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检索条件"任意字段=Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems"

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An Evaluation of modeling Options for Cloud-native Application Architectures to Enable Quality Investigations

An Evaluation of Modeling Options for Cloud-native Applicati...

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IEEE international conference on Utility and Cloud Computing (UCC)

作者： Karolin Dürr Robin Lichtenthäler Distributed Systems Group University of Bamberg Germany

Cloud-native as a style to build web applications embraces the potential of cloud computing. A possibility to evaluate software architectures of cloud-native applications according to quality aspects would be beneficial to developers, but is challenging due to the thematic breadth of cloud-native ranging from design to operation of applications. This work therefore evaluates existing architecture description languages according to their suitability for representing those architectural aspects specific to the cloud-native style. The evaluation is driven by a recently proposed quality model for which a suitable architectural modeling option is needed. We find that several existing languages are suitable as a basis for implementing an extension specific to the quality model. We choose TOSCA as an established standard to implement such an extension with corresponding tooling support.

关键词： Cloud computing Software architecture Computational modeling computer architecture Distance measurement Reliability Standards

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Ensemble Deep Learning Model based on Multi-Class Classification Technique to Detect Cyber Attacks in IoT Environment

Ensemble Deep Learning Model based on Multi-Class Classifica...

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Smart Computing, IoT and Machine Learning (SIML), international conference on

作者： Ahmed Alrefaei Mohammad Ilyas Electrical Engineering and Computer Science Florida Atlantic University Boca Raton Florida United States

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The Internet of Things (IoT) has seen a remarkable expansion, encompassing devices that connect to the internet, collect data, and share information. However, this growth has introduced a host of security challenges, leading to an increase in cyber-attacks. To address these challenges and ensure data integrity, researchers have been focusing on developing Intrusion Detection systems (IDS) using deep ensemble models. This paper presents a multi-class classification algorithm approach for intrusion detection in IoT, employing a deep ensemble model incorporating Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Evaluation metrics such as F1 score, recall, and precision under the Receiver Operating Characteristics (ROC) curve are utilized. The proposed models’ proficiency is demonstrated using the UNSW 2018 IoT Botnet dataset, showing that the deep ensemble multi-class classification models achieve high accuracy in detecting IoT attacks with 98.4%.

关键词： measurement Computational modeling Intrusion detection Receivers Multilayer perceptrons Internet of Things Convolutional neural networks

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Surrogate-based Digital Twin for Predictive Fault Modelling and Testing of Cyber Physical systems

Surrogate-based Digital Twin for Predictive Fault Modelling ...

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IEEE/acm international Symposium on Big Data Computing (BDC)

作者： Hayatullahi Bolaji Adeyemo Rami Bahsoon Peter Tiño School of Computer Science University of Birmingham Birmingham UK

Cyber Physical systems (CPS) pose a pressing need to ensure they are sufficiently reliable and continue to be dependable. It is, therefore, essential to test these systems to uncover any potential anomalies, which if not detected can lead to failure and/or cause loss or injury. Adequate or complete coverage of behaviours can be difficult to accomplish in CPS. We advocate a less expensive and easy-to-evaluate representation of the system via surrogate modelling. In this paper, we present a novel predictive fault modelling framework leveraging surrogate-based Digital Twin for probing for likely faults that can support software analysts and testers of CPS in their testing plans. The approach abstracts the CPS and uses a variant of Recurrent Neural Network known as Long Short-Term Memory (LSTM) surrogate model for forecasting. The forecasting can help in predicting multiple behaviours of the system components and the likely faults of systems under test; observations will consequently feed into the testing plans. Both direct and iterative (i.e. one-time and multiple-time varying steps) forecasting are supported as part of the framework. We evaluate our surrogate-based Digital Twins predictive modelling approach on two CPSs namely: water distribution system and air pollution detection system. The results show that our approach performed decently in predicting multiple time steps.

关键词： Recurrent neural networks Computational modeling Atmospheric modeling Predictive models Water pollution Software Digital twins

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Deep Model Pruning By Parallel Pooling Attention Block

Deep Model Pruning By Parallel Pooling Attention Block

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2022 IEEE/WIC/acm international Joint conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Junnan Wu Liming Zhang Computer and Information Science Macau University Macau China

Deep neural network models have achieved great success in various fields, including computer vision and recommendation systems. However, deep models are usually large and computationally time-consuming. Reducing the size of deep models and speeding up the learning process without a sharp drop in accuracy becomes a promising goal, both in research and practice. The channel pruning is one of the most effective methods, which can not only compress deep model size, but also directly speed up inference. In this paper, we propose a novel channel attention block called parallel pooling attention block (PPAB) that is designed on top of the squeeze and excitation (SE) blocks. There are two optimization improvements of PPAB. First, parallel max-pooling branch is added on top of SE blocks. Second, we avoid dimensionality reduction during the excitation phase. Both of these optimizations improve the channel importance measurement capabilities of PPAB. Experiment results show that PPAB outperforms general SE blocks on the channel attention objective. The proposed pruning method could be applied efficiently both in computer vision and recommendation systems.

