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Online Collaborative Data Caching in Edge Computing

IEEE TRANSACTIONS ON parallel AND distributed systems 2021年第2期32卷 281-294页

作者： Xia, Xiaoyu Chen, Feifei He, Qiang Grundy, John Abdelrazek, Mohamed Jin, Hai Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Huazhong Univ Sci & Technol Serv Comp Technol & Syst Lab Big Data Technol & Syst Lab Cluster & Grid Comp LabSch Comp Sci & Technolgoy Wuhan 430074 Hubei Peoples R China

In the edge computing (EC) environment, edge servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on edge servers can ensure low latency in app users' retrieval of app data. However, an edge server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative edge data caching problem (CEDC) as a constrained optimization problem and prove that it is NP-complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.

关键词： Servers Mobile handsets Cloud computing distributed databases Collaboration Edge computing Data models Edge computing data caching online algorithm

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A Modified Dual Loop Droop Control for parallel Operation of Grid-forming Converters

A Modified Dual Loop Droop Control for Parallel Operation of...

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International Conference for Convergence of Technology (I2CT)

作者： Anshul Awasthi Souvik Bera Ragavan K Electrical Engineering Indian Institute of Technology Gandhinagar India

distributed Generators (DGs) are becoming more integrated into the existing power system due to the growing popularity of renewable energy sources. However, in contrast to synchronous generators in a conventional power system, most renewable-based DGs are connected to the grid via a low inertial power electronic interface, which can impact the system stability significantly due to their uncertain generation. These power electronic converters can operate in grid following or grid forming mode. However, the latter taxonomy is the most widely used topology since it can operate in grid-connected or standalone modes and can self-restore the nominal frequency and voltage conditions. This article presents a modified dual loop droop control technique to operate two grid-forming converters in parallel, acquire precise power sharing, and maintain voltage regulation according to the DGs’ rated capacities. The mathematical formulation and the simulation of the proposed control strategy are performed in MATLAB/Simulink, while OPAL-RT is utilized for the real-time digital simulation of the framework in Software in loop (SIL) mode.

关键词： Power system stability Synchronous generators real-time systems Power electronics Mathematical models Topology Voltage control

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Analysis of parallel Genetic Algorithm and parallel Particle Swarm Optimization Algorithm UAV Path Planning on Controller Area Network

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL systems 2020年第1期31卷 129-140页

作者： Jamshidi, Vahid Nekoukar, Vahab Refan, Mohammad Hossein Shahid Rajaee Teacher Training Univ Sch Elect Engn Tehran Iran

Path planning of the unmanned aerial vehicle (UAV) is one of the complex optimization problems due to the model complexity and a high number of constraints. Evolutionary algorithms are a conventional method to solve complex optimization problems with multiple constraints. However, they are time-consuming algorithms which make their implementation difficult. UAV path planning is usually a real-time problem that is needed to solve in limited time steps. parallelization is one of the most effective methods for reducing the computation time of evolutionary algorithms. In the industrial applications, usually there is a communication platform between devices which can be used for the implementation of the parallelization process. For example, controller area network (CAN) is a protocol used for reliable communication in aerospace systems. Due to the development of this protocol on UAVs and its characteristics, it can be used for parallel optimization of path planning problem. In this paper, an asynchronous distributed multi-master parallel genetic algorithm and a parallel particle swarm optimization algorithm are implemented on CAN bus to improve the speed and performance of the UAV path planning.

关键词： Controller area network (CAN) bus Multi-master parallel genetic algorithm (GA) parallel particle swarm optimization (PSO)

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Experimental Evaluation of a Null-Steered Performance Weighted Blended Beamformer

Experimental Evaluation of a Null-Steered Performance Weight...

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Sensor Array and Multichannel Signal Processing workshop

作者： Jeff Tucker Kathleen E. Wage ECE Department George Mason University Fairfax USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In array signal processing beamformers are used to improve the signal to interference and noise ratio (SINR). Dominant mode rejection (DMR) is a common adaptive beamforming algorithm designed to detect quiet sources in the presence of a few loud interferers. Buck and Singer proposed the Universal DMR (UDMR) beamformer that automates the selection of the subspace rank by blending fixed-rank DMR beamformers based on their performance. While UDMR requires only slightly more computation than a single fixed-rank DMR, it still requires a computationally expensive eigendecomposition. This paper proposes to reduce the computational complexity of the UDMR by replacing the DMR beamformers with a set of conventional beamformers. The null-steered performance weighted blended (NS-PWB) beamformer combines beamformers with nulls steered to interlocking locations so that if a plane-wave interferer is present at least one beamformer in the set will have a null nearby. Additionally, when there are persistent sources of non-plane-wave interference then beamformers can be designed and included in the beamformer set to reject those interferers when they are present. This paper applies the NS-PWB beamformer to data from a vertical line array in the Philippine Sea. The results show that the NS-PWB approximates the SINR performance of DMR beamformers with significant computational savings.

