| 1. |
- Trung, Hieu Tran, et al.
(författare)
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Anti-inflammatory and Antiphytopathogenic Fungal Activity of 2,3-seco-Tirucallane Triterpenoids Meliadubins A and B from Melia dubia Cav. Barks with ChemGPS-NP and In Silico Prediction
- 2023
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Ingår i: ACS Omega. - : American Chemical Society (ACS). - 2470-1343. ; 8:40, s. 37116-37127
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Tidskriftsartikel (refereegranskat)abstract
- Two new rearranged 2,3-seco-tirucallane triterpenoids, meliadubins A (1) and B (2), along with four known compounds, 3-6, were isolated from the barks of Melia dubia Cav. Compound 2 exhibited a significant inflammatory inhibition effect toward superoxide anion generation in human neutrophils (EC50 at 5.54 +/- 0.36 mu M). It bound to active sites of a human inducible nitric oxide synthase (3E7G) through interactions with the residues of GLU377 and PRO350, which may benefit in reducing the neutrophilic inflammation effect. The ChemGPS-NP interpretation combined with bioactivity assay and in silico prediction results suggested 2 to be an agent for targeting iNOS with different mechanisms as compared to a selected set of current approved drugs. Moreover, compounds 1 and 2 showed remarkable inhibition against the rice pathogenic fungus Magnaporthe oryzae in a dose-dependent manner with IC50 values of 137.20 +/- 9.55 and 182.50 +/- 18.27 mu M, respectively. Both 1 and 2 displayed interactions with the residue of TYR223, a key active site of trihydroxynaphthalene reductase (1YBV). The interpretation of 1 and 2 in the ChemGPS-NP physical-chemical property space indicated that both compounds are quite different compared to all members of a selected set of reference compounds. In light of demonstrated biological activity and in silico prediction experiments, both compounds possibly exhibited activity against phytopathogenic fungi via a novel mode of action.
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| 2. |
- Le Duc, Thang, 1980-, et al.
(författare)
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Application, Workload, and Infrastructure Models for Virtualized Content Delivery Networks Deployed in Edge Computing Environments
- 2018
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Ingår i: 2018 27th International Conference on Computer Communication and Networks (ICCCN). - : IEEE. - 9781538651568 - 9781538651575
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Konferensbidrag (refereegranskat)abstract
- Content Delivery Networks (CDNs) are handling a large part of the traffic over the Internet and are of growing importance for management and operation of coming generations of data intensive applications. This paper addresses modeling and scaling of content-oriented applications, and presents workload, application, and infrastructure models developed in collaboration with a large-scale CDN operating infrastructure provider aimed to improve the performance of content delivery subsystems deployed in wide area networks. It has been shown that leveraging edge resources for the deployment of caches of content greatly benefits CDNs. Therefore, the models are described from an edge computing perspective and intended to be integrated in network topology aware application orchestration and resource management systems.
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| 3. |
- Le Duc, Thang, 1980-, et al.
(författare)
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Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing : A Survey
- 2019
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Ingår i: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 52:5
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale software systems are currently designed as distributed entities and deployed in cloud data centers. To overcome the limitations inherent to this type of deployment, applications are increasingly being supplemented with components instantiated closer to the edges of networks—a paradigm known as edge computing. The problem of how to efficiently orchestrate combined edge-cloud applications is, however, incompletely understood, and a wide range of techniques for resource and application management are currently in use.This article investigates the problem of reliable resource provisioning in joint edge-cloud environments, and surveys technologies, mechanisms, and methods that can be used to improve the reliability of distributed applications in diverse and heterogeneous network environments. Due to the complexity of the problem, special emphasis is placed on solutions to the characterization, management, and control of complex distributed applications using machine learning approaches. The survey is structured around a decomposition of the reliable resource provisioning problem into three categories of techniques: workload characterization and prediction, component placement and system consolidation, and application elasticity and remediation. Survey results are presented along with a problem-oriented discussion of the state-of-the-art. A summary of identified challenges and an outline of future research directions are presented to conclude the article.
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| 4. |
- Le Duc, Thang, 1980-, et al.
(författare)
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Workload Diffusion Modeling for Distributed Applications in Fog/Edge Computing Environments
- 2020
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Ingår i: ICPE '20. - New York : Association for Computing Machinery (ACM). - 9781450369916 ; , s. 218-229
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of workload generation for distributed applications in fog/edge computing. Unlike most existing work that tends to generate workload data for individual network nodes using historical data from the targeted node, this work aims to extrapolate supplementary workloads for entire application / infrastructure graphs through diffusion of measurements from limited subsets of nodes. A framework for workload generation is proposed, which defines five diffusion algorithms that use different techniques for data extrapolation and generation. Each algorithm takes into account different constraints and assumptions when executing its diffusion task, and individual algorithms are applicable for modeling different types of applications and infrastructure networks. Experiments are performed to demonstrate the approach and evaluate the performance of the algorithms under realistic workload settings, and results are validated using statistical techniques.
