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Removing Decorator to Improve Energy Efficiency 29

Removing Decorator to Improve Energy Efficiency

引用

29th ieee International conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Bree, Deaglan Connolly Cinneide, Mel O. Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9781665437868

With growing concerns regarding climate change and rising energy costs, there is an increased focus on improving the energy efficiency of software. One relevant avenue of research is the transformation of software designs in order to increase efficiency and reduce energy consumption. Design Patterns are of interest here as the indirection they introduce is likely to have a detrimental effect on energy consumption. In this paper we report on our investigations into the impact on energy consumption of the Decorator pattern, which is a structural design pattern used to enable additional behaviour to be attached to an individual component dynamically. Application of this pattern improves the maintainability of software at the cost of increased indirection and redundancy, which may reduce energy efficiency. In this paper we explore features of the Decorator pattern that impact on its systematic removal, with a future goal of removing this pattern using automated refactoring. We subsequently examine experimentally the Decorator pattern's impact on energy consumption in Java and find its implementation results in an increased number of instantiated objects and method invocations at run-time. The removal of the pattern can result in up to a 96% reduction in energy consumption in a best-case scenario with a textbook example, and up to a 5% reduction in energy consumption in an open-source project with a case study examining JUnit, a popular unit testing framework for Java. These results demonstrate the energy savings achievable with the removal of Decorator pattern instances and open the way for similar investigations of other design patterns.

关键词： Design Patterns Decorator Pattern Green Technology Energy Efficiency Refactoring Software Transformation Software Design Object-Oriented programming

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PortSec:Securing Port Knocking System using Sequence Mechanism in SDN Environment 18

PortSec:Securing Port Knocking System using Sequence Mechani...

引用

18th ieee International Wireless Communications and Mobile Computing conference (IWCMC)

作者： Pali, Isha Amin, Ruhul IIIT Naya Raipur Dept Comp Sci & Engn Uparwara India

ISBN: (纸本)9781665467490

Port knocking provides an added layer of security on top of the existing security systems of a network. A predefined port knocking sequence is used to open the ports, which are closed by the firewall by default. The server determines the valid request if the knocking sequence is correct and opens the desired port. However, this sequence poses a security threat due to its static nature. This paper presents the port knock sequence-based communication protocol in the Software Defined network (SDN). It provides better management by separating the control plane and data plane. At the same time, it causes a communication overhead between the switches and the controller. To avoid this overhead, we are using the port knocking concept in the data plane without any involvement of the SDN controller. This study proposes three port knock sequence-based protocols (static, partial dynamic, and dynamic) in the data plane. To test the protocol in SDN environment, the P4 implementation of the underlying model is done in the BMV2 (behavioral model version 2) virtual switch. To check the security of the protocols, an informal security analysis is performed, which shows that the proposed protocols are secured to be implemented in the SDN data plane.

关键词： Port knocking Security SDN P4 programming Security Threats

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Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression 42

Heterogeneity-Aware Federated Learning with Adaptive Client ...

引用

42nd ieee International conference on Computer Communications, INFOCOM 2023

作者： Jiang, Zhida Xu, Yang Xu, Hongli Wang, Zhiyuan Qian, Chen University of Science and Technology of China School of Computer Science and Technology China University of Science and Technology of China Suzhou Institute for Advanced Research China University of California Jack Baskin School of Engineering Department of Computer Science and Engineering United States

ISBN: (纸本)9798350334142

Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data. However, the existing works fail to address all these practical concerns in FL: limited communication resources, dynamic network conditions and heterogeneous client properties, which slow down the convergence of FL. To tackle the above challenges, we propose a heterogeneity-aware FL framework, called FedCG, with adaptive client selection and gradient compression. Specifically, the parameter server (PS) selects a representative client subset considering statistical heterogeneity and sends the global model to them. After local training, these selected clients upload compressed model updates matching their capabilities to the PS for aggregation, which significantly alleviates the communication load and mitigates the straggler effect. We theoretically analyze the impact of both client selection and gradient compression on convergence performance. Guided by the derived convergence rate, we develop an iteration-based algorithm to jointly optimize client selection and compression ratio decision using submodular maximization and linear programming. Extensive experiments on both real-world prototypes and simulations show that FedCG can provide up to 5.3× speedup compared to other methods. © 2023 ieee.

关键词： Linear programming

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FPGA for Aggregate Processing: The Good, The Bad, and The Ugly 37

FPGA for Aggregate Processing: The Good, The Bad, and The Ug...

