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QoE-based Resource Management of applications in the Fog Computing Environment using AHP Technique 6

QoE-based Resource Management of Applications in the Fog Com...

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6th IEEE international conference on Signal processing, Computing and Control, ISPCC 2021

作者： Varshney, Shefali Sandhu, Rajinder Gupta, P.K. Jaypee University Of Information Technology Department Of Computer Science And Engineering Solan India

ISBN: (纸本)9781665425520

Fog Computing supports the data distributions as a computing structure in a well-defined manner. The Fog computing environment executes the smart applications through some computing nodes and the servers in the cloud data centres. Whereas, resource heterogeneity, resource limitation and unpredictable nature of the Fog environment are responsible for the resource management issues. These issues have been considered as one of the significant problems in the Fog computing environment. Developing such resource management approaches appears to be difficult in the Fog computing environment. The multi-criteria decision making (MCDM) techniques are extremely helpful in this type of problem. Also, Resource management issues lie under the MCDM problems. AHP is among some of the well known MCDM techniques which has been considered for selection and ranking. In this paper, a resource management approach has been designed by using the AHP technique for managing the Fog resources on a priority basis. The evaluation of this resource management approach has been done on the basis of selected QoE parameters. The experimental study shows that the proposed approach helps in supervising the Fog resources concerning their ranking and sore values. The results shows that Fog resource 2 with score of 0.23952 is distributed first to the smart application. © 2021 IEEE.

关键词： Fog computing

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Interleaved Function Stream Execution Model for Cache-Aware High-Speed Stateful Packet processing

Interleaved Function Stream Execution Model for Cache-Aware ...

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international conference on distributed Computing Systems

作者： Ziyan Wu Yang Zhang Feng Tian Minjun Wu Antonia Zhai Zhi-Li Zhang University of Minnesota Twin Cities Minneapolis USA

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

The evolving network infrastructure, particularly the 5G core network, is increasingly adopting cloud technologies. This shift brings to the forefront the challenge of meeting the demanding per-packet processing requirements posed by multi-hundred Gbps Ethernet NICs (network interface cards). While traditional NFV (network function virtualization) platforms are effective on older hardware, the per-packet run-to-completion (RTC) execution model for per-packet processing suffers from stalling on state access due to L1/L2 cache misses. Although previous work applying software prefetching can mitigate the issues, their applications are fundamentally limited by the nature of a single execution stream, hence limiting them to batch lookups, suffering from control-flow divergence, and requiring manual tuning. To address the limitations, we introduce a novel interleaved function stream execution model that exploits the function-level parallelism through memory-level parallelism, targeting feature-rich network functions such as 5G Core. To provide the visibility into network functions, we introduce a novel programming model based on the principle of Granular Decomposition, which provides deep visibility into the state access by decoupling the state in a more fine-grained manner compared to traditional modular approaches. We integrate these two innovative designs into a new open-source NF platform, which we refer to as GuNFu. We have tested GuNFu on widely deployed network functions such as 5G UPF (User Plane Function), 5G AMF (Access Management Function), NAT (Network Address Translator) and others. Extensive evaluations reveal that GuNFu can achieve throughput ranging from 1.5 to 6 times over the traditional modular approach.

关键词： 5G mobile communication Prefetching Computational modeling Programming parallel processing Throughput Distance measurement

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Performance Meets Programmabilty: Enabling Native Python MPI Tasks In PyCOMPSs 28

Performance Meets Programmabilty: Enabling Native Python MPI...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Elshazly, Hatem Lordan, Fratacesc Ejarque, Jorge Badia, Rosa M. Barcelona Supercomp Ctr BSC Dept Comp Sci Barcelona Spain

ISBN: (纸本)9781728165820

The increasing complexity of modern and future computing systems makes it challenging to develop applications that aim for maximum performance. Hybrid parallel programming models offer new ways to exploit the capabilities of the underlying infrastructure. However, the performance gain is sometimes accompanied by increased programming complexity. We introduce an extension to PyCOMPSs, a high-level task-based parallel programming model for Python applications, to support tasks that use MPI natively as part of the task model. Without compromising application's programmability, using Native MPI tasks in PyCOMPSs offers up to 3x improvement in total performance for compute intensive applications and up to 1.9x improvement in total performance for 110 intensive applications over sequential implementation of the tasks.

关键词： Hybrid Programming Models distributed Computing MPI High Performance Computing Task-based parallel Programming Models Performance Productivity

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Standardisation of AML/CFT Regulatory Frameworks using Natural Language processing

Standardisation of AML/CFT Regulatory Frameworks using Natur...

