Main Track (MT) Session 11

Time and Date: 14:10 - 15:50 on 11th June 2014

Room: Tully I

Chair: Dieter Kranzlmuller

360 Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms [abstract]
Abstract: With more and more workflow systems adopting cloud as their execution environment, it becomes increasingly challenging on how to efficiently manage various workflows, virtual machines (VMs) and workflow execution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) architecture with independent services. A core part of the architecture is how to efficiently respond continuous workflow requests from users and schedule their executions in the cloud. Based on different targets, we propose four heuristic workflow scheduling algorithms for the WFaaS architecture, and analyze the differences and best usages of the algorithms in terms of performance, cost and the price/performance ratio via experimental studies.
Jianwu Wang, Prakashan Korambath, Ilkay Altintas, Jim Davis, Daniel Crawl
36 Large Eddy Simulation of Flow in Realistic Human Upper Airways with Obstructive Sleep Apnea [abstract]
Abstract: Obstructive sleep apnea (OSA) is a common type of sleep disorder characterized by abnormal repetitive cessation in breathing during sleep caused by partial or complete narrowing of pharynx in the upper airway. The upper airway surgery is commonly performed for this disorder, however the success rate is limited because the lack of the thorough understanding of the primary mechanism associated with OSA. The computational fluid dynamics (CFD) simulation with Large Eddy Simulation approach is conducted to investigate a patient-specific upper airway flow with severe OSA. Both pre and post-surgical upper airway models are simulated to reveal the effect of the surgical treatment. Only the inhaled breathing is conducted with six periods (about 15 second) unsteady flow. Compared with the results before and after treatment, it is illustrated that there exists a significant pressure and shear stress dropping region near the soft palate before treatment; and after the treatment the flow resistance in the upper airway is decreased and the wall shear stress value is significantly reduced.
Mingzhen Lu, Yang Liu, Jingying Ye, Haiyan Luo
86 Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm [abstract]
Abstract: The Jacobi-Davidson (JD) algorithm is very well suited for the computation of a few eigenpairs of large sparse complex symmetric nonlinear eigenvalue problems. The performance of JD crucially depends on the treatment of the so-called correction equation, in particular the preconditioner, and the initial vector. Depending on the choice of the spectral shift and the accuracy of the solution, the convergence of JD can vary from linear to cubic. We investigate parallel preconditioners for the Krylov space method used to solve the correction equation. We apply our nonlinear Jacobi-Davidson (NLJD) method to quadratic eigenvalue problems that originate from the time-harmonic Maxwell equation for the modeling and simulation of resonating electromagnetic structures.
Yoichi Matsuo, Hua Guo, Peter Arbenz
184 Improving Collaborative Recommendation via Location-based User