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Towards to Characterization of Network Management Traffic in OpenStack-Based Clouds

SN Computer Science 2023年第5期4卷 450页

作者： Donatti, Adnei W. Miers, Charles C. Koslovski, Guilherme P. Pillon, Maurício A. Carvalho, Tereza C. M. B. Graduate Program in Electrical Engineering Escola Politécnica da Universidade de São Paulo São Paulo 05508-010 Brazil Graduate Program in Applied Computing Santa Catarina State University Joinville 89219-710 Brazil

OpenStack is versatile and popular, allowing full customization for creating private or public IaaS clouds. This work addresses a network traffic analysis and characterization for the management domain inside OpenStack clouds. We conduct an induced lifecycle to execute virtual machine (VM)-related tasks, measure the traffic, and identify the services behind the traffic generated by these operations. Also, we analyze the impact of different images of operating systems (OSs) and VM’s flavor in the measured traffic. Moreover, we observe that predicting the volume of network traffic from operations, such as creation and shelving instances of VMs, helps estimate bandwidth boundaries, avoiding bottlenecks, for example. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Characterization Network traffic OpenStack Virtual machine VM

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Load Balancing to Optimize MPEG-DASH Video Streaming

Load Balancing to Optimize MPEG-DASH Video Streaming

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International Conference on Information Networking

作者： Edenilson Jônatas Dos Passos Adriano Fiorese Department of Computer Science (DCC) Graduate Program in Apllied Computing (PPGCAP) Santa Catarina State University (UDESC) Joinville Brazil

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Recent advancements in on-demand multimedia streaming have revealed the potential of this business model, driven by the convenience of accessing content anytime and anywhere. However, delivering a seamless user experience remains challenging due to the demanding nature of audiovisual streams, despite significant resource allocation. A promising solution involves using load balancers to distribute workloads evenly among processing units. This study proposes optimizing the processing and transmission resources by means of Software-Defined Networks (SDN). By continuously monitoring performance metrics like CPU usage and throughput, the SDN network controller determines the best server for handling new connections, even allowing seamless server migration during playback. The results show up to 80% faster response times and more consistent video quality, with reduced latency and uninterrupted playback. The approach also confirms the feasibility of SDN in streaming services, providing a foundation for future network improvements.

关键词： Transform coding Streaming media Load management User experience Quality assessment Servers Time factors Software defined networking Video recording Monitoring

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Joint Cache Placement and Request Routing Optimization in Heterogeneous Cellular Networks

Joint Cache Placement and Request Routing Optimization in He...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Marisangila Alves Guilherme Piĉgas Koslovski Graduate Program in Applied Computing - Santa Catarina State University - UDESC

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

The 5G Quality-of-Service (QoS) objectives con-tributed to the Heterogeneous Cellular Network (HCN) evolution, dictating that applications can rely on low-latency and high-bandwidth networks. However, concurrent requests of large amount of multimedia data generate a burden on the backhaul and fronthaul networks due to redundant retransmissions and pose challenges for achieving the QoS objectives. Although mobile network operators can place content closer to the HCN edge to improve the overall QoS indicators, there are still challenges to design a cache policy aware of limited storage capacity, different content popularity, device mobility, and network congestion. This work innovates by introducing a cooperative policy to join caches placement and routing users' requests atop an HCN. By combining networking and cache QoS requirements, the policy balances the fronthaul network load and dynamically maps the caches to HCN resources. We formulated the cache policy through linear programming and in-depth evaluated its performance using extensive simulation scenarios. The results indicate that the proposed network-aware policy decreases the network latency, even when subject to changes in content popularity distribution and total HCN storage capacity.

关键词： Cellular networks Measurement 5G mobile communication Quality of service Routing Linear programming Low latency communication

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Multi-Instance Task in Swarm Robotics: Sorting Groups of Robots or Objects into Clusters with Minimalist Controllers

Multi-Instance Task in Swarm Robotics: Sorting Groups of Rob...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Adilson Krischanski Yuri K. Lopes Andre B. Leal Ricardo F. Martins Roberto S.U. Rosso Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil Graduate Program in Applied Computing (PPGCAP) UDESC Brazil Department of Electrical Engineering Santa Catarina State University (UDESC) Joinville Brazil

Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots' differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.

