Traffic engineering based on stochastic model predictive control for uncertain traffic change

Otoshi, Tatsuya; Ohsita, Yuichi; Murata, Masayuki; Takahashi, Yoshiko; Ishibashi, Keisuke; Shiomoto, Kohei; Hashimoto, Takuya

doi:10.1109/inm.2015.7140461

Cited by 9 publications

(12 citation statements)

References 12 publications

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“…The graph shows that proposed system is able to minimize the end-to-end delay to a larger extent as compared to existing studies of Otoshi et al [25] and Sahuquillo et al [27]. The reason behind this is the technique that is adopted for processing search request and control messages by the proposed system.…”

Section: A Comparative Analysis Of End-to-end Delaymentioning

confidence: 75%

“…The performance parameters like packet delivery ratio, end-to-end delay and processing time are considered to analyze the effectiveness of the proposed system. It is benchmarked with similar studies of Otoshi et al [25] and Sahuquillo et al [27]. Otoshi et al [25] who have presented a stochastic modeling with predictive analysis for identifying discrete states of traffic in distributed networking system.…”

Section: Resultsmentioning

confidence: 99%

“…It is benchmarked with similar studies of Otoshi et al [25] and Sahuquillo et al [27]. Otoshi et al [25] who have presented a stochastic modeling with predictive analysis for identifying discrete states of traffic in distributed networking system. This technique has used a predictive control scheme to minimize the possibilities of predictive error considering network constraints e.g number of hops, length of the hops etc.…”

Section: Resultsmentioning

confidence: 99%

“…The authors have used a mechanism that integrates injection throttle and segregation of congested traffic. We perform a minor modification to techniques introduced in [25] [27] in order to make a suitable testbed for carrying out the comparative analysis. The parameters considered for analysis are end-to-end delay, packet delivery ratio and processing time.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, end-to-end delay of the proposed work is reduced in the presence of mobility of the nodes which varies at every simulation track points. The problem explored in Otoshi et al [25] is a predictive scheme. Here, the stochastic processing is adapted to predict and identify the possible prediction error.…”

Section: A Comparative Analysis Of End-to-end Delaymentioning

confidence: 99%

See 4 more Smart Citations

Congestion Control using Cross layer and Stochastic Approach in Distributed Networks

Selvarani¹,

Vinodha²

2017

ijacsa

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Abstract-In recent past, the current Internet architecture has many challenges in supporting the magnificent network traffic. Among the various that affect the quality of communication in the massive architecture the challenge in maintaining congestion free flow of traffic is one of the major concerns. In this paper, we propose a novel technique to address this issue using cross layer paradigm based on stochastic approach with extended markovian model. The cross layer approach will bridge the physical layer, link layer, network layer and transport layer to control congestion. The resource provisioning operation will be carried out over link layer and the mechanism of exploring the congestion using stochastic approach will be implemented over the network layer. The Markov modeling is adopted to identify the best routes amidst of highly congested paths and it is carried out at the transport layer. An analytical research methodology will be adopted to prove that it is feasible to develop a technique that can identify the origination point of congestion and share the same with the entire network. It is found that this approach for congestion control is effective with respect to end to end delay, packet delivery ratio and processing time.

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Section: A Comparative Analysis Of End-to-end Delaymentioning

confidence: 75%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: A Comparative Analysis Of End-to-end Delaymentioning

confidence: 99%

See 3 more Smart Citations

Congestion Control using Cross layer and Stochastic Approach in Distributed Networks

Selvarani¹,

Vinodha²

2017

ijacsa

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CECT: computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers

et al. 2018

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The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: 1) the elapsing of a predefined time interval, or, 2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3x compared to ECMP while packet loss is decreased up to 2x.

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Traffic engineering cooperating with traffic monitoring for the case with incomplete information

Satake

Otoshi

Ohsita

et al. 2017

2017 27th International Telecommunication Networks and Applications Conference (ITNAC)

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Traffic engineering based on stochastic model predictive control for uncertain traffic change

Cited by 9 publications

References 12 publications

Congestion Control using Cross layer and Stochastic Approach in Distributed Networks

Congestion Control using Cross layer and Stochastic Approach in Distributed Networks

CECT: computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers

Traffic engineering cooperating with traffic monitoring for the case with incomplete information

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