Service class mapping based on integer programming algorithm in the third party agent

Mali, Bozidar J.; Ninkovic, Nemanja M.; Stojanović, Mirjana D.; Savić, Gordana

doi:10.1109/telfor.2014.7034382

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…The local controllers obtain the QoS information (such as the delay, jitter, and PLR) from the data plane devices for all the service requests and the service classes on the E2E paths. The service requests and service classes are shown in Table 2 [36] and Table 3 [37]. The service request is a combination of the E2E delay, jitter, and PLR for an application.…”

Section: Optimal Policy Learning Mechanismmentioning

confidence: 99%

“…For example, for application 1 the service requests are different than from application 2 and so on. Several solutions [40][41][42] have been proposed by researchers for service class mapping to meet the E2E QoS requirements for the applications. Furthermore, the mapping of the service classes is a challenging task with respect to meeting the E2E service needs due to the local view of the network state information in the domains.…”

Section: Use Casementioning

confidence: 99%

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Management of Software-Defined Networking Powered by Artificial Intelligence

Ali¹,

Roh²

2022

Computer-Mediated Communication

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Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

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Section: Optimal Policy Learning Mechanismmentioning

confidence: 99%

Section: Use Casementioning

confidence: 99%

Management of Software-Defined Networking Powered by Artificial Intelligence

Ali¹,

Roh²

2022

Computer-Mediated Communication

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“…The 3PIP algorithm relies on the premise that there is a single objective that needs to be accomplished, while simultaneously adhering to multiple constraints [15]. Constraints refer to fulfillment of QoS requirements, whereas the objective function states that E2E interconnection cost for every class mapping obeying the constraints is minimal.…”

Section: Pip Service Class Mapping Algorithmmentioning

confidence: 99%

Service class mapping based on integer programming algorithm in the third party agent

Mali¹,

Ninkovic²,

Stojanović³

et al. 2015

Telfor

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Abstract-In this paper, we propose a novel algorithm for mapping of service classes among multiple Internet providers on an end-to-end (E2E) path. The third-party (3P) approach is assumed for E2E service negotiation, whereas the foundation for class mapping is laid on the integer programming mathematical model. The algorithm selects service classes in domains on the path so that requirements for E2E quality of service (QoS) are fulfilled. This selection is based on multiple constraints, referring to performance fulfillment and at the same time aiming to achieve minimal E2E interconnection cost through definition of a single objective function. Performance evaluation has clearly indicated benefits of the proposed algorithm in terms of QoS achievement and decreasing costs, as well as its suitability for services that require stringent QoS guarantees.

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“…Research work presented in [10], [11] and [12] reveals the advantages of the 3P-based class mapping with respect to fairness, flexibility of class selection and compliance with different QoS models. Conformance Matching Scheme (CMS) is a 3P-based algorithm for mapping of service classes on E2E path, starting from E2E QoS requirements [10].…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…A multi-constraint approach to service class mapping has been proposed and evaluated in [12]. The constraints involve fulfillment of QoS requirement and minimization of the overall interconnection costs.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Multi-Objective Third-party Approach for Service Class Mapping among Multiple Providers in the Internet

Ninkovic

Mali

Stojanović

et al. 2015

ElAEE

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The third party (3P) model has been recognized as a perspective approach for different interprovider quality of service (QoS) solutions. In this paper, we address 3P-based mapping of services classes among heterogeneous providers' networks, which constitute an end-to-end (E2E) path. We propose and investigate a novel, highly flexible mapping scheme, which enables fulfillment of E2E network performance objectives, whereas minimizing interconnection costs. Starting from E2E service requirements, the proposed scheme uses goal programming technique to select the most appropriate service class in each domain on the path. Results of the comparative analysis of the proposed scheme and the two existing 3P-based schemes have clearly demonstrated superiority of our proposal in terms of accuracy, flexibility, and capability to support deployment of various business objectives. IndexTerms-Goal programming, interprovider negotiation, third party, quality of service. I. INTRODUCTIONRecent trends in Internet development including cloud computing, mobility, content distribution, Internet of things, and the big data paradigm pose new architectural challenges for network interconnection in the future Internet [1]. Enhanced solutions for quality of service (QoS) provisioning are still needed, particularly regarding end-to-end (E2E) service negotiation, performance monitoring and measurements for inter-domain performance assessment. The basic bilateral approach [2], which assumes service negotiation only between the adjacent providers, fails to meet E2E requirements. On the other side, heterogeneity of the existing providers' networks, including different intradomain QoS architectures, complicates the problem of establishing QoS-enabled E2E paths.The third-party (3P) approach has been recognized as a promising solution for the interprovider QoS delivery [3]. It assumes that a trusted, authorized intermediary (3P agent) Manuscript

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Service class mapping based on integer programming algorithm in the third party agent

Cited by 8 publications

References 12 publications

Management of Software-Defined Networking Powered by Artificial Intelligence

Management of Software-Defined Networking Powered by Artificial Intelligence

Service class mapping based on integer programming algorithm in the third party agent

Multi-Objective Third-party Approach for Service Class Mapping among Multiple Providers in the Internet

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