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IEEE International Conference on Fog and Edge Computing (ICFEC)

作者： Nicolò Bartelucci Paolo Bellavista Thomas Pusztai Andrea Morichetta Schahram Dustdar Department of Computer Science and Engineering University of Bologna Bologna Italy Distributed Systems Group Vienna University of Technology Vienna Austria

With the increasing complexity, requirements, and variability of cloud services, it is not always easy to find the right static/dynamic thresholds for the optimal configuration of low-level metrics for autoscaling resource management decisions. A Service Level Objective (SLO) is a high-level commitment to maintaining a specific state of a service in a given period, within a Service Level Agreement (SLA): the goal is to respect a given metric, like uptime or response time within given time or accuracy constraints. In this paper, we show the advantages and present the progress of an original SLO-aware autoscaler for the Polaris framework. In addition, the paper contributes to the literature in the field by proposing novel experimental results comparing the Polaris autoscaling performance, based on highlevel latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.

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HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets

HTPS: Heterogeneous Transferring Prediction System for Healt...

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Euromicro Conference on Parallel, distributed and Network-Based Processing

作者： Jia-Hao Syu Jerry Chun-Wei Lin Marcin Fojcik Rafał Cupek Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Medical internet of things leads to revolutionary improvements in medical services, also known as smart healthcare. With the big healthcare data, data mining and machine learning can assist wellness management and intelligent diagnosis, and achieve the P4-medicine. However, healthcare data has high sparsity and heterogeneity. In this paper, we propose a Heterogeneous Transferring Prediction System (HTPS). Feature engineering mechanism transforms the dataset into sparse and dense feature matrices, and autoencoders in the embedding networks not only embed features but also transfer knowledge from heterogeneous datasets. Experimental results show that the proposed HTPS outperforms the benchmark systems on various prediction tasks and datasets, and ablation studies present the effectiveness of each designed mechanism. Experimental results demonstrate the negative impact of heterogeneous data on benchmark systems and the high transferability of the proposed HTPS.

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HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets

arXiv

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arXiv 2023年

作者： Syu, Jia-Hao Fojcik, Marcin Lin, Jerry Chun-Wei Cupek, Rafal Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

关键词： Diagnosis

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Joint Data Transmission and Energy Harvesting for MISO Downlink Transmission Coordination in Wireless IoT Networks

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Sensors 2023年第8期23卷 3900页

作者： Liu, Jain-Shing Lin, Chun-Hung Hu, Yu-Chen Donta, Praveen Kumar Department of Computer Science and Information Engineering Providence University Taichung433 Taiwan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Department of Computer Science and Information Management Providence University Taichung433 Taiwan Research Unit of Distributed Systems TU Wien Vienna1040 Austria

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greed

关键词： Energy harvesting

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Peer Discovery in Tree-Structured P2P Overlay Networks by Means of Connected Dominating Sets

Peer Discovery in Tree-Structured P2P Overlay Networks by Me...

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Conference on Local computer Networks (LCN)

作者： Peter Detzner Jana Gö deke Steffen Bondorf Department for AI and Autonomous Systems Fraunhofer Institute for Material Flow and Logistics Dortmund Germany Faculty of Computer Science Distributed and Networked Systems Group Ruhr University Bochum Bochum Germany

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480024

A Peer-to-Peer (P2P) network consists of a large number of nodes, where each node may have different capabilities and properties. Finding peers with specific capabilities and properties is challenging. Thus, we propose a practical solution to the problem of peer discovery, which is finding peers in the network according to a specified query. We contribute a peer discovery for an m-ary tree-structured P2P network by utilizing a connected dominating set (CDS), a technique that is typically used in unstructured networks. Our approach of constructing the CDS requires no additional communication cost, while nodes can insert, update and remove data within ${\mathcal{O}}(1)$. Each node of the CDS – a dominating set node – maintains only a limited number of nodes. We confirm the properties of our proposed solution by using the ns-3 discrete-event simulator. This includes, besides the degree of decentralism of the peer discovery, also the heterogeneity of peers.