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| 5. |
- Nguyen, Van-Giang, 1989-, et al.
(författare)
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On Auto-scaling and Load Balancing for User-plane Functions in a Softwarized 5G Core
- 2021
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Ingår i: Proceedings of the 2021 17th International Conference on Network and Service Management: Smart Management for Future Networks and Services, CNSM 2021. - : IEEE. - 9783903176362 ; , s. 132-138
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Konferensbidrag (refereegranskat)abstract
- In the fifth generation (5G) mobile networks, the number of user plane functions has increased, and, in contrast to previous generations. They can be deployed in a decentralized way and auto-scaled independently from their control plane functions. Moreover, the performance of the user plane functions can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user plane functions has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user-plane functions. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load-balancing algorithms for the user plane of a VPP-based, softwarized 5G network. The novel performance indicator is estimated based on the VPP vector rate, and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user plane function and their VPP vector rate. We validated and evaluated our proposed solution in a 5G testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user plane traffic, and our proposed load-balancing algorithms seem to give a better distribution of traffic load as compared to traditional static algorithms.
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| 6. |
- Nguyen, Van-Giang, 1989-, et al.
(författare)
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On Auto-scaling and Load Balancing for User-plane Gateways in a Softwarized 5G Network
- 2021
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Ingår i: Proceedings of the 2021 17th International Conference on Network and Service Management. - : IEEE. - 9783903176362 ; , s. 132-138
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Konferensbidrag (refereegranskat)abstract
- In the fifth generation (SG) mobile networks, the number of user-plane gateways has increased, and, in contrast to previous generations they can be deployed in a decentralized way and auto-scaled independently from their control-plane functions. Moreover, the performance of the user-plane gateways can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user-plane gateways has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user- plane gateways. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load- balancing algorithms for the user plane of a VPP-based, softwarized SG network. The novel performance indicator is estimated based on the VPP vector rate and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user-plane gateway and their VPP vector rate. We validate and evaluate our proposed solution in a SG testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user-plane traffic, and that our proposed load-balancing algorithms can give a better distribution of traffic load as compared to traditional static algorithms.
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| 7. |
- Thang, Truong Cong, et al.
(författare)
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Design and implementation of an e-Health system for depression detection
- 2015
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Konferensbidrag (refereegranskat)abstract
- We present the design and implementation of a cost-effective e-Health system for automatic depression detection. The system is based on a client-server architecture, where clients are popular mobile devices. For practical deployment, various factors that affect the accuracy and speed of depression detection are discussed and evaluated with extensive experiments.
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| 8. |
- Östberg, Per-Olov, et al.
(författare)
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Application Optimisation : Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications
- 2020
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Ingår i: Managing Distributed Cloud Applications and Infrastructure. - Cham : Palgrave Macmillan. - 9783030398620 - 9783030398637 ; , s. 51-68
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Bokkapitel (refereegranskat)abstract
- Optimisation of (the configuration and deployment of) distributed cloud applications is a complex problem that requires understanding factors such as infrastructure and application topologies, workload arrival and propagation patterns, and the predictability and variations of user behaviour. This chapter outlines the RECAP approach to application optimisation and presents its framework for joint modelling of applications, workloads, and the propagation of workloads in applications and networks. The interaction of the models and algorithms developed is described and presented along with the tools that build on them. Contributions in modelling, characterisation, and autoscaling of applications, as well as prediction and generation of workloads, are presented and discussed in the context of optimisation of distributed cloud applications operating in complex heterogeneous resource environments.
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| 9. |
- Östberg, Per-Olov, et al.
(författare)
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Reliable Capacity Provisioning for Distributed Cloud/Edge/Fog Computing Applications
- 2017
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Ingår i: 2017 EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS (EUCNC). - : IEEE. - 9781538638736
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Konferensbidrag (refereegranskat)abstract
- The REliable CApacity Provisioning and enhanced remediation for distributed cloud applications (RECAP) project aims to advance cloud and edge computing technology, to develop mechanisms for reliable capacity provisioning, and to make application placement, infrastructure management, and capacity provisioning autonomous, predictable and optimized. This paper presents the RECAP vision for an integrated edge-cloud architecture, discusses the scientific foundation of the project, and outlines plans for toolsets for continuous data collection, application performance modeling, application and component auto-scaling and remediation, and deployment optimization. The paper also presents four use cases from complementing fields that will be used to showcase the advancements of RECAP.
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