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Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Aggregated Problem Information

Protein Structure Prediction Using Dynamic Speciation Evolut...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Rafael Stubs Parpinelli Nicholas Wojeicchowski Nilcimar Neitzel Will Graduate Program in Applied Computing State University of Santa Catarina Joinville Brazil Computer Science Department State University of Santa Catarina Joinville Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact maps can significantly enhance the exploration of the search space. In this study, an evolutionary algorithm is introduced, which incorporates such problem information for protein structure prediction. The proposed method employs a dynamic speciation technique alongside fragment insertion to foster population diversity. To ensure a rich variety of fragments, a fragment library is constructed using the Rosetta Quota protocol. Additionally, information from contact maps and secondary structure is integrated into two selection strategies to facilitate a more thorough exploration of the conformational search space. The results of an experimental evaluation involving 9 proteins are presented, demonstrating competitive performance compared to existing literature. Evaluation metrics include RMSD, GDT, and processing time.

关键词： Proteins Measurement Visualization Protocols Heuristic algorithms Evolutionary computation Search problems Libraries Space exploration Bioinformatics

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An IoT Transport Architecture for Passenger Counting: A Real Implementation 17

An IoT Transport Architecture for Passenger Counting: A Real...

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17th IFIP/IEEE International Symposium on Integrated Network Management, IM 2021

作者： Kniess, Janine Rutke, Julio Cezar Castaneda, William Alberto Cruz Santa Catarina State University Graduate Program in Applied Computing Brazil

ISBN: (纸本)9783903176324

A smart urban mobility considers the technological-based development in transport systems, services and their use to improve the movement of people and goods inter or intra-cities. Cities increasingly face problems caused by traffic and need to find solutions to improve mobility and to reduce congestion and pollution. In this paper, we present a Passenger Counting Scheme into buses. Embedded counting algorithms based on Computer Vision are proposed to count passenger arrivals and departures in real time. Experiments in a real scenario expose that the proposed scheme achieved a high percentage of success in passenger count. © 2021 IFIP.

关键词： Smart city

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Lightweight SPIFFE Verifiable Identity Document (LSVID): A Nested Token Approach for Enhanced Security and Flexibility in SPIFFE

Lightweight SPIFFE Verifiable Identity Document (LSVID): A N...

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Henrique Z. Cochak Charles C. Miers Pedro H.B. Correia Marco A. Marques Marcos A. Simplicio Graduate Program in Applied Computing (PPGCAP) Santa Catarina State University (UDESC) Brazil Laboratory of Computer Networks and Architecture (LARC) Universidade de Sao Paulo (USP) Brazil

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

Service identities are crucial for authentication and access control, ensuring that only authorized services access specific resources. The SPIFFE framework addresses workload identity management and authentication effectively but needs support for solutions (e.g., extensible tokens) that fine-granular authorization mechanisms in distributed scenarios can use. In this context, we present the Lightweight SVID (LSVID), an identity document in JSON format that can be extended and used as a token. As an extensible token, Lightweight SVID (LSVID) enables features such as delegation, attenuation, and traceability, enhancing its flexibility and applicability. Our approach provides efficient handling of token extensions and validations, demonstrated through a proof-of-concept implemented in Go. Baseline results indicate that LSVID critical operations are efficient, with processing times in the microsecond range, offering significant functional advantages over the traditional JWT-SVIDs, one of two key security documents from SPIFFE.

