11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Network State Estimation Using Smart Experts

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  • @INPROCEEDINGS{10.4108/icst.mobiquitous.2014.257949,
        author={Jong-Suk Ahn and Yalda Edalat and Katia Obraczka},
        title={Network State Estimation Using Smart Experts},
        proceedings={11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={ICST},
        proceedings_a={MOBIQUITOUS},
        year={2014},
        month={11},
        keywords={machine learning network state estimation expert framework smart experts},
        doi={10.4108/icst.mobiquitous.2014.257949}
    }
    
  • Jong-Suk Ahn
    Yalda Edalat
    Katia Obraczka
    Year: 2014
    Network State Estimation Using Smart Experts
    MOBIQUITOUS
    ICST
    DOI: 10.4108/icst.mobiquitous.2014.257949
Jong-Suk Ahn1,*, Yalda Edalat2, Katia Obraczka2
  • 1: Dongguk University
  • 2: University of Californai, Santa Cruz
*Contact email: jahn@dgu.edu

Abstract

Several core network protocols and applications adjust their operation dynamically based on current network conditions. TCP and IEEE 802.11 are notable examples, both of which periodically adapt the retransmission timeout and the contention window size depending on the average round trip time and the degree of collisions, respectively. Consequently, accurate network state estimation is critical to the performance of networks and their applications. In this paper, we present a novel mechanism to estimate "nearfuture" network state based on past network conditions. Smart Experts for Network State Estimation, or SENSE, uses a simple, yet effective algorithm combining a machine-learning method known as Fixed-Share Experts and Exponentially Weighted Moving Average (EWMA). SENSE introduces novel techniques that improve the performance of the basic Fixed-Share Experts framework by: (1) making SENSE's accuracy considerably less sensitive to the number of experts; and (2) making SENSE more responsive to network dynamics at different time scales, i.e., long- and medium-term fluctuations as well as short-lived variations. We evaluate SENSE using synthetic and real datasets. Our results show that it yields superior performance for all datasets we used in our experiments when compared to "pure" Fixed- Share Experts and EWMA. We confirm that the performance of EWMA is quite sensitive to its "smoothing" factor, which specifies how much weight will be placed on the "past" versus the "present" when predicting the "future". Another key advantage of SENSE is that, unlike Fixed-Share Experts, it needs no a-priori information about the dataset. In our experiments, SENSE yields up to 24% and 30% prediction accuracy improvement over the Fixed-Share algorithm and EWMA, respectively.