关键词： Dimensionality reduction Deep learning computer vision Computational modeling Neural networks Libraries Hardware

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Truffle: Efficient Data Passing for Data-Intensive Serverless Workflows in the Edge-Cloud Continuum

Truffle: Efficient Data Passing for Data-Intensive Serverles...

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IEEE international conference on Utility and Cloud Computing (UCC)

作者： Cynthia Marcelino Stefan Nastic Distributed Systems Group TU Wien

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Serverless computing promises a scalable, reliable, and cost-effective solution for running data-intensive applications and workflows in the heterogeneous and limited-resource environment of the Edge-Cloud Continuum. However, building and running data-intensive serverless workflows also brings new challenges that can significantly degrade the application performance. Cold start remains one of the main challenges that impact the total function execution time. Further, since the serverless functions are not directly addressable, Serverless workflows need to rely on external (storage) services to pass the input data to the downstream functions. Empirical evidence from our experiments shows that the cold start and the function data passing take up the most time in the function execution *** this paper, we introduce Truffle – a novel model and architecture that enables efficient inter-function data passing in the Edge-Cloud Continuum by introducing mechanisms that separate computation and I/O, allowing serverless functions to leverage the cold starts to their advantage. Truffle introduces Smart Data Prefetch (SDP) mechanism that abstracts the retrieval of input data for the serverless functions by triggering the data retrieval from the external storage during the function’s startup. Truffle’s Cold Start Pass (CSP) mechanism optimizes inter-function data passing and data exchange within serverless workflows in the Edge-Cloud Continuum by hooking into the functions’ scheduling lifecycle to trigger early data passing during the function’s cold start. Experimental results show that by leveraging the data prefetching and cold-start data passing, Truffle reduces the IO latency impact on the total function execution time by up to 77%, improving the function execution time by up to 46% compared to the state-of-the-art data passing approaches.

关键词： Prefetching Computational modeling Buildings Serverless computing computer architecture Data retrieval Data models Computational efficiency Reliability

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Intelligent Tutoring for Large-Scale Personalized Programming Learning Based on Knowledge Graph 53

Intelligent Tutoring for Large-Scale Personalized Programmin...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Li, Ying Qiu, Jincheng Yang, Runze Zhu, Tongyu Sheng, Hao Gui, ShiJie Liang, Yu School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (纸本)9798350336429

The development of learning analytics technology has created the necessary conditions for the implementation of large-scale personalized education. By taking advantage of the current advancements available, we can adopt a deep integration of 'artificial intelligence + education', take educational big data as the research object, aim at achieving precise teaching, and use cognitive diagnosis as a means to construct an intelligent guidance model. The model will conform to a learner's cognitive patterns and curriculum characteristics to meet his personalized learning needs under different knowledge states. Firstly, use knowledge map and abstract syntax trees to extract the characteristics of teaching objectives from questions and standard programing codes. Secondly, Use 'target characteristics - learning characteristics - learning sequence - learning effect' cognitive diagnosis model to assess student achievement in learning. Finally, according to the correlation between knowledge points, the initial learning path consisting of all relevant knowledge points is identified from a knowledge map. And the most optimal learning path is selected based on the importance of knowledge points and student's learning situation. There are three innovation points in this paper. First, the traditional method which only focuses on discovering the one-dimensional knowledge features implicated in the questions, is extended to construct a knowledge-ability multidimensional feature matrix. Secondly, it optimized static cognitive diagnosis into dynamic cognitive arbitrariness, which has a modeling capability of dynamic temporal sequence. Thirdly, it regarded the correlation between knowledge points, and the importance of knowledge points as the heuristic conditions for constructing a learning path. © 2023 IEEE.

关键词： Cognitive Diagnosis Intelligent Guidance Model Knowledge Graph Personalized Learning Path

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Factor Graph Accelerator for LiDAR-Inertial Odometry (Invited Paper)

Factor Graph Accelerator for LiDAR-Inertial Odometry (Invite...

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IEEE international conference on computer-Aided Design

作者： Yuhui Hao Bo Yu Qiang Liu Shaoshan Liu Yuhao Zhu Tianjin University PerceptIn University of Rochester

Factor graph is a graph representing the factorization of a probability distribution function, and has been utilized in many autonomous machine computing tasks, such as localization, tracking, planning and control etc. We are developing an architecture with the goal of using factor graph as a common abstraction for most, if not, all autonomous machine computing tasks. If successful, the architecture would provide a very simple interface of mapping autonomous machine functions to the underlying compute hardware. As a first step of such an attempt, this paper presents our most recent work of developing a factor graph accelerator for LiDAR-Inertial Odometry (LIO), an essential task in many autonomous machines, such as autonomous vehicles and mobile robots. By modeling LIO as a factor graph, the proposed accelerator not only supports multi-sensor fusion such as LiDAR, inertial measurement unit (IMU), GPS, etc., but solves the global optimization problem of robot navigation in batch or incremental modes. Our evaluation demonstrates that the proposed design significantly improves the real-time performance and energy efficiency of autonomous machine navigation systems. The initial success suggests the potential of generalizing the factor graph architecture as a common abstraction for autonomous machine computing, including tracking, planning, and control etc.

