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Special Track on Dependable, Adaptive, and Secure distributed Systems

Proceedings of the ACM Symposium on Applied Computing

引用

Proceedings of the ACM symposium on Applied computing 2024年 224-225页

作者： Goeschka, Karl M. Hiltunen, Matti Oliveira, Rui Uas Technikum Vienna Austria At and T Shannon Laboratory United States Universidade Do Minho Portugal

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Cost-based Load Balancing of RDF Reasoning in Fog-computing Environments 24

Cost-based Load Balancing of RDF Reasoning in Fog-Computing ...

引用

39th Annual ACM symposium on Applied computing (SAC)

作者： Kokubo, Yuma Amagasa, Toshiyuki Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9798400702433

Fog computing has attracted growing attention as a distributed computing platform for real-world problems, and semantic web technologies, including RDF (resource description framework), have been used as key enablers for semantic data processing. RDF reasoning allows us to perform high-level reasoning about real-world entities, but RDF reasoning in a distributed environment is still challenging because of the changing nature of the real world, leading to unbalanced loads among fog nodes and cloud servers. Existing distributed reasoning methods in fog computing cannot cope with such dynamism. this work proposes a dynamic load-balancing method for RDF reasoning in fog environments. Specifically, we devise a cost model of RDF reasoning considering the processing and network loads, thereby enabling balancing the load between fog nodes. We conduct a set of experiments to demonstrate the performance of the method.

关键词： Fog computing RDF reasoning load balancing IoT

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Collection and Analysis of Sensitive Data with Privacy Protection by a distributed Randomized Response Protocol 24

Collection and Analysis of Sensitive Data with Privacy Prote...

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39th Annual ACM symposium on Applied computing (SAC)

作者： Imran, Faisal Meo, Rosa Bahria Univ Islamabad Pakistan Univ Torino Turin Italy

ISBN: (纸本)9798400702433

the data collected from personal devices is intrinsically private and should be collected through a privacy-guaranteed mechanism. Local differential privacy solves privacy problems by collecting randomized responses from each user, and it does not need to rely on a trusted data aggregator/curator. the proposed approach utilizes the randomized response technique in a novel manner: it guarantees privacy to users during the data collection and simultaneously preserves the high utility of the analysis. It can be seen as a case of synthetic data generation by producing contingency tables (marginals) in a privacy-preserving mechanism. this article describes the proposed randomized response technique and discusses the motivating applications domains. It justifies why it satisfies the property of differential privacy and utility guarantees theoretically and through experimental analysis with excellent results.

关键词： Randomized Response Local Differential Privacy Contingency Tables Privacy protection distributed computation protocol

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OASIS: An Organizational CertificateLess Aggregate Signature Scheme in distributed Networks for IoT 24

OASIS: An Organizational CertificateLess Aggregate Signature...

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39th Annual ACM symposium on Applied computing (SAC)

作者： Gritti, Clementine EURECOM Sophia Antipolis France

ISBN: (纸本)9798400702433

In recent years, a large number of CertificateLess Aggregate Signature (CLAS) schemes have been proposed to overcome both the complexity of Public Key Infrastructure (PKI) certificate management and the key escrow problem. these CLAS schemes have mostly been developed for the Internet of things (IoT). However, the current CLAS schemes require the trusted authority to manage all the devices in a network, whose number and turn-over are huge. One way to alleviate devices' management in IoT while improving access to resources is to consider a distributed architecture. In this paper, we introduce OASIS, an Organizational CertificateLess Aggregate SIgnature Scheme in IoT networks. OASIS is a hierarchical CLAS scheme that delegates the devices' management workload to multiple entities, while mitigating PKI certification and key escrow issues. We prove the security of OASIS in the random oracle model. Furthermore, the experimental results show that OASIS is well suitable for IoT distributed systems.

关键词： Certificateless aggregate signatures Organizational chart distributed networks Internet of things Random oracle model

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Dynamic Optimization of the Latency throughput Trade-off in Parallel Chain distributed Ledgers 24

Dynamic Optimization of the Latency Throughput Trade-off in ...