关键词： Computers Authorization Cloud computing Authentication Attenuation

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Data classification and reinforcement learning to avoid congestion on SDN-based data centers

Data classification and reinforcement learning to avoid cong...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Gustavo Diel Charles Christian Miers Maurício Aronne Pillon Guilherme Piêgas Koslovski Graduate Program in Applied Computing (PPGCAP) Santa Catarina State University (UDESC) Joinville Brazil

ISBN: (纸本)9781665435413

A contemporaneous data center (DC) hosts multiple competitive network data flows from different applications, sharing the intermediate switches capacities. In this context, congestion control and avoidance on Transmission Control Protocol (TCP) are critical tasks to ensure the quality of service for hosted applications. Specifically, Software Defined Networking (SDN) created an opportunity to avoid congestion once the centralized controller can gather ongoing and historical information from all network switches and flows. However, the data gathered is enormous, and fast-computing algorithms are crucial for decision-making. In this sense, this work proposes Reinforcement Learning- and SDN-aided Congestion Avoidance Tool (RSCAT), which uses data classification to determine if the network is congested and actor-critic reinforcement learning to find better TCP parameters. Our experimental analysis shows RSCAT could decrease the Flow Completion Time (FCT) of DCTCP and CUBIC variants in several cases without requiring any software update on DC end-points.

关键词： Data centers Protocols Software algorithms Decision making Reinforcement learning Quality of service Software

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Using synthetic data to develop machine learning models to predict the performance of fiber-reinforced concrete

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Asian Journal of Civil Engineering 2025年第5期26卷 2209-2221页

作者： Silva, Victor Hugo Peres Pessoa, Carolina Luiza Emereciana Sousa, Derica dos Santos Stefani, Ricardo Graduate Program in Materials Science Federal University of Mato Grosso Campus Araguaia MT Cuiaba 78605-091 Brazil Center for Agricultural and Veterinary Sciences University for the Development of Santa Catarina State (UDESC) Campus Lages SC Florianópolis 88520-000 Brazil Departament of Chemistry Federal University of Ouro Preto MG Ouro Preto 35402-136 Brazil

Concrete is a widely used construction material owing to its high compressive strength. However, its durability is often compromised by the development of cracks caused by tensile stress within the structures. These cracks can occur during the drying process, leading to water infiltration and corrosion of the concrete reinforcement, which subsequently require repair. Consequently, innovative technologies, such as self-repairing concrete and crack control, have become crucial for reducing the costs associated with structural repairs. In this context, this study investigated novel crack-control technologies in concrete structures using a machine-learning model that can accurately predict the performance of a specific fiber in fiber-reinforced concrete using a comprehensive dataset. The dataset was compiled from 18 studies and further augmented using synthetic data-generation techniques. It encompasses 13 different fiber types and 1953 fiber-reinforced concrete formulations. The computational model was implemented in Python, and multiple linear regression (MLR), support vector regression (SVR), random forest, and gradient boosting techniques were employed to develop the prediction model. The results showed that the Random Forest (R² = 0.887 and RMSE = 0.110), gradient boosting (R² = 0.868 and RMSE = 0.368), and SVR models (R² = 0.856 and RMSE = 0.376) outperformed their MLR counterparts (R² = 0.587 and RMSE = 0.637). Moreover, the Random Forest method showed a lower RMSE, making it more suitable for accurately predicting the performance of fiber-reinforced concrete. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Crack control Fiber-reinforced concrete Machine learning

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Modelling IF Steels Using Artificial Neural Networks and Automated Machine Learning 20th

Modelling IF Steels Using Artificial Neural Networks and Aut...

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20th International Conference on Hybrid Intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Sgrott, Douglas Macedo Cerqueira, Fabricio Moreira Miranda, Fabiano J. F. Filho, José F. S. Parpinelli, Rafael S. UDESC Graduate Program in Applied Computing Santa Catarina State University JoinvilleSC Brazil Global Research and Development Group ArcelorMittal São Francisco do SulSC Brazil

ISBN: (纸本)9783030730499

The development of steels requires multiples manufacturing steps and involves a high number of variables and non-linear processes. Although there are data-driven models used to predict such steels’ mechanical properties, their design often requires specialized Machine Learning knowledge, hampering the application of such models. In this paper, an Automated Machine Learning (AutoML) framework, namely Auto-Keras, was employed to generate and train a data-driven model on a steel data-set through Neural Architecture Search. This paper proposes predicting the mechanical properties of steels applying Artificial Neural Networks (ANN) as a multivariate regression problem using AutoML. The results show that even with a vast parameter search space, it is possible to generate ANNs with different architectures that provide high performance with free and open-source AutoML. Three ANNs with different activation functions were optimized using Auto-Keras and compared. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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