关键词： Location awareness Autonomous systems computer architecture Probability distribution Energy efficiency Real-time systems Planning

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Spin Chemistry Simulation via Hybrid-Quantum Machine Learning 3

Spin Chemistry Simulation via Hybrid-Quantum Machine Learnin...

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3rd IEEE international conference on Quantum Computing and Engineering (QCE)

作者： Brokowski, Trevor J. Chowdhury, Farhan T. Smith, Luke D. Alvarez, Pedro Sandeep, Samarth Aiello, Clarice Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA Univ Exeter Dept Phys Exeter Devon England Univ Exeter Living Syst Inst Exeter Devon England Univ Estadual Campinas Inst Fis Campinas Brazil If & Only If Iff Technol Davis CA USA

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

The Radical Pair Mechanism, a process by which magnetic fields modulate the kinetics of chemical reactions by influencing spin dynamics, is thought to underlie a variety of biological mechanisms and is inherently both quantum and noisy. Model simulations with limited electron-nuclear interactions have been conducted on classical devices resulting in experimentally verified final state results, however these simulations become too complex to simulate as the systems scale in size. This motivates us to propound the simulation of the relevant spin dynamics in a noisy quantum device to probe the currently unexplored open quantum systems aspects of the radical pair mechanism and to provide a novel theoretical tool for us to be able to tackle bottlenecks in simulating larger and more physically relevant biological spin systems. We employ a hybrid quantum machine learning approach where we evaluate a parametrized quantum circuit representing the evolution of the system on the quantum device and then update the parameters according to our objective cost function on a classical computer using a gradient optimizer and a noisy gradient free (SPSA) optimizer. We show that a 3 qubit circuit with a series of parametrized rotation gates representing the Hyperfine and Zeeman interactions can be optimized, resulting in a circuit that evolves a singlet state under different magnetic field angles. Moreover, we show that the noisy gradient free optimizer is better suited for optimizing on a noisy quantum device. However, the precision in the quantum circuit outputs is only sub-par at present, leaving room for further improvements.

关键词： Quantum system Biological system modeling Qubit Machine learning Logic gates Kinetic theory Noise measurement

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Generalized Sketch Families for Network Traffic measurement 20

Generalized Sketch Families for Network Traffic Measurement

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2020 SIGMETRICS/Performance Joint international conference on measurement and modeling of computer systems, SIGMETRICS 2020

作者： Zhou, You Zhang, Youlin Ma, Chaoyi Chen, Shigang Odegbile, Olufemi O. University of Florida Gainesville United States

ISBN: (纸本)9781450379854

Traffic measurement provides critical information for network management, resource allocation, traffic engineering, and attack detection. Most prior art has been geared towards specific application needs with specific performance objectives. To support diverse requirements with efficient and future-proof implementation, this paper takes a new approach to establish common frameworks, each for a family of traffic measurement solutions that share the same implementation structure, providing a high level of generality, for both size and spread measurements and for all flows. The designs support many options of performance-overhead tradeoff with as few as one memory update per packet and as little space as several bits per flow on average. Such a family-based approach will unify implementation by removing redundancy from different measurement tasks and support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference in the design of these traffic measurement families and perform experimental comparisons on hardware/software platforms to find their tradeoff, which provide practical guidance for which solutions to use under given performance goals. © 2019 Owner/Author.

关键词： Information management

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Evaluating the Impact of Consensus Mechanisms on Blockchain-Based Data Security: A Systematic Review

Evaluating the Impact of Consensus Mechanisms on Blockchain-...

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Delhi Section conference (DELCON), IEEE

作者： Shubham Narwal Basant Ishant Neda Fatima School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India School of Engineering & Technology Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The increasing use of blockchain in different industries has urged consensus algorithms to be fundamental for effective blockchain operation. Such an understanding still lacks a comprehensive measurement of how consensus algorithms affect the information security of blockchain-based data. The objective of this systematic review is to assess the literature and determine which of the consensus mechanisms is likely to provide the best security for data within blockchain. This study involved a systematic review of the scholarly research articles that were published between the years 2018 and 2024, and the sources used were comprised of IEEE, acm, and ScienceDirect, among others. The screening process was littered with strict criteria for federal, randomized controlled trials, and peer review, which limited to 87 qualitative and quantitative trials of high quality for an extensive analysis. The provided analysis revealed that in the majority of cases, PoS algorithms and the derived models provide better data protection than PoW systems, resulting in 51% of attacks as well as the possibility of selfish mining. The BFT-based mechanisms outperformed other solutions, especially in permissioned blockchains, where one might observe practically immediate finality and a high throughput rate. The present review paper discovered that no single consensus mechanism consistently outperforms the others across all security measures. Therefore, the choice of consensus mechanism should be tailored to specific network scenarios. The present review attempts to provide insights that will significantly aid blockchain architects and security professionals in developing more secure and robust blockchain systems.

关键词： Industries Data security Consensus algorithm Telecommunication traffic Throughput Stability analysis Blockchains Consensus protocol Security Systematic literature review

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