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DPLE: A Privacy-Enhanced and Straggler-Resilient distributed Learning Framework for Smart Cloud 9

DPLE: A Privacy-Enhanced and Straggler-Resilient Distributed...

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9th ieee international conference on Smart Cloud, SmartCloud 2024

作者： Xue, Yilei Li, Jianhua Wu, Jun Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350389500

distributed machine learning (DML) encounters issues related to privacy and the presence of straggling nodes in smart cloud systems. Lagrange coded computing offers a partial solution to mitigate these concerns. Nonetheless, the privacy of the system becomes vulnerable when the number of semi-trusted nodes surpasses a specific limit, or when external eavesdroppers are present. To confront this hurdle, we introduce a novel framework for distributed learning called DPLE (Differentially Private Lagrange Encoding). this framework employs Lagrange interpolation polynomials to obscure the original data while introducing redundancy, thus improving privacy safeguards and increasing robustness to straggling nodes. It also incorporates artificial noise into local computation outcomes to protect confidential data from potential exposures. Furthermore, we perform theoretical analyses to identify the necessary variance of this noise to maintain desired privacy levels. Experimental validations confirm the efficacy of DPLE and examine how different settings of system parameters impact the accuracies of the results. © 2024 ieee.

关键词： Interpolation

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A Fast Belief Propagation-Based distributed Gauss-Newton Method for Power System State Estimation 8

A Fast Belief Propagation-Based Distributed Gauss-Newton Met...

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8th international conference on Cloud computing and Big Data Analytics (ICCCBDA)

作者： Guo, Peng Shi, Danni Wang, Xuan Shi, Xinghua State Grid Smart Grid Res Inst Co Ltd State Grid Lab Grid Adv Comp & Applicat Beijing 102209 Peoples R China State Grid Smart Grid Res Inst Co Ltd Zhejiang 310007 Peoples R China

ISBN: (纸本)9781665455336

State estimation is the foundation for a variety of online power system applications in energy management systems, and the stability of power systems is directly impacted by the speed with which current system states can be obtained through state estimation. this paper proposed a fast Gaussian-Newton state estimation method for power systems based on parallel belief propagation, which implements the Gaussian belief process via multi-core and multi-thread parallel computation to achieve efficient state estimation. Simulation findings on numerous ieee-standard power systems show that the suggested technique outperforms the traditional algorithm.

关键词： Gauss-Newton state estimation factor graphs belief propagation parallel computing

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Model-Driven System Development for a Cloud-Based Healthcare Application Using Colored Petri Nets 18

Model-Driven System Development for a Cloud-Based Healthcare...

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18th Annual ieee international systems conference (SysCon)

作者： Cagaanan, Adrian Gehlot, Vijay Villanova Univ Dept Comp Sci Villanova PA 19085 USA Villanova Univ Ctr Excellence Enterprise Technol Villanova PA 19085 USA

ISBN: (纸本)9798350358810;9798350358803

the 21st century has brought widespread adoption of the cloud computing paradigm when constructing large-scale software systems. While the approach has enabled systems to exhibit software quality attributes such as high agility and scalability, it has also magnified long-standing issues related to maintaining both software development standards and their accompanying software implementations. the research aims to improve or maintain the quality of consumer-driven software as cloud computing adoption becomes more prevalent. Our model-driven approach intends to offer solutions to fix the typical disconnect between design and implementation. the paper demonstrates the construction of a cloud-based healthcare application using a modeling and simulation language called Colored Petri Nets (CPNs). It demonstrates design verification techniques during a model simulation and software prototyping in the context of a cloud-based healthcare application. the healthcare domain serves as an ideal candidate since the domain usually lags behind other industries in updating software development approaches and tools. Furthermore, a cloud-first approach allows for infrastructure that supports millions of patients and physicians distributed globally. the paper aims to promote the technical merits of model-driven design in maintaining high assurance of software quality standards, especially in the context of cloud-based applications. Key contributions of the paper are adoption of model-driven system development in the context of cloud-based applications and integration of machine-learning models into simulation models for such purposes.