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Blockchain Computing and applications (BCCA), international conference on

作者： Chukwunonso Ngwu Zakwan Jaroucheh Nikolaos Pitropakis School of Computing Engineering & the Built Environment Edinburgh Napier University United Kingdom

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

With the G-20 prioritising the development of a solution that will address the challenges of cross-border payment, a lot of focus and research has been exploring ways of using a decentralised ledger technology (DLT) solution to address this challenge. Some of the issues that have been identified with the DLT option are interoperability, scalability, security, and privacy. Central Bank Digital Currencies (CBDCs) implementing DLT have also been acknowledged by experts as having the potential to address some of these challenges. Given the focus on CBDCs as an alternative technology that could address the shortcomings of traditional cross-border payment systems, some of the design challenges that are impacting their widespread adoption have come to the fore. Considering that early CBDC projects were in reaction to emerging digital currencies and therefore were meant to address the domestic needs of each implementing jurisdiction, the lack of a common development framework for developing the technology contributed to the challenge of the interoperability of the various CBDCs across jurisdictions and has impacted it’s fullscale adoption as an alternative to existing cross-border payment systems. With specific focus on regulatory frameworks used by various jurisdictions and also an identified challenge of CBDCs, this paper explores Customer Due Diligence (CDD) regulation as a mechanism for combating anti-money laundering (AML) and counter-terrorist financing (CFT). As part of the efforts to adapt cross-border payment system into a world of distributed ledgers relying on smart contracts, it reviews the impact of divergent regulatory frameworks that limit the interoperability of CBDCs. It discusses some of the existing techniques in place for conducting CDD, such as Know Your Customer (KYC) and how they fit with the new technology. It concludes with a proposal on how the incongruence of legal frameworks and AML/CFT regulations, can be addressed and standardised using new tec

关键词： Text analysis distributed ledger Scalability Semantics Smart contracts Natural language processing Regulation Security Zero knowledge proof Interoperability

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Performance study of HPC applications on an Arm-based cluster using a generic efficiency model 28

Performance study of HPC applications on an Arm-based cluste...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Banchelli, Fabio Peiro, Kilian Querol, Andrea Ramirez-Gargallo, Guillem Ramirez-Miranda, Guillem Vinyals, Joan Vizcaino, Pablo Garcia-Gasulla, Marta Mantovani, Filippo Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781728165820

HPC systems and parallel applications are increasing their complexity. Therefore the possibility of easily study and project at large scale the performance of scientific applications is of paramount importance. In this paper we describe a performance analysis method and we apply it to four complex HPC applications. We perform our study on a pre-production HPC system powered by the latest Arm-based CPUs for HPC, the Marvell ThunderX2. For each application we spot inefficiencies and factors that limit their scalability. The results show that in several cases the bottlenecks do not come from the hardware but from the way applications are programmed or the way the system software is configured.

关键词： Performance analysis High Performance Computing parallel applications Arm ThunderX2

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OEDASA: Optimization Enabled Error-Diluted Approximate Systolic Array Design for an Image processing Application

OEDASA: Optimization Enabled Error-Diluted Approximate Systo...

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international conference on VLSI Design

作者： Dantu Nandini Devi Gandi Ajay Kumar Bindu G Gowda Madhav Rao IIIT-Bangalore Bangalore India

Systolic Array (SA) architecture is a hardware accelerator for running Artificial Intelligence (AI) workloads. Although approximate computing offers hardware and performance benefits, it often sacrifices accuracy, limiting its application to error-resilient tasks. Many inexact multipliers in SA exhibit one-sided Error Distribution (ErD), resulting in significant accumulated errors. Approximate Computing proves valuable in error-resilient image processing applications, fostering research into various approximation techniques for enhanced hardware performance. This paper explores SA architecture with different configurations of approximate multipliers (AMs) featuring distinct ErDs. It employs a meta-heuristic optimization algorithm, Particle Swarm optimization (PSO), for image smoothing tasks. The study delves into trade-offs between hardware acceleration and accuracy, offering insights for advancing approximate computing in SA-based AI workloads. The PSO-evolved SA configuration showcases a 3.05% performance improvement, 10.63% silicon footprint reduction, and 10.32% power savings compared to the exact SA. The PSO-derived SA structure also offers notable hardware gains when compared with other state-of-the-art (SOTA) benchmark designs. Additionally, it demonstrates a better Structural Similarity Index (SSIM) compared to SA with one-sided error-distributed AMs. The proposed spatially optimized SAs with AMs of different ErDs represent a step towards reliable hardware design and establishing a nearly exact AI accelerator system. All design files and results are shared openly with the research and design community for easy adoption and further exploration.

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Re-structuring CNN using quantum layer executed on FPGA hardware for classifying 2-D data

Re-structuring CNN using quantum layer executed on FPGA hard...

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international conference on Integrated Circuits, Design, and Verification (ICDV)

作者： Nhat Hoang Bach Le Ha Vu Dinh Lam Tran Thanh Toan Dao Thi Thu Hong Luu Duy Ninh Nguyen Institute of Electronics Academy of Science and Technology Hanoi Vietnam Electronic department University of Transport and Communications Hanoi Vietnam

ISBN: (数字)9798350371864

ISBN: (纸本)9798350371871

The article proposes a solution to restructure Convolutional Neural Network (CNN) architectures by integrating parameter quantization techniques with traditional CNN models capable of deployment on Field-Programmable Gate Array (FPGA) hardware for evaluating the classification performance of two-dimensional data in real-world scenarios. The solution introduces an additional quantum layer before the final classification layer of the CNN to receive standardized outputs, followed by computations in the Hilbert vector space to generate probability values for assessing classification results. The quantization process helps the model swiftly identify data features while optimizing the parallel computing capability of FPGA hardware. The model is evaluated on the MNIST handwritten digit dataset, revealing two advantages: time-processing on FPGA is four times faster compared to using only the Central processing Unit (CPU) on the PynQ-z2 kit board; classification accuracy is higher when utilizing the quantum layer compared to without it, with the same number of training iterations. These results demonstrate the feasibility of hardware-accelerated AI algorithms combined with quantum algorithms in real applications.

