OASM: An overload‐aware workload scheduling method for cloud computing based on biogeographical optimization

With the daily increase in the number of cloud users and the volume of submitted workloads, load balancing (LB) over clouds followed by a reduction in users' response time is emerging as a vital issue. To successfully address the LB problem, we have optimized workload distribution among virtual machines (VMs). This approach consists of two parts: Firstly, a meta‐heuristic method based on biogeographical optimization for workload dispatching among VMs is introduced; secondly, we propose an innovative heuristic algorithm inspired by the “Banker algorithm” that runs in core scheduler to control and avoid VM overloads. The combination of these two (meta‐)heuristic algorithms constitutes an LB approach through which we have been able to reduce the value of the makespan to a reasonable time frame. Moreover, an information base repository (IBR) is introduced to maintain the online processing status of physical machines (PMs) and VMs. In our approach, data stored in IBR are retrieved when needed. This approach is compared with well‐known (non‐)evolutionary approaches, such as round‐robin, max‐min, MGGS, and TBSLB‐PSO. Experimental results reveal that our proposed approach outperforms its counterparts in a heterogeneous environment when the resources are smaller than the workloads. Moreover, the utilization of physical resources gradually increases. Therefore, optimal workload scheduling, as well as the lack of overload occurrence, results in a reduction in makespan.     

A genetic algorithm‐based flow update scheduler for software‐defined networks

Software‐defined networking (SDN) facilitates network programmability through a central controller. It dynamically modifies the network configuration to adapt to the changes in the network. In SDN, the controller updates the network configuration through flow updates, ie, installing the flow rules in network devices. However, during the network update, improper scheduling of flow updates can lead to a number of problems including overflowing of the switch flow table memory and the link bandwidth. Another challenge is minimizing the network update completion time during large‐network updates triggered by events such as traffic engineering path updates. The existing centralized approaches do not search the solution space for flow update schedules with optimal completion time. We proposed a hybrid genetic algorithm‐based flow update scheduling method (the GA‐Flow Scheduler). By searching the solution space, the GA‐Flow Scheduler attempts to minimize the completion time of the network update without overflowing the flow table memory of the switches and the link bandwidth. It can be used in combination with other existing flow scheduling methods to improve the network performance and reduce the flow update completion time. In this paper, the GA‐Flow Scheduler is combined with a stand‐alone method called the three‐step method. Through large‐scale experiments, we show that the proposed hybrid approach could reduce the network update time and packet loss. It is concluded that the proposed GA‐Flow Scheduler provides improved performance over the stand‐alone three‐step method. Also, it handles the above‐mentioned network update problems in SDN.     

Multiaspect‐based opinion classification model for tourist reviews

Tourist reviews on social media websites reflect the tourist's opinions concerning various aspects of a tourist place or service (e.g., “comfortable room” and “terrible service” in hotel reviews). Extracting these aspects from reviews is a challenging task in opinion mining. Therefore, aspect‐based opinion mining has emerged as a new area of social review mining. Existing approaches in this area focus on extracting explicit aspects and classification of opinions around these aspects. However, the implicit and coreferential aspects during aspect extraction are often neglected, and the classification of multiaspect opinions is relatively less emphasized in prior art. In this paper, we propose a model, namely, “enhanced multiaspect‐based opinion classification” that addresses existing challenges by automatically extracting both explicit and implicit aspects and classifying the multiaspect opinions. In this model, first, a probabilistic co‐occurrence‐based method is proposed that utilizes the co‐occurrence between aspects and sentiment words to identify the coreferential aspects and merge them into groups. Second, an implicit aspect extraction method is proposed that associates the sentiment words with suitable aspects to build an aspect‐sentiment hierarchy. Third, a multiaspect opinion classification approach is proposed that employs multilabel classification algorithms to classify opinions into different polarity classes. The effectiveness of the proposed model is evaluated by conducting experiments on benchmark and real‐world datasets. The experimental results revealed the supremacy of multilabel classifiers by achieving 90% accuracy per label on classification when extracting 87% domain‐relevant aspects. A state‐of‐the‐art performance comparison is conducted that also verifies the advantages of the proposed model.     

An enhanced two‐phase SVM algorithm for cooperative spectrum sensing in cognitive radio networks

Cognitive radio (CR) networks have emerged recently to address the problem of spectrum scarcity. As reliable spectrum sensing (SS) is vital in low signal‐to‐noise ratio (SNR) for CR networks, we propose a novel method of enhancing support vector machines (SVM) classifier named as 2‐Phase SVM for the task of SS in a cooperative sensing structure. In this study, the vectors containing energy levels of primary users (PU) are considered as feature vectors and are fed into the classifier during training and test phase. First, the classifier is trained; afterward, the test feature vectors are labeled as channel available class or channel unavailable class in an online fashion by using 2‐Phase SVM, which is applied during two phases compared with the conventional SVM algorithm. The performance of suggested cooperative SS method is evaluated by receiver operating characteristic (ROC) curve and the functionality of our proposed algorithm is qualified in terms of misclassification error rate in addition to misclassification risk. The results reveal that 2‐Phase SVM outperforms previous methods since it not only increases the classification accuracy and reduces the misclassification risk but also enhances the detection probability.     

Thermal radiation effects on flow of Jeffery fluid in converging and diverging stretchable channels

Neural Computing and Applications - This paper investigates the flow of Jeffery fluid between converging and diverging channels. The walls of the channel are assumed to be capable for stretching or...     