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39th Annual ACM symposium on Applied computing (SAC)

作者： Campones, Pedro Domingos, Henrique NOVA LINCS Lisbon Portugal NOVA FCT Lisbon Portugal

ISBN: (纸本)9798400702433

In the realm of distributed Ledgers (DLs), a solution to enhancing maximum peak throughput involves establishing multiple independent blockchains that evolve concurrently. However, an excess of parallel chains in comparison to the application's demands can result in increased latency in content delivery, squandered bandwidth, and heightened computational strain on network nodes. Our innovation, Blockmess, introduces a novel parallel chain DL architecture. this solution dynamically adjusts the number of active parallel chains to align with the application's throughput, effectively reducing latency for end-users and seamlessly accommodating shifting application needs. We have implemented Blockmess in a practical cryptocurrency application, validating its efficacy and deploying it in a large scale distributed environment. Notably, Blockmess builds upon and refines existing state-of-the-art scalability solutions, substantially mitigating latency degradation by intelligently adapting the number of active chains in response to the application's load.

关键词： Blockchain Scalability Elastic Consensus

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Routing Optimization Based on distributed Intelligent Network Softwarization for the Internet of things 24

Routing Optimization Based on Distributed Intelligent Networ...

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39th Annual ACM symposium on Applied computing (SAC)

作者： Zormati, Mohamed Ali Lakhlef, Hicham Ouni, Sofiane Univ Technol Compiegne UTC Sorbonne Univ Alliance Heudiasyc UMR 7253 Compiegne France Univ Carthage Natl Inst Appl Sci & Technol INSAT Tunis Tunisia

ISBN: (纸本)9798400702433

the Internet of things (IoT) establishes connectivity between billions of heterogeneous devices that provide a variety of essential everyday services. the IoT faces several challenges, including energy efficiency and scalability, that require consideration of enabling technologies such as network softwarization. this technology is an appropriate solution for IoT, leveraging Software Defined Networking (SDN) and Network Function Virtualization (NFV) as two main techniques, especially when combined with Machine Learning (ML). Although many efforts have been made to optimize routing in softwarized IoT, the existing solutions do not take advantage of distributed intelligence. In this paper, we propose to optimize routing in softwarized IoT networks using Federated Deep Reinforcement Learning (FDRL), where distributed network softwarization and intelligence (i.e., FDRL) join forces to improve routing in constrained IoT networks. Our proposal introduces the combination of two novelties (i.e., distributed controller design and intelligent routing) to meet the IoT requirements (mainly performance and energy efficiency). the simulation results confirm the effectiveness of our proposal compared to the conventional counterparts.

关键词： Internet of things (IoT) network softwarization Federated Deep Reinforcement Learning (FDRL) routing optimization

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An effective and efficient green federated learning method for one-layer neural networks 24

An effective and efficient green federated learning method f...

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39th Annual ACM symposium on Applied computing (SAC)

作者： Fontenla-Romero, Oscar Guijarro-Berdinas, Bertha Hernandez-Pereira, Elena Perez-Sanchez, Beatriz Univ A Coruna CITIC La Coruna Spain

ISBN: (纸本)9798400702433

We present a green Federated learning method (FL), based on a neural network without hidden layers, capable of generating a global collaborative model in a single training round, unlike traditional FL methods that require multiple rounds for convergence. Moreover, the method preserves data privacy by design, a crucial aspect of current data protection regulations. Experiments with large data sets and a large number of federated clients show that the model achieves competitive accuracy results compared to state-of-the-art machine learning models. Moreover, it performs equally well in identically and non-identically distributed scenarios.