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37th ieee International conference on Data Engineering (ieee ICDE)

作者： Eryilmaz, Zubeyr F. Kakaraparthy, Aarati Patel, Jignesh M. Sen, Rathijit Park, Kwanghyun Univ Wisconsin Madison Madison WI 53706 USA Microsoft Gray Syst Lab Madison WI USA

ISBN: (纸本)9781728191843

In this paper, we focus on current CPU-FPGA architectures and study their usability for database management systems. To focus our scope, we choose aggregation as the query processing primitive for this investigation. We implement a fully pipelined stall-free module that performs aggregation on the FPGA, and also describe a performance model that predicts the runtime of this module with 99% accuracy. We study the performance of this module on two different CPU-FPGA architectures, namely remote-main-memory and bump-in-the-wire. Compared to an implementation of aggregation on CPU, we find that the former is 1.7x slower whereas the latter is 2.2x faster. This significant performance gap suggests two important architectural considerations when designing CPU-FPGA systems, namely the bandwidth ceiling and the resource ceiling, while also highlighting issues of switching times and programmer efficiency. We consider broader hardware trends to study the suitability of the two FPGA architectures for accelerating the aggregation operation, and find that the performance gap is likely to stay in the coming future. Based on these observations, we discuss some challenges and opportunities for CPU-FPGA architectures.

关键词： Runtime Query processing Aggregates Switches Bandwidth Tools programming

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A Contact-Based Classification to Aid Routing in Heterogeneous Wireless networks 15

A Contact-Based Classification to Aid Routing in Heterogeneo...

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15th ieee International conference on Advanced networks and Telecommunications Systems (ieee ANTS)

作者： El Alaoui, Sara Ramamurthy, Byrav Univ Nebraska Lincoln Dept Com Sc & Eng Lincoln NE USA UNL Dept Comp Sc & Eng Lincoln NE 68588 USA

ISBN: (纸本)9781665448932

The emergence of the Internet of Things (IoT), Internet of Everything (IoE), and now Tactile Internet (TI) among other new networking paradigms, has been enabled by the architectural changes of the Internet through 5G, 6G and beyond. New network architectures had to be heterogeneous in order to fulfill the tighter requirements for latency, reliability, adaptability, etc, the seamless support for a variety of devices, protocols and applications led to the creation of the multi-layered network architectures that include Non Terrestrial networks (NTN). Many research questions remain open and routing is one that is especially challenging. One manifestation of this heterogeneity is the diversity of spatio-temporal contacts established for communication between different network entities both inter- and intra-layer. In this paper, we expand the definition of heterogeneous wireless networks. We then propose a novel and rigorous mathematical model for contact classification that captures several aspects of heterogeneity. Finally, we provide real-world application examples and map them to our proposed classification.

关键词： Heterogeneous Wireless networks contact classification Routing network modeling 5G Tactile Internet multi-layered network architecture

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Research on Fault Recovery Reconfiguration of Distribution network with Soft open Point and Cold Load Pick-up

Research on Fault Recovery Reconfiguration of Distribution N...

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Energy Systems and Electrical Power (ICESEP), International conference on

作者： Min Wang Zijian Zhang Mingyu Ma Zhen Zheng School of Electrical and Power Engineering Hohai university Nanjing China Qingpu Power Supply Company State Grid Shanghai Electric Power Company Shanghai China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

To improve the fault recovery capability of distribution networks under the consideration of cold load startup impact, this paper proposes a fault recovery reconfiguration strategy for distribution networks containing smart soft switches considering cold load. Firstly, the conceptual characteristics of cold load and its influencing factors are analyzed; secondly, with the objectives of maximizing the weighted load recovery and minimizing the network loss, a reconfiguration model of distribution network fault recovery with soft open point is established and transformed into a mixed-integer second-order conic programming model; lastly, an arithmetic case analysis is carried out through the ieee33 node system to verify the validity of the proposed recovery strategy.

关键词： Analytical models Soft switching Distribution networks programming Surges Load modeling Arithmetic

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network Slicing in O-RAN-Enabled Cell-Free Massive MIMO: A DRL-Based Power Control

Network Slicing in O-RAN-Enabled Cell-Free Massive MIMO: A D...