Effect of spinning speed on the structure and physical properties of filament yarns produced from used PET bottles

In the previous work (S. Kiantash, MS Thesis, Amirkabir University of Technology, Textile Engineering Department, Tehran, Iran, 2002), the possibility of producing filament yarns from used PET bottles was investigated and the production was successfully carried out. To improve physical properties and to have a detailed understanding of the molecular structure, spinning variables such as the take‐up speed (one of the most influencing factors) should be varied and studied. In the present work, continuous filament yarns from virgin PET chips and used PET bottles were produced at the two take‐up speeds of 2500 and 3000 m/min. Optical birefringence, crystallinity (obtained from three methods including density, calorimetry, and FTIR), tenacity, breaking elongation, initial modulus, and shrinkage of yarns were measured and compared. Optical birefringence and crystallinity (obtained from all three methods) of used samples show higher values compared with those of virgin samples produced at both take‐up speeds. Consequently, the tenacity of used samples is higher and breaking elongation is lower. Generally, samples having bigger crystallinity present higher initial modulus and smaller shrinkage. However, results of initial modulus and shrinkage do not correspond to this assumption. As it was predicted, increasing the take‐up speed resulted in an increase in the optical birefringence, crystallinity, tenacity, and initial modulus and a reduction in the breaking elongation of both virgin and used samples. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 3972–3975, 2007     

Formability Enhancement of Galvanized IF-Steel TWB by Modification of Forming Parameters

Tailor-welded blanks (TWBs) have numerous advantages over traditional blanks used in manufacturing, such as energy conservation and environment protection. Low formability and weld line movement during forming operation are main limitations of these blanks. In this research, the effects of forming parameters including thickness ratio (TR), rolling direction with respect to the weld line and direction of major stress with respect to the weld line, on formability and weld line movement of TWBs made of galvanized Interstitial-Free (IF) steel were investigated experimentally. Also the effect of application of non-uniform blankholder force on weld line movement was studied by FEM simulation. By utilization of ABAQUS software, blankholders with different geometries, namely one-piece and two-pieces were modeled and forming process was simulated. The results revealed that formability maximized when the major stress and rolling direction were along the weld line. The results showed applying different blankholder forces, by application of the two-pieces blankholder, leads to more uniform strain distribution and correspondingly less weld line movement in TWBs with TR greater than 1. It was also concluded that the effect of geometric discontinuities on reducing formability was greater than the effect of the weld region.     

Movement-Assisted Connectivity Restoration in Wireless Sensor and Actor Networks

Recent years have witnessed a growing interest in applications of wireless sensor and actor networks (WSANs). In these applications, a set of mobile actor nodes are deployed in addition to sensors in order to collect sensors' data and perform specific tasks in response to detected events/objects. In most scenarios, actors have to respond collectively, which requires interactor coordination. Therefore, maintaining a connected interactor network is critical to the effectiveness of WSANs. However, WSANs often operate unattended in harsh environments where actors can easily fail or get damaged. An actor failure may lead to partitioning the interactor network and thus hinder the fulfillment of the application requirements. In this paper, we present DARA, a Distributed Actor Recovery Algorithm, which opts to efficiently restore the connectivity of the interactor network that has been affected by the failure of an actor. Two variants of the algorithm are developed to address 1- and 2-connectivity requirements. The idea is to identify the least set of actors that should be repositioned in order to reestablish a particular level of connectivity. DARA strives to localize the scope of the recovery process and minimize the movement overhead imposed on the involved actors. The effectiveness of DARA is validated through simulation experiments.     

Multivariate nonnormal process capability analysis

Precise modelling of servo-pneumatic actuation plants continues to remain a major challenge. In this work, the complete mathematical model of a double-acting pneumatic cylinder with cushioning sections at both ends of the piston stroke has been developed. The proposed model considers the entire stroke of the piston, including cushioning sections as these sections affect the positioning accuracy of the piston. A pulse width modulation algorithm has been applied to control the position of the pneumatic cylinder. Computer simulation and experimental tests have been carried out on the developed model. The results of simulation and experimental studies have been shown to yield a better performance compared with existing mathematical models.     

TORAP — a new tool for conducting rapid risk-assessments in petroleum refineries and petrochemical industries

The use of a new computer-automated tool TORAP (TOol for Rapid risk Assessment in Petroleum refinery and Petrochemical industries) is demonstrated through a rapid and quantitative risk assessment of a typical petroleum refinery. The package has been applied for an appraisal of the risks of accidents (fires, explosions and toxic release) posed by different units of the refinery, and to identify steps to prevent/manage accidents. The studies reveal that TORAP enables a user to quickly focus on the accidents likely to occur, and enables forecasting the nature and impacts of such accidents. This information is directly utilizable in identifying 'soft' spots and in taking appropriate remedial measures to prevent or control accidents. The special attributes of TORAP are: (a) wide range of applications —  achieved by incorporating models capable of handling all types of industrial fires and explosions, (b) sophistication — brought about by including state-of-the-art models developed by these authors and others, (c) user-friendliness —  achieved by incorporating on-line help, graphics, carefully formatted output, and, above all, an automatic module, with which even a lay user can conduct a risk assessment. The entire package, especially its automatic module, is supported by an extensive knowledge-base built into the software.