关键词： Array signal processing Conferences Signal processing algorithms Interference parallel processing real-time systems Computational efficiency

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Revisiting Consistent Hashing with Bounded Loads 35

Revisiting Consistent Hashing with Bounded Loads

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35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence

作者： Chen, John Coleman, Benjamin Shrivastava, Anshumali Rice Univ Dept Comp Sci Houston TX 77005 USA

ISBN: (纸本)9781577358664

Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. Load balancing is critical to many areas including cloud systems, distributed databases, and distributed and data-parallel machine learning. A popular and widely adopted solution to dynamic load balancing is the two-decade-old Consistent Hashing (CH). Recently, an elegant extension was provided to account for server bounds. In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. This cascading effect leads to decreasing performance of the hashing procedure with increasing load. To overcome the cascading effect, we propose a simple solution to CH based on recent advances in fast minwise hashing. We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses. On the AOL search dataset and Indiana University Clicks dataset with real user activity, our proposed solution reduces cache misses by several magnitudes.

关键词： distributed database systems

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Strengthening Healthcare Cybersecurity by Optimizing Adversarial Attack Defences in Deep Learning Models using GPU and parallel Processing Technologies

Strengthening Healthcare Cybersecurity by Optimizing Adversa...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Hridya Venugopal Mary Subaja Christo Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu Tamilnadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

In healthcare, safeguarding patient data integrity is paramount, necessitating robust deep learning models. This research focuses on bolstering the cyber security defenses of these models, particularly against adversarial attacks, through the application of the Localized Adversarial Feature Attack (LAFEAT) technique. Leveraging Graphics Processing Units (GPU) and the TensorFlow framework, the study explores algorithmic enhancements like the Fast Gradient Sign Method (FGSM) and Model-Agnostic Meta-Learning (MAML). These techniques perturb input data gradients or adapt model parameters, enhancing resilience against adversarial perturbations. Additionally, Particle Swarm Optimization (PSO), a metaheuristic optimization technique, is examined to fortify defense mechanisms further. parallel processing techniques, utilizing parallel GPU or distributed clusters, are implemented to expedite the optimization process, reducing computational burdens and enhancing scalability. Despite advancements, gaps remain in understanding how these models can be optimized for real-world healthcare applications, particularly in terms of balancing computational efficiency and robustness. The research demonstrates tangible benefits, with a significant 10% increase in success rates against adversarial attacks compared to baseline methods. Moreover, parallel processing implementation results in a 30% reduction in optimization time, improving the efficiency of cyber security defenses for deep learning models in healthcare. These numerical enhancements underscore the research’s value in fortifying healthcare systems against adversarial threats, ensuring the security and integrity of patient data while addressing existing gaps in the optimization of adversarial defenses in healthcare applications. The strengths of this research lie in its robust adversarial attack optimization techniques, integration of parallel processing for efficiency, and application to critical healthcare tasks. Comprehensive

关键词： Deep learning Adaptation models Computational modeling Graphics processing units Medical services parallel processing Data models

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A spatially distributed hydrodynamic model framework for urban flood hydrological and hydraulic processes involving drainage flow quantification

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JOURNAL OF HYDROLOGY 2023年第PartB期625卷

作者： Guo, Kaihua Guan, Mingfu Yan, Haochen Xia, Xilin Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China Univ Birmingham Sch Engn Birmingham England

Two-dimensional hydrodynamic models are increasingly used to predict overland surface water flooding in urban catchments. The complexity of urban surfaces and subsurface drainage systems poses great challenges to modelling urban rainfall-runoff and flood hydraulics. This work develops a spatially distributed hydrodynamic model framework incorporating both hydrological and hydraulic processes of surface and subsurface drainage processes during urban flooding. The model is built upon 2D full shallow water equations solved by a Godunovtype finite volume method with robust treatments of the bed source term. To properly involve drainage flow effects, we present three methods to quantify the drainage flow discharge and its delay time which affect both urban hydrology and surface inundation. Besides, a simple OpenMPI-based parallel algorithm is applied to accelerate the model and ensure computational efficiency when applied to real-world events. The model framework is justified through the validations with five benchmark cases. Benchmark results indicate that the well-balanced treatment of bed source term ensures the model applicability in irregular urban surfaces, the model can predict both urban surface inundation and flood hydrograph, and the proposed drainage flow treatments can well reproduce drainage flow hydrograph. The model framework is successfully applied to simulate rainstorm-induced flood events in a rural-urban catchment, the upper Shenzhen River catchment. Results further demonstrate the robust capability of the model in the quantification of flow exchange between surface and subsurface systems to some extent even in the absence of drainage information. Our framework provides a practical solution for urban flood simulation even when pipe data are scarce.

关键词： Urban flood Hydrodynamic model Shallow Water Equations Bed source treatment Drainage flow configuration

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NPV: Fast Network Policy Verification for Cloud-Native Networking

NPV: Fast Network Policy Verification for Cloud-Native Netwo...

