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  • 1.
    Hosseini, Ahmad
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Wadbro, Eddie
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Connectivity reliability in uncertain networks with stability analysis2016Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 57, s. 337-344Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper treats the fundamental problems of reliability and stability analysis in uncertain networks. Here, we consider a collapsed, post-disaster, traffic network that is composed of nodes (centers) and arcs (links), where the uncertain operationality or reliability of links is evaluated by domain experts. To ensure the arrival of relief materials and rescue vehicles to the disaster areas in time, uncertainty theory, which neither requires any probability distribution nor fuzzy membership function, is employed to originally propose the problem of choosing the most reliable path (MRP). We then introduce the new problems of α-most reliable path (α-MRP), which aims to minimize the pessimistic risk value of a path under a given confidence level α, and very most reliable path (VMRP), where the objective is to maximize the confidence level of a path under a given threshold of pessimistic risk. Then, exploiting these concepts, we give the uncertainty distribution of the MRP in an uncertain traffic network. The objective of bothα-MRP and VMRP is to determine a path that comprises the least risky route for transportation from a designated source node to a designated sink node, but with different decision criteria. Furthermore, a methodology is proposed to tackle the stability analysis issue in the framework of uncertainty programming; specifically, we show how to compute the arcs’ tolerances. Finally, we provide illustrative examples that show how our approaches work in realistic situation.

  • 2.
    Lorido-Botran, Tania
    et al.
    University of Deusto.
    Huerta, Sergio
    University of Deusto.
    Tomás, Luis
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tordsson, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Sanz, Borja
    University of Deusto.
    An unsupervised approach to online noisy-neighbor detection in cloud data centers2017Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 89, s. 188-204Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Resource sharing is an inherent characteristic of cloud data centers. Virtual Machines (VMs) and/or Containers that are co-located in the same physical server often compete for resources leading to interference. The noisy neighbor’s effect refers to an anomaly caused by a VM/container limiting resources accessed by another one. Our main contribution is an online, lightweight and application-agnostic solution for anomaly detection, that follows an unsupervised approach. It is based on comparing models for different lags: Dirichlet Process Gaussian Mixture Models to characterize the resource usage profile of the application, and distance measures to score the similarity among models. An alarm is raised when there is an abrupt change in short-term lag (i.e. high distance score for short-term models), while the long-term state remains constant. We test the algorithm for different cloud workloads: websites, periodic batch applications, Spark-based applications, and Memcached server. We are able to detect anomalies in the CPU and memory resource usage with up to 82–96% accuracy (recall) depending on the scenario. Compared to other baseline methods, our approach is able to detect anomalies successfully, while raising low number of false positives, even in the case of applications with unusual normal behavior (e.g. periodic). Experiments show that our proposed algorithm is a lightweight and effective solution to detect noisy neighbor effect without any historical info about the application, that could also be potentially applied to other kind of anomalies.

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