关键词： model-driven system development cloud computing colored petri nets

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VATE: Edge-Cloud System for Object Detection in Real-Time Video Streams 8

VATE: Edge-Cloud System for Object Detection in Real-Time Vi...

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8th ieee international conference on Fog and Edge computing (ICFEC)

作者： Maresch, Maximilian Nastic, Stefan TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9798350361360;9798350361353

In the realm of edge intelligence, emerging video analytics applications are often based on resource constrained edge devices. these applications need systems which are able to provide both low-latency and high-accuracy video stream processing, such as for object detection in real-time video streams. State-of-the-art systems tackle this challenge by leveraging edge computing and cloud computing. Such edge-cloud approaches typically combine low-latency results from the edge and high accuracy results from the cloud when processing a frame of the video stream. However, the accuracy achieved so far leaves much room for improvement. Furthermore, using more accurate object detection often requires having more capable hardware. this limits the edge devices which can be used. Applications related to autonomous drones, with the drone being the edge device, give one example. A wide variety of objects needs to be detected reliably for drones to operate safely. Drones with more computing capabilities are often more expensive and suffer from short battery life, as they consume more energy. In this paper, we introduce VATE, a novel edge-cloud system for object detection in real-time video streams. An enhanced approach for edgecloud fusion is presented, leading to improved object detection accuracy. A novel multi-object tracker is introduced, allowing VATE to run on less capable edge devices. the architecture of VATE enables it to be used when edge devices are capable of running on-device object detection frequently and when edge devices need to minimise on-device object detection to preserve battery life. Its performance is evaluated on a challenging, dronebased video dataset. the experimental results show that VATE improves accuracy by up to 27.5% compared to the state-of-theart system, while running on less capable and cheaper hardware.

关键词： Video Analytics Edge Intelligence Edge computing Edge-Cloud systems Object Detection Object Tracking

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ADSwitch: Adaptive CPU Scheduling for distributed systems 21

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

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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： Zhong, Yidong Ao, Yang Li, Dingding Li, Jianguo Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. the experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness. © 2023 ieee.

关键词： Recurrent neural networks

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15th international Green and Sustainable computing conference

作者： Gnibga, Wedan Emmanuel Blavettet, Anne Orgerie, Anne-Cecile Univ Rennes INRIA CNRS IRISA Rennes France Univ Rennes ENS Rennes CNRS SATIE Lab Rennes France

ISBN: (纸本)9798331507879;9798331507862

Self-consumption is developing worldwide to increase renewable electricity consumption and reduce electricity bills. It can be carried out individually or collectively (grouping several entities), but is generally restricted geographically. In datacenters, one may use load shifting to benefit from the self-consumption tariffs, at the cost of increasing energy consumption. An alternative would be to extend the self-consumption rules to wider perimeters. this paper proposes a comparative study on several aspects influencing the collective self-consumption (CSC) of an Edge infrastructure, including spatial load shifting, temporal load shifting and extending the current rules to encompass wider geographical boundaries. Spatial shifting under the current CSC scheme is found more cost-effective (3.9% of cost reduction) with a negligible increase in energy consumption (0.19%), compared to the revised definition of collective self-consumption which leads to 3.7% cost reduction and no increase in energy consumption. Moreover, allowing up to 10% of the user tasks to be shifted in time can further increase the self-consumption rate by 1.3%.

关键词： Collective self-consumption distributed systems load shifting carbon footprint

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Designing Hash and Encryption Engines using Quantum computing 37

Designing Hash and Encryption Engines using Quantum Computin...

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37th international conference on VLSI Design (VLSID) / 23rd international conference on Embedded systems (ES)

作者： Upadhyay, Suryansh Roy, Rupshali Ghosh, Swaroop Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9798350384406

Quantum computing (QC) holds the promise of revolutionizing problem-solving by exploiting quantum phenomena like superposition and entanglement. It offers exponential speed-ups across various domains, from machine learning and security to drug discovery and optimization. In parallel, quantum encryption and key distribution have garnered substantial interest, leveraging quantum engines to enhance cryptographic techniques. Classical cryptography faces imminent threats from quantum computing, exemplified by Shor's algorithm's capacity to breach established encryption schemes. However, quantum circuits and algorithms, capitalizing on superposition and entanglement, offer innovative avenues for enhancing security. In this paper we explore quantum-based hash functions and encryption to fortify data security. Quantum hash functions and encryption can have numerous potential application cases, such as password storage, digital signatures, cryptography, anti-tampering etc. the integration of quantum and classical methods demonstrates potential in securing data in the era of quantum computing.