引用

ieee conference on Wireless Communications and networking

作者： Mahdi Eskandari Mostafa Rahmani Alister G. Burr School of Physics Engineering and Technology University of York York United Kingdom

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The advent of 5G networks necessitates more flexible and intelligent architectures, prompting a shift from conventional models to open Radio Access networks (0-RAN) augmented with integrated network slicing (NS). The combination of O-RAN with a cell-free architecture improves both coverage and performance, while NS facilitates dynamic resource allocation to support diverse services, such as ultra-reliable low-latency communications (uRLLC) and enhanced mobile broadband (eMBB). This paper introduces a novel NS-enabled, cell-free O-RAN framework designed to optimize resource allocation and power control. In contrast to traditional methods, we adopt a Deep Reinforcement Learning (DRL) approach, leveraging the Soft Actor-Critic (SAC) algorithm to dynamically allocate power and resources across distributed access points (APs), while simultaneously ensuring the efficient management of network slices. The proposed framework aims to maximize the number of admitted slices while minimizing network costs, ensuring optimal data rates for eMBB and low latency for uRLLC services. Through this approach, we demonstrate enhanced flexibility, scalability, and performance in dynamic 5G wireless environments.

关键词： Wireless communication Power demand Spectral efficiency network slicing Scalability Power control open RAN Massive MIMO Ultra reliable low latency communication Resource management

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Research on the Collaborative Mechanism of P4Runtime and SAI in White Box Switches Based on the SONiC System

Research on the Collaborative Mechanism of P4Runtime and SAI...

引用

Power Electronics, Computer Applications (ICPECA), ieee International conference on

作者： Zhuyu Pan Hongchao Wang Chen Yan Zishi Zhang School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

With the advancement of Software-Defined networking (SDN) technology, network programmability and flexibility have been significantly enhanced. However, effectively integrating traditional network systems with emerging programmable platforms remains a critical research challenge. This paper proposes a fusion architecture based on the P4Runtime protocol to achieve deep integration of SONiC white box switches and the P4 platform. By introducing SDN programmability, we design and implement a hybrid model that enables traditional network architectures to achieve advanced functionalities with the assistance of external SDN controllers. This paper provides a detailed discussion of the architecture design and implementation process, including P4 code development, compilation, target configuration file generation, and interaction mechanisms between the control plane and the data plane. Furthermore, we validate the applicability and performance of the architecture in various scenarios, offering robust support for future research and applications in programmable networks.

关键词： Protocols Process control Computer architecture Switches network architecture programming Control systems Power electronics Software defined networking Glass box

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Deep Neural networks architectures from the Perspective of Manifold Learning

Deep Neural Networks Architectures from the Perspective of M...

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Pattern Recognition and Artificial Intelligence (PRAI), International conference on

作者： German Magai Department of Big Data and Information Retrieval Higher School of Economics Moscow Russia

Despite significant advances in the field of deep learning in applications to various areas, an explanation of the learning process of neural network models remains an important open question. The purpose of this paper is a comprehensive comparison and description of neural network architectures in terms of geometry and topology. We focus on the internal representation of neural networks and on the dynamics of changes in the topology and geometry of a data manifold on different layers. In this paper, we use the concepts of topological data analysis (TDA) and persistent homological fractal dimension. We present a wide range of experiments with various datasets and configurations of convolutional neural network (CNNs) architectures and transformers in CV and NLP tasks. Our work is a contribution to the development of the important field of explainable and interpretable AI within the framework of geometrical deep learning.

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Neural network Architecture Search Algorithm for Technical Object State Prediction

Neural Network Architecture Search Algorithm for Technical O...

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2025 International Russian Smart Industry conference, SmartIndustryCon 2025

作者： Kuvayskova, Yuliya Nemykin, Aleksandr Ulyanovsk State Technical University Department of applied mathematics and informatics Ulyanovsk Russia

ISBN: (纸本)9798331511241

The paper presents a new algorithm for automated search for neural network architecture for predicting the state of technical objects. Prediction is a key element of monitoring, allowing us to prevent accidents and improve the reliability of complex systems. The proposed approach combines the methods of Cartesian genetic programming, multi-criteria evolutionary algorithms and Bayesian optimization. This algorithm adapts neural network architectures to different types of data, speeds up the search for a suitable model configuration, and improves forecast accuracy. The developed algorithm was implemented as a standalone program in the Python programming language using the TensorFlow and Keras libraries. The effectiveness of the developed approach has been tested in practice in predicting the remaining service life of bearings, a turbojet engine, and a Li-ion battery. The AutoKeras library was used to compare the results. The study showed that the use of the developed algorithm significantly improves the performance quality of neural network models compared to the models obtained using the AutoKeras library. The algorithm demonstrated high accuracy of predictions, stability to various conditions, and potential for application in monitoring systems of technical objects. © 2025 ieee.

关键词： Python

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