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Dynamic Classifier Selection (DCS) techniques aim to select the most competent classifiers from an ensemble per test sample. For each test sample, only a subset of the most competent classifiers is used to estimate its target value. The performance of the DCS highly depends on how we define the local region of competence, which is a local region in the feature space around the test sample. In this paper, we propose a new definition of region of competence based on a new proximity measure. We exploit the observed similarities between traffic profiles at different links, days and hours to obtain similarities between different values. Furthermore, long-term traffic pattern prediction is a complex problem and most of the traffic prediction literature are based on time-series and regression approaches and their prediction time is limited to next few hours or days. We tackle the long-term traffic pattern prediction as a classification of discretized traffic indicators to improve the accuracy of urban traffic pattern forecasting of next weeks by using DCS. We also employ two different link clustering methods, for grouping traffic links. For each cluster, we train a dynamic classifier system for predicting the traffic variables (flow, speed and journey time). Our results on strategic road network data shows that the proposed method outperforms the existing ensemble and baseline models in long-term traffic prediction. [ABSTRACT FROM AUTHOR]

We herein propose a heuristic redundancy selection algorithm that combines resubmission, replication, and checkpointing redundancies to reduce the resiliency overhead in fault‐tolerant workflow scheduling. The appropriate combination of these redundancies for workflow tasks is obtained in two consecutive phases. First, to compute the replication vector (number of task replicas), we apportion the set of provisioned resources among concurrently executing tasks according to their needs. Subsequently, we obtain the optimal checkpointing interval for each task as a function of the number of replicas and characteristics of tasks and computational environment. We formulate the problem of obtaining the optimal checkpointing interval for replicated tasks in situations where checkpoint files can be exchanged among computational resources. The results of our simulation experiments, on both randomly generated workflow graphs and real‐world applications, demonstrated that both the proposed replication vector computation algorithm and the proposed checkpointing scheme reduced the resiliency overhead. [ABSTRACT FROM AUTHOR]

Most of the existing work in the area of task graph scheduling considers resources with fixed processing capacity. The algorithms in these works rely on an estimation of the execution times of tasks on different resources. However, in practice, due to fluctuations in performance of cloud resources, these algorithms have challenges in these environments. In this paper, we focus on the problem of fault-tolerant scheduling of task graphs in the presence of performance fluctuations of computational resources. With the aim of reducing the adverse impacts of both soft errors and resource performance degradations, we propose an opportunistic task replication scheme that uses idle durations of resources for replicating tasks. Unlike the previous works, the proposed algorithm does not rely on estimation of task execution times for finding idle resources. We introduce the notion of concurrency graphs and propose a graph theory-based algorithm for finding the number of idle resources during the execution of a set of tasks. The appropriate redundancy for each task is chosen with respect to the number of idle resources and the characteristics of the set of tasks that are being processed concurrently. Simulation experiments show that, in most situations, the proposed algorithm outperforms the previous algorithms in terms of average execution time and cost. [ABSTRACT FROM AUTHOR]

In this paper, we investigate some important aspects of a new formalism for modelling and verification of hybrid dynamic systems (HDS), which is called multisingular hybrid Petri nets (MSHPNs). This new hybrid formalism is aimed to bridge the gap between hybrid automata (HA) and hybrid Petri nets (HPNs) by equipping the HPN model with the capabilities of HA to control the execution and firing of timed transitions. Practically, MSHPNs can be considered as the counterpart with the same expressive power as multisingular hybrid automata (MSHA). In order to analyse MSHPN models, a speed-based partitioning technique has been introduced in which the variable space is partitioned based on the balance of continuous places. In this paper, we formalize the notions of conflicts and conflict resolution and the challenging issue of speed computation. Then, we focus on considering a translation from a bounded MSHPN to a multisingular hybrid automaton that preserves the behavioural semantics of the original MSHPN in terms of weak timed bisimulation. The translation algorithm uses the speed-based partitioning method and obtains a speed-based partitioning hybrid automaton for a given bounded MSHPN. Model checking a timed property for an MSHPN amounts to model checking its equivalent property on the obtained speed-based partitioning hybrid automaton, thus MSHPN models can be analysed using the existing tools. The advantages of the proposed method are twofold: (1) hybrid systems can be described more succinctly and therefore more readably as MSHPNs, and (2) one can use the existing tools (like HYTECH) to analyse MSHPN models. [ABSTRACT FROM AUTHOR]

Stochastic activity networks (SANs) are a well-known petri net-based formalism used for the performance and dependability modeling of a wide range of systems. On the other hand, the growing complexity of timed systems makes it imperative to apply formal analysis techniques in the early stages of the system's development. Finding a suitable framework for the modeling, evaluation, and verification of these systems is still a great challenge. In this paper, we introduce a new formalism named timed activity networks (TANs), which are based on the activity networks that are the nondeterministic settings of the SANs. The advantages of TANs are 2-fold: 1) allowing the construction of more compact petri net-based models of timed systems and 2) allowing the assignment of time intervals to timed activities where the activity completion rates are marking-dependent, which makes TANs a better model for timed systems. Thanks to the presence of the input/output gates, TANs are capable of describing a situation whose specification using petri net-based formalisms was not practical, due to the naivety of the enabling and firing rules in these models. In addition, a great benefit of TANs is the similarity of their primitives and notations to ordinary SANs, which allows us to easily obtaining these 2 models from each other. Accordingly, SANs can be used for performance, and dependability modeling and evaluation, while TANs can be used for the model checking of timed systems. In this paper, we present the definitions, semantics, and model checking techniques of the proposed formalism. In order to model check TAN models, a transformation procedure is given for translating TAN models into the equivalent linear hybrid automaton, which can then be used with the existing techniques and tools. [ABSTRACT FROM AUTHOR]