关键词： Green AI Federated learning neural networks edge computing

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VAMP: Visual Analytics for Microservices Performance 24

VAMP: Visual Analytics for Microservices Performance

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39th Annual ACM symposium on Applied computing (SAC)

作者： Traini, Luca Leone, Jessica Stilo, Giovanni Di Marco, Antinisca Univ Aquila Laquila Italy

ISBN: (纸本)9798400702433

Analysis of microservices' performance is a considerably challenging task due to the multifaceted nature of these systems. Each request to a microservices system might raise several Remote Procedure Calls (RPCs) to services deployed on different servers and/or containers. Existing distributed tracing tools leverage swimlane visualizations as the primary means to support performance analysis of microservices. these visualizations are particularly effective when it is needed to investigate individual end-to-end requests' performance behaviors. Still, they are substantially limited when more complex analyses are required, as when understanding the system-wide performance trends is needed. To overcome this limitation, we introduce VAMP, an innovative visual analytics tool that enables, at once, the performance analysis of multiple end-to-end requests of a microservices system. VAMP was built around the idea that having a wide set of interactive visualizations facilitates the analyses of the recurrent characteristics of requests and their relation w.r.t. the end-to-end performance behavior. through an evaluation of 33 datasets from an established open-source microservices system, we demonstrate how VAMP aids in identifying RPC execution time deviations with significant impact on end-to-end performance. Additionally, we show that VAMP can support in pinpointing meaningful structural patterns in end-to-end requests and their relationship with microservice performance behaviors.

关键词： Microservices distributed Tracing Performance Analysis

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Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning 24

Training Heterogeneous Client Models using Knowledge Distill...

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39th Annual ACM symposium on Applied computing (SAC)

作者： Chadha, Mohak Khera, Pulkit Gu, Jianfeng Abboud, Osama Gerndt, Michael Tech Univ Munich Munich Germany Huawei Technol Dusseldorf Germany

ISBN: (纸本)9798400702433

Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Functionas-a-Service (FaaS) for FL, can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders. However, existing serverless FL systems implicitly assume a uniform global model architecture across all participating clients during training. this assumption fails to address fundamental challenges in practical FL due to the resource and statistical data heterogeneity among FL clients. To address these challenges and enable heterogeneous client models in serverless FL, we utilize Knowledge Distillation (KD) in this paper. Towards this, we propose novel optimized serverless workflows for two popular conventional federated KD techniques, i.e., FedMD and FedDF. We implement these workflows by introducing several extensions to an open-source serverless FL system called FedLess. Moreover, we comprehensively evaluate the two strategies on multiple datasets across varying levels of client data heterogeneity using heterogeneous client models with respect to accuracy, fine-grained training times, and costs. Results from our experiments demonstrate that serverless FedDF is more robust to extreme non-IID data distributions, is faster, and leads to lower costs than serverless FedMD. In addition, compared to the original implementation, our optimizations for particular steps in FedMD and FedDF lead to an average speedup of 3.5x and 1.76x across all datasets.

关键词： Federated Learning Serverless computing FaaS Deep Learning Scalability of learning algorithms Knowledge Distillation

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Unsealing the secrets of blockchain consensus: A systematic comparison of the formal security of proof-of-work and proof-of-stake 24

Unsealing the secrets of blockchain consensus: A systematic ...

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39th Annual ACM symposium on Applied computing (SAC)

作者： Alvarez, Ivan Abellan Gramlich, Vincent Sedlmeir, Johannes Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Esch Sur Alzette Luxembourg Fraunhofer FIT Branch Business & Informat Syst Engn St Augustin Germany

ISBN: (纸本)9798400702433

With the increasing adoption of decentralized information systems based on a variety of permissionless blockchain networks, the choice of consensus mechanism is at the core of many controversial discussions. Ethereum's recent transition from proof-of-work (PoW) to proof-of-stake (PoS)-based consensus has further fueled the debate on which mechanism is more favorable. While the aspects of energy consumption and degree of (de-)centralization are often emphasized in the public discourse, seminal research has also shed light on the formal security aspects of both approaches individually. However, related work has not yet comprehensively structured the knowledge about the security properties of PoW and PoS. Rather, it has focused on in-depth analyses of specific protocols or high-level comparative reviews covering a broad range of consensus mechanisms. To fill this gap and unravel the commonalities and discrepancies between the formal security properties of PoW- and PoS-based consensus, we conduct a systematic literature review over 26 research articles. Our findings indicate that PoW-based consensus with the longest chain rule provides the strongest formal security guarantees. Nonetheless, PoS can achieve similar guarantees when addressing its more pronounced tradeoff between safety and liveness through hybrid approaches.

关键词： distributed ledger technology dynamic availability finality liveness PoS PoW safety

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