关键词： Overlay networks Upper bound Runtime Costs Peer-to-peer computing

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FileDAG: A Multi-Version Decentralized Storage Network Built on DAG-based Blockchain

arXiv

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arXiv 2022年

作者： Guo, Hechuan Xu, Minghui Zhang, Jiahao Liu, Chunchi Yu, Dongxiao Dustdar, Schahram Cheng, Xiuzhen The School of Computer and Science and Technology Shandong University China The Department of Computer Science The George Washington University United States The Research Division of Distributed Systems TU Wien Austria

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. First, deduplication can only be achieved at the directory-level. Missing file-level deduplication leads to unavoidable extra storage and bandwidth cost. Second, current DSNs realize file indexing by storing extra metadata while blockchain ledgers are not fully exploited. To overcome these problems, we propose FileDAG, a DSN built on DAG-based blockchain to support file-level deduplication in storing multi-versioned files. When updating files, we adopt an increment generation method to calculate and store only the increments instead of the entire updated files. Besides, we introduce a two-layer DAG-based blockchain ledger, by which FileDAG can provide flexible and storage-saving file indexing by directly using the blockchain database without incurring extra storage overhead. We implement FileDAG and evaluate its performance with extensive experiments. The results demonstrate that FileDAG outperforms the state-of-the-art industrial DSNs considering storage cost and latency. Copyright © 2022, The Authors. All rights reserved.

关键词： Blockchain

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EtherClue:Digital investigation of attacks on Ethereum smart contracts

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Blockchain(Research and Applications) 2021年第4期2卷 12-29页

作者： Simon Joseph Aquilina Fran Casino Mark Vella Joshua Ellul Constantinos Patsakis Department of Computer Science University of MaltaMsidaMSD 2080Malta Department of Informatics University of PiraeusPiraeus18534Greece Information Management Systems Institute Athena Research CenterMarousi15125Greece Centre for Distributed Ledger Technologies University of MaltaMsidaMSD 2080Malta

Programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen *** vulnerability detectors can prevent vulnerable contracts from being deployed,this does not mean that such contracts will not be *** a vulnerable contract is instantiated on the blockchain and becomes the target of attacks,the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were *** this work,we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise(IoC)specially crafted for use in the *** definitions need to capture the side-effects of successful exploitation in the context of the Ethereum ***,we define a model for smart contract execution,comprising multiple abstraction levels that mirror the multiple views of code execution on a ***,we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue,a prototype tool for investigating Ethereum security *** results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less ***,they are contract-specific and suffer from false *** the other hand,fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation,which is nevertheless applicable for practical use.

关键词： Blockchain forensics Ethereum attacks Indicators of compromise Ethereum operational semantics

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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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A New Intelligent Scheduler to Improve Reactive OpenFlow Communication in SDN-Based IoT Data Streams

SSRN

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SSRN 2023年

作者： Batista, Ernando Alencar, Brenno M. Silva, Eliabe Canário, João Paulo Rios, Ricardo A. Dustdar, Schahram Figueiredo, Gustavo Bittencourt Prazeres, Cássio Federal Institute of Bahia Brazil Federal University of Bahia Brazil TU Wien University Distributed Systems Group Vienna Austria Stone Co. Bahia Salvador Brazil Department of Computer Science Brazil

The significant adoption of the Internet of Things (IoT) has increased the challenges in providing adequate IoT infrastructures meeting essential requirements, such as dynamicity networks and low latency. In this context, the Software-Defined Networking (SDN) paradigm and the OpenFlow protocol provide new possibilities for IoT networks. Based on the global view of network elements enabled by the Controller, SDN allows the programmability and control of the infrastructure according to the actual demands of applications. The OpenFlow protocol defines the exchange of messages between controllers and switches, enabling communication and network control. OpenFlow implements three operation modes: proactive, reactive, and hybrid. Due to the dynamic characteristic of the IoT data stream, the reactive mode is mainly used and indicated for IoT environments. As the OpenFlow controller installs rules dynamically, there is no need to know the network's sources, destinations, and paths in advance. Although reactive mode introduces dynamicity, it can generate additional delay due to switch-controller communication. This delay increases the response time of the IoT data stream. We propose an SDN-IoT scheduler based on Deep Neural Networks (DNN) to predict the time between data stream changes from IoT devices and install rules in advance, suppressing the existing delay in reactive mode. The proposal automatically uses previous data from the IoT data stream to calculate the time of the following communication from IoT devices. Our results indicate that predicting IoT data stream changes and installing OpenFlow rules in advance reduced about 51% of communications response time. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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