Uncertainty-driven ensemble forecasting of QoS in Software Defined Networks

Kolomvatsos, Kostas; Anagnostopoulos, Christos; Marnerides, Angelos K.; Ni, Qiang; Hadjiefthymiades, Stathes; Pezaros, Dimitrios P.

doi:10.1109/iscc.2017.8024701

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…The justification given by the authors for the adoption of the learning algorithm in the context of resource allocation in computational grids is that Teaching-Learning Based Optimization is considered a light and efficient algorithm to find the global solution to optimization problems. Another work based on artificial intelligence is presented in [14]. The authors use an approach centered on estimating values for various data transmission parameters, such as latency and use of links.…”

Section: Related Workmentioning

confidence: 99%

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A Resource Allocation Model Driven through QoC for Distributed Systems

D'Amato¹,

Andrade²

2023

Ubiquitous and Pervasive Computing - New Trends and Opportunities

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The trend of fog computing has generated challenges to establish resource allocation provided by this type of environment, since, in fog environments, the computing resource setting occurs on demand and at the edge of the network. Thus, ensuring both environment performance and providing user satisfaction imposes a severe technical problem. Since distributed systems are context-aware systems, the quality of context design can be applied to manage customer service, which aims to improve QoS, and provides system performance, for a given context. So, in this chapter, we propose a model to obtain runtime improvement for individual users and improve the global system performance using the quality of context in fog computing environment. The contribution of this proposal is to provide a resource allocation model, and metrics, based on QoC to deal with different distributed computing scenarios, in order to coordinate and enhance the environmental performance and user satisfaction. Experimental results show that our model improves system performance and users’ satisfaction. For measuring workloads, estimates of users’ satisfaction were performed. The proposed model obtained average results between 80 and 100% of users’ satisfaction acceptance, and a standard deviation adherent to a flat surface for workloads with a large number of tasks.

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Section: Related Workmentioning

confidence: 99%

“…The authors use an approach centered on estimating values for various data transmission parameters, such as latency and use of links. The approach used in [14] is applied only to provide service guarantees, based on QoS prediction through fuzzy logic. Parameters, or formulations, for controlling overall performance are not specified.…”

Section: Related Workmentioning

confidence: 99%

A Resource Allocation Model Driven through QoC for Distributed Systems

D'Amato¹,

Andrade²

2023

Ubiquitous and Pervasive Computing - New Trends and Opportunities

View full text Add to dashboard Cite

show abstract

“…Given several algorithms, they firstly dig out the features to analyse the data respectively, and then we get the final results among them by counting the highest voting. Kolomvatsos et al [129] developed an ensemble forecasting method to provide QoS in SDN network with its own prediction rule. However, it is worse than the original machine learning algorithm.…”

Section: Ensemble Learningmentioning

confidence: 99%

A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge

Islam¹,

Hossain²,

AlMukhtar³

2022

Preprint

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Software Defined Network (SDN) is the next generation network that decouples the control plane from the data plane of forwarding devices by utilizing the OpenFlow protocol as a communication link between the data plane and the control plane. However, there are some security issues might be in actions on SDN that the attackers can take control over the SDN control plane. Thus, traffic measurement is a fundamental technique of protecting SDN against the high-security threats such as DDoS, heavy hitter, superspreader as well as live video calling, QoS control, high bandwidth requirement, resource management are also inevitable in SDN/Software Defined Cellular Network (SDCN). In such a scenario, we survey SDN traffic measurement solutions, in order to assess how these solutions can make a secured, efficient and robust SDN/SDCN architecture. In this paper, various types of SDN traffic measurement solutions have been categorized based on network applications behaviour. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and future scope of research, which will guide to design and develop more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical and widely deployed SDN/SDCN in future prospects. More in details, we list out kinds of practical machine learning (ML) approaches to analyze how we can make improvement in the traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will give benefit to get secured SDN/SDCN network in complementary ways.

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“…For describing the proposed T2FLS, we borrow the notation of our previous efforts (in other domains) presented in [26], [25]. T2FLS is adopted locally at every node at t by fusing the past e t observations and future e t realizations.…”

Section: B the Uncertainty Driven Modelmentioning

confidence: 99%

Data Synopses Management based on a Deep Learning Model

Fountas¹,

Kolomvatsos²,

Anagnostopoulos³

2020

Preprint

Self Cite

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Pervasive computing involves the placement of processing units and services close to end users to support intelligent applications that will facilitate their activities. With the advent of the Internet of Things (IoT) and the Edge Computing (EC), one can find room for placing services at various points in the interconnection of the aforementioned infrastructures. Of significant importance is the processing of the collected data to provide analytics and knowledge. Such a processing can be realized upon the EC nodes that exhibit increased computational capabilities compared to IoT devices. An ecosystem of intelligent nodes is created at the EC giving the opportunity to support cooperative models towards the provision of the desired analytics. Nodes become the hosts of geo-distributed datasets formulated by the IoT devices reports. Upon the datasets, a number of queries/tasks can be executed either locally or remotely. Queries/tasks can be offloaded for performance reasons to deliver the most appropriate response. However, an offloading action should be carefully designed being always aligned with the data present to the hosting node. In this paper, we present a model to support the cooperative aspect in the EC infrastructure. We argue on the delivery of data synopses distributed in the ecosystem of EC nodes making them capable to take offloading decisions fully aligned with data present at every peer. Nodes exchange their data synopses to inform their peers. We propose a scheme that detects the appropriate time to distribute the calculated synopsis trying to avoid the network overloading especially when synopses are frequently extracted due to the high rates at which IoT devices report data to EC nodes. Our approach involves a Deep Learning model for learning the distribution of calculated synopses and estimate future trends. Upon these trends, we are able to find the appropriate time to deliver synopses to peer nodes. We provide the description of the proposed mechanism and evaluate it based on real datasets. An extensive experimentation upon various scenarios reveals the pros and cons of the approach by giving numerical results.

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Uncertainty-driven ensemble forecasting of QoS in Software Defined Networks

Cited by 10 publications

References 20 publications

A Resource Allocation Model Driven through QoC for Distributed Systems

A Resource Allocation Model Driven through QoC for Distributed Systems

A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge

Data Synopses Management based on a Deep Learning Model

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