关键词： Quantization (signal) Computational modeling Hardware Data models Vectors Central processing Unit Convolutional neural networks

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Demystifying Privacy and Security Issues in Potentially Harmful Mobile applications

Demystifying Privacy and Security Issues in Potentially Harm...

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international conference on distributed Computing Systems

作者： Kaifa Zhao Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong SAR

Android system's features, such as support for peer-to-peer networking, cloud computing services, and various communication protocols and network technologies, make it a suitable platform for distributed computing systems that require efficient resource management and coordination. The widespread popularity of Android, as one of the most widely-used mobile operating systems [1], is attributed to its ability to provide users with a wide range of convenient and entertaining options through its functional apps. Nevertheless, mobile users may face a risk to their privacy and property from potentially harmful apps that can be installed on their devices. Thus, it is imperative to conduct an analysis of potentially harmful applications [2]–[10] with a sense of urgency. Our research focuses on combining Android static analysis, adversarial machine learning, and natural language processing techniques to investigate app behavior, discover vulnerabilities in Android malware detection systems, and understand software privacy policy statements. To safeguard user privacy from potentially harmful apps, we propose the following measures: (I) Investigating the vulnerability of Android malware detection systems under evolving attacks and proposing defense solutions [11]; (II) Analyzing whether Android app privacy policies meet regulatory requirements [12]; and (III) Examining the privacy concerns associated with third-party libraries in Android [13]. For (I), to investigate the vulnerability of Android malware detection (AMD) systems under evolving attacks and discover defense solutions, we propose a Heuristic optimization model integrated with Reinforcement learning framework to optimize our structural ATtack, namely HRAT [11], which is the first problem-pace structural attack designed to deceive AMD. HRAT employs four types of graph modification operations and corresponding bytecode manipulation techniques to generate executable adversarial apps that can evade detection. HRAT bridg

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Towards Network-accelerated ML-based distributed Computer Vision Systems 27

Towards Network-accelerated ML-based Distributed Computer Vi...

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27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

作者： Siddique, Hisham Neves, Miguel Kuzniar, Carson Haque, Israat Dalhousie University Faculty of Computer Science HalifaxNS Canada

ISBN: (纸本)9781665408783

Computer vision is a crucial component in many modern applications (e.g., medical image analysis, environmental monitoring and self-driving cars). However, their stringent computational, latency and bandwidth requirements still pose a huge challenge to system architects, which must seek for alternatives to both the limited resources (e.g., low-end CPU) on client devices and the hurdles of moving data from clients to cloud/edge servers for analysis. In this work, we advocate for the usage of emerging programmable network devices to speed up ML-based computer vision tasks, particularly image classification, on resource constrained environments. To take the first step towards this new paradigm, we propose NetPixel, a framework that enables P4-programmable switches to classify images in realtime, accurately and at scale. We implemented a prototype of NetPixel in a software switch to show its feasibility and conducted a preliminary evaluation on widely adopted datasets. Our results show that NetPixel can classify images with an accuracy within 8% that of a server-based implementation even for shallow classifiers and low-resolution images. © 2021 IEEE.

关键词： Network protocols

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Hybrid Image Encryption for IoT applications: Integrating Cellular Automata and Henon Map to Improve Security and Performance

Hybrid Image Encryption for IoT Applications: Integrating Ce...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Biswarup Yogi Ajoy Kumar Khan Satyabrata Roy Dept. of Computer Engineering Mizoram University Aizawl Mizoram India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Securing the vast volumes of data exchanged over networks is crucial in IoT (Internet of Things) applications. This study introduces an innovative image encryption algorithm that combines the interactive properties of Cellular Automata (CA) and the Henon map to improve both security and performance. CA, renowned for its simplicity and parallelism, are utilized to produce intricate pseudorandom sequences. These sequences are then used as the keystream for encryption purposes. Whereas, the Henon map is employed to incorporate further levels of unpredictability and intricacy into the encryption procedure. The proposed method is thoroughly assessed through considerable experimentation and is compared with existing methodologies. The findings demonstrate substantial enhancements with respect to various standard metrics such as “Mean Squared Error (MSE)”., “Peak Signal-to-Noise Ratio (PSNR)”., “Unified Average Changing Intensity (UACI)”., and “Number of Pixels Change Rate (NPCR)” measurements., underscoring the resilience and efficacy of the proposed methodology. The results emphasize the capability of this hybrid method to offer a secure and robust performance for image encryption in IoT applications. This method guarantees the integrity and confidentiality of data while outperforming current encryption techniques in performance.

关键词： Image quality PSNR Measurement uncertainty Automata parallel processing Encryption Internet of Things Standards Resilience Random sequences

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