, we observe that serving consecutive requests with the same container can notably decrease the number of cold starts. We propose TAC, a Temporal Adjacency Function Clustering algorithm, to meet the challenge. TAC selects the functions with time adjacency requests into a cluster from the historical data. TAC packs functions serving time adjacency requests into a cluster to reduce cold starts and enable efficient resource utilization. The experiment result shows that TAC reduces 8% cold start occurrences and 53% memory usage with the real-world traces compared to the state-of-the-art methods, e.g., Defuse [1] and Hybrid histogram policy [2]. © 2023 IEEE.

关键词： cold-start container platform Function-as-a-Service serverless

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Reference remote sensing image scene segmentation based on multiscale cross-axis attention network

Reference remote sensing image scene segmentation based on m...

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2024 international conference on Remote Sensing, Mapping, and Geographic information systems, RSMG 2024

作者： Su, Jiashun Chen, Yantong Department of Information Science and Technology Dalian Maritime University Dalian116026 China

ISBN: (纸本)9781510685826

Segmentation of scenes in remote sensing images represents a crucial and demanding area in the processing of remote sensing image data, merging the fields of computer vision and natural language processing. Traditional image segmentation methods often struggle with the complex spatial scales of remote sensing images and the issue of fuzzy boundaries in segmentation targets, resulting in suboptimal outcomes. To address these challenges, we introduce the Multimodal information Interaction Network (IMNet), a novel method specifically designed to meet the unique needs of scene segmentation in remote sensing imagery. first, we design an Intra-Modal information Mining Module (IMM) to effectively handle fine-grained details at multiple scales. Additionally, to better capture global information, we introduce a mechanism to compute double cross-attention between two parallel axial attentions. Our experimental results indicate that our method attains a mean Intersection over Union (mIOU) of 80.46% on our dataset, outperforming current state-of-the-art models and establishing a new standard in the performance of remote sensing image scene segmentation. © 2024 SPIE.

关键词： Data mining

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BILAM: A BiLSTM-Based Model for Detecting Phishing Scams in Ethereum 21

BILAM: A BiLSTM-Based Model for Detecting Phishing Scams in ...

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作者： Ye, Mingshun Tang, Mingdong Chen, Weili Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou China

ISBN: (纸本)9798350329223

With the rapid advancement of blockchain technology, cryptocurrencies based on blockchain have become a hot topic. However, various issues accompany this development, with phishing scams emerging as a severe financial crime within the blockchain ecosystem, causing significant economic losses to both blockchain platforms and users. In order to address this threat, this essay proposes a phishing scam account identification model based on Bidirectional Long Short-Term Memory Networks (BiLSTM) named BILAM. The model has been validated on the Ethereum platform and has been proven to be *** study proposes a novel approach by using transaction records for the first time to construct a time series, and it leverages the BILAM model to learn latent information. Experimental results demonstrate the effectiveness of this method in constructing transaction time series. Moreover, the BILAM model shows excellent performance, with its predictive accuracy significantly surpassing other models, particularly achieving an AUC index of 92.8%. © 2023 IEEE.

关键词： Blockchain

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