A tensor-based big data model for QoS improvement in software defined networks

Kuang, Liwei; Yang, Laurence T.; Wang, Xiaokang; Wang, Puming; Zhao, Yaliang

doi:10.1109/mnet.2016.7389828

Cited by 49 publications

(18 citation statements)

References 13 publications

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“…As already mentioned, a number of studies has introduced a range of TE mechanisms that looked at particular QoS features where experimentations were oriented on explicit application scenarios, e.g. [4], [5]. Overall, the list for QoSrelated mechanisms is quite long, however, the agglomeration of SDNs for improving QoS is comparably a quite recent trend in TE.…”

Section: Related Workmentioning

confidence: 99%

“…As reported in [4] there has been minimal work on exploiting the properties of SDNs in order to firstly confront the high volume of monitored network packets and secondly to enable 'Big Data' technologies and predictive analytics for improving the aspect of QoS. Hence, the blending of SDNs and 'Big Data' for enabling QoS-related intelligent mechanisms is considered as a quite new topic.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the work in [4] suggested tensor-based models for formalizing the networking functions of the data plane and for routing path recommendations that coexist with a transition tensor model in charge of traffic prediction and QoS provisioning. In parallel, with the aforementioned study, the work in [11] introduced a QoS provisioning scheme that was instrumented by a centralized controller that run traffic classification modules based on Support Vector Machine (SVM) [10].…”

Section: Related Workmentioning

confidence: 99%

“…In parallel, with the aforementioned study, the work in [11] introduced a QoS provisioning scheme that was instrumented by a centralized controller that run traffic classification modules based on Support Vector Machine (SVM) [10]. In general, our work is aligned with the latter two pieces of work in [4] and [11] w.r.t. the objectives and the generic merging of the benefits from SDNs and Big Data forecasting analytics for achieving quality of service provisioning.…”

Section: Related Workmentioning

confidence: 99%

“…Regardless of the benefits offered by SDN deployments, such as the clear separation of the control with the data forwarding plane, there is still a number of issues related with the actual instrumentation of TE and particularly quality-driven and proactive decision making due to the limitations on real-time network flow monitoring and processing capabilities residing in current SDN controllers and the OpenFlow protocol [1], [2], [4]. Hence, the composition of novel SDN-based quality of service management methods needs to greatly consider the incorporation of real-time 'Big Data' forecasting analytics to construct flexible, scalable and self-managing networks [4].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty-driven ensemble forecasting of QoS in Software Defined Networks

Kolomvatsos

Anagnostopoulos

Marnerides

et al. 2017

2017 IEEE Symposium on Computers and Communications (ISCC)

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Abstract-Software Defined Networking (SDN) is the key technology for combining networking and Cloud solutions to provide novel applications. SDN offers a number of advantages as the existing resources can be virtualized and orchestrated to provide new services to the end users. Such a technology should be accompanied by powerful mechanisms that ensure the endto-end quality of service at high levels, thus, enabling support for complex applications that satisfy end users needs. In this paper, we propose an intelligent mechanism that agglomerates the benefits of SDNs with real-time 'Big Data' forecasting analytics. The proposed mechanism, as part of the SDN controller, supports predictive intelligence by monitoring a set of network performance parameters, forecasting their future values, and deriving indications on potential service quality violations. By treating the performance measurements as time-series, our mechanism employs a novel ensemble forecasting methodology to estimate their future values. Such predictions are fed to a Type-2 Fuzzy Logic system to deliver, in real-time, decisions related to service quality violations. Such decisions proactively assist the SDN controller for providing the best possible orchestration of the virtualized resources. We evaluate the proposed mechanism w.r.t. precision and recall metrics over synthetic data.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Uncertainty-driven ensemble forecasting of QoS in Software Defined Networks

Kolomvatsos

Anagnostopoulos

Marnerides

et al. 2017

2017 IEEE Symposium on Computers and Communications (ISCC)

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Proactive multipath routing with a predictive mechanism in software‐defined networks

Lin

Liu

Wang

et al. 2019

Int J Communication

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With the growth of network traffic volume, link congestion cannot be avoided efficiently with conventional routing protocols. By utilizing the single shortestpath routing algorithm from link state advertisement information, standard routing protocols lack of global awareness and are difficult to be modified in a traditional network environment. Recently, software-defined network (SDN) provided innovative architecture for researchers to program their own network protocols. With SDN, we can divert heavy traffic to multiple paths in order to resolve link congestion. Furthermore, certain network traffics come in periodic fashion such as peak hours at working days so that we can leverage forecasting for resource management to improve its performance. In this paper, we propose a proactive multipath routing with a predictive mechanism (PMRP) to achieve high-performance congestion resolution. PMRP has two main concepts: (a) a proactive mechanism where PMRP deploys M/M/1 queue and traffic statistics to simulate weighted delay for possible combinations of multipaths placement of all subnet pairs, and leverage genetic algorithm for accelerating selection of optimized solution, and (b) a predictive mechanism whereby PMRP uses exponential smoothing for demand traffic volumes and variance predictions. Experimental results show a 49% reduction in average delay as compared with single shortest routing, and a 16% reduction in average delay compared with utilization & topology-aware multipath routing (UTAMP). With the predictive mechanism, PMRP can decrease an additional 20% average delay. Furthermore, PMRP reduces 93% of flow table usage on average as compared with UTAMP.links. Furthermore, periodically updating the link weights provided by link state advertisement is insensitive for network congestions. In addition to congestion resolution, there are no forecasting functions for resource management. The network pattern also has locality phenomenon such as daily rush hours so that we can leverage it for bandwidth allocation in routing mechanism. However, it is difficult to modify standard routing protocols in conventional distributed network architecture, and it is impossible to replace every individual commodity router to provide better congestion control algorithms.Recent work on software-defined network (SDN) architecture 1 separates the control plane from data plane and provides programmable Application Programming Interface (API) for provisioning and management. With a centralized controller, a global view of network topology with status can be obtained, and researchers could implement network protocols easily in the SDN environment. However, flow tables in SDN switches are limited resources, and care has to be taken with use of flow entries. If we design a routing management mechanism according to host pairs, the number of flow entries will increase dramatically and will rapidly exceed the limits of the hardware. One would consequently like to manage the flows based on subnet pairs for better flow table utilization. For exa...

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A Novel Approach for Better QoS in Cognitive Radio Ad Hoc Networks Using Cat Optimization

Singh

Dutta²

2019

Data Management, Analytics and Innovation

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A tensor-based big data model for QoS improvement in software defined networks

Cited by 49 publications

References 13 publications

Uncertainty-driven ensemble forecasting of QoS in Software Defined Networks

Uncertainty-driven ensemble forecasting of QoS in Software Defined Networks

Proactive multipath routing with a predictive mechanism in software‐defined networks

A Novel Approach for Better QoS in Cognitive Radio Ad Hoc Networks Using Cat Optimization

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