关键词： Quantum computing Hash Encryption Decryption

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Accelerating distributed ML Training via Selective Synchronization 25

Accelerating Distributed ML Training via Selective Synchroni...

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25th ieee international conference on Cluster computing (CLUSTER)

作者： Tyagi, Sahil Swany, Martin Indiana Univ Bloomington Bloomington IN 47405 USA

ISBN: (纸本)9798350307924

In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not linearly scale-out due to high communication cost of aggregation. To mitigate this overhead, alternatives like Federated Averaging (FedAvg) and Stale-Synchronous Parallel (SSP) either reduce synchronization frequency or eliminate it altogether, usually at the cost of lower final accuracy. In this paper, we present SelSync, a practical, low-overhead method for DNN training that dynamically chooses to incur or avoid communication at each step either by calling the aggregation op or applying local updates based on their significance. We propose various optimizations as part of SelSync to improve convergence in the context of semi-synchronous training. Our system converges to the same or better accuracy than BSP while reducing training time by up to 14x.

关键词： Cluster computing distributed systems Data-parallel training Federated learning Deep learning

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Hybrid Visual Verification 14

Hybrid Visual Verification

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14th international conference on Cloud computing, Data Science and Engineering, Confluence 2024

作者： Bushra, S.Nikkath Syed Ismail, A. Sasigresa, P. Edwin Singh, C. Srm Institute of Scienece and Technology Department of Computing Technologies Chengalpattu Kattankulathur India Srm Institute of Scienece and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India Veltech University Department: Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department: Computer Science and Engineering India

ISBN: (纸本)9798350344837

Data security is becoming increasingly important as cloud computing advances. Data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications and services belonging to various organizations spread across different locations. Cloud computing raises security concerns, because it shares data with distributed users via network in an open terrain. In this system, some of the most important security services like image-based security are handled by the cloud computing system. the proposed scheme takes care of several attacks like botnets, malware and guessing attacks. this scheme is contrasted withseveral established schemesand a new form of authentication is used for the purpose of storing files in a secure manner. © 2024 ieee.

关键词： Authentication

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distributed systems - What Every Software Architect Should Know 20

Distributed Systems - What Every Software Architect Should K...

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ieee 20th international conference on Software Architecture (ICSA)

作者： Gorton, Ian Northeastern Univ Khoury Coll Comp Sci Seattle WA 98109 USA

ISBN: (纸本)9781665464598

Modern software systems in every application domain are increasingly built as distributed systems. Business applications are structured as cooperating microservices, IoT devices communicate with cloud-based services over a network, and Web sites store data in globally dispersed data centers to support fast access in to localitiesin which their users reside. Behind all these systems lurk distributed computing infrastructures that architects and engineers must exploit to satisfy application service level agreements. To be successful, it is essential that architects understand the inherent complexity of distributed systems. In this half day tutorial, I'll guide the attendees through the fundamental characteristics that distributed systems exhibit. Each characteristic will be related to the software architecture quality attributes that they directly impact. the topics covered include communications reliability and latencies, message delivery semantics, state management, idempotence, data safety, consistency, time, distributed consensus, cascading failures and failover and recovery. I'll introduce each concept using an example distributed system and multiple 'props' to illustrate concepts. Once I've explained a concept using the example, Ill move on to show how the concept manifests itself in a software system and its effects on quality attributes requirements and inherent trade-offs. the tutorial will be suitable for graduate students, engineers and architects who have no or minimal exposure to distributed systems concepts. the presentation format will be suitable for a mix of both in person and remote participants. It will combine interactive sessions with short technical explanations and examples to illustrate each distributed systems concept.

关键词： distributed systems software architecture

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