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International Conference on distributed Computing systems

作者： Shunbin Dong Yumin Xie Jin Zhao Kun Qiu School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Network policy plays a crucial role in cloud-native networking, especially in multi-tenant scenarios. It provides precise control over connectivity by specifying source and destination endpoints, traffic types, and other criteria to allow or deny traffic. However, manual configuration of these policies introduces the risk of errors, leading to isolation violations or network service unavailability. Therefore, network policy verification is essential for maintaining security and quality of service in cloud-native networking. Currently, a naïve approach involves individually checking each policy within the cluster, which can take over 100s for verification in a cluster size of over 100k. Existing verification frameworks, like Kano and Verikube, improve performance by leveraging pre-filtering and Satisfiability Modulo Theories (SMT) solvers, achieving a 3.12x to 12.99x performance boost over the naïve baseline. However, as network policy changes rapidly within 100ms in real cloud-native networks, both frameworks need over 10s to perform verification for cluster sizes over 100k, which is far from satisfying. To overcome these issues, we propose and implement a novel network policy verification framework NPV, which utilizes the policy-label pre-filter process with bitwise compression. We further enhance the policy verification algorithm with a policy-namespace divide-and-conquer strategy to improve the data-level parallelism. We implement NPV on commodity servers and evaluate its performance using real network policy datasets. Our experiments indicate that, compared with the state-of-the-art methods, NPV can achieve up to 139.00x to 651.06x improvement in verification time compared to Kano and Verikube, with 65% less memory usage.

关键词： Memory management Clustering algorithms Quality of service Manuals parallel processing Servers Security

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10th International Conference on New Trends in Model and Data Engineering, MEDI 2021 held with 4th International workshop on Modeling, Verification and Testing of Dependable Critical systems, DETECT 2021, Symposium on Intelligent and Autonomous systems, SIAS 2021, 1st workshop on Control Software: Methods, Models, and Languages, CSMML 2021, workshop on Blockchain for Inter-Organizational Collaboration, BIOC 2021 and 1st International Health Data workshop, HEDA 2021

10th International Conference on New Trends in Model and Dat...

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10th International Conference on New Trends in Model and Data Engineering, MEDI 2021 held with 4th International workshop on Modeling, Verification and Testing of Dependable Critical systems, DETECT 2021, Symposium on Intelligent and Autonomous systems, SIAS 2021, 1st workshop on Control Software: Methods, Models, and Languages, CSMML 2021, workshop on Blockchain for Inter-Organizational Collaboration, BIOC 2021 and 1st International Health Data workshop, HEDA 2021

ISBN: (纸本)9783030876562

The proceedings contain 24 papers. The special focus in this conference is on New Trends in Model and Data Engineering. The topics include: Record Linkage for Auto-tuning of High Performance Computing systems;protecting Sensitive Data in Web of Data;COVID-DETECT: A Deep Learning Based Approach to Accelerate COVID-19 Detection;time Insertion Functions;static Checking Consistency of Temporal Requirements for Control Software;visual Language for Device Management in Telecommunication Product Line;Using Process-Oriented Structured Text for IEC 61499 Function Block Specification;the DibiChain Protocol: Privacy-Preserving Discovery and Exchange of Supply Chain Information;towards a Resource-Aware Formal Modelling Language for Workflow Planning;Development of Critical systems with UML/OCL and FoCaLiZe;systematic Literature Review of Methods for Maintaining Data Integrity;medical Data Engineering – Theory and Practice;querying Medical Imaging Datasets Using Spatial Logics (Position Paper);evaluation of Anonymization Tools for Health Data;usages of the ContSys Standard: A Position Paper;systematic Assessment of Formal Methods Based Models Quality Criteria;deriving Interaction Scenarios for timed distributed systems by Symbolic Execution;energy Efficient real-time Calibration of Wireless Sensor Networks for Smart Buildings;edge-to-Fog Collaborative Computing in a Swarm of Drones;Coverage Maximization in WSN Deployment Using Particle Swarm Optimization with Voronoi Diagram;EPSAAV: An Extensible Platform for Safety Analysis of Autonomous Vehicles;bridging Trust in Runtime Open Evaluation Scenarios.

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Optimizing embedded industrial safety systems based on time-of-flight depth imaging 17

Optimizing embedded industrial safety systems based on time-...

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17th IEEE International Conference on eScience, eScience 2021

作者： Thoman, Peter Hirsch, Alexander Wippler, Markus Hranitzky, Robert University of Innsbruck Distributed and Parallel Systems Group Austria Tofmotion GmbH Vienna1120 Austria

ISBN: (纸本)9781665403610

time-of-flight camera systems provide rapid, low-latency depth imaging. In the context of industrial safety, they can be employed to replace costly and inflexible physical safety installations with virtual safety volumes, monitored by time-of-flight cameras which turn off dangerous systems when unknown objects enter specified bounding *** this context, the 3D point-shape collision detection algorithm has to be highly optimized for very specific goals. These include high real-time (worst case) performance, low power consumption and acceptable thermal characteristics, ideally with low-cost and well-established embedded *** present our work in the INPACT project, which explores various algorithmic and parallelization-based methods of improving the performance, responsiveness and reliability of such systems. Our experiments are are based on a setup allowing long-term measurement of multiple thermal sampling points as well as fine-grained power consumption information and of course performance and throughput metrics. © 2021 IEEE.

关键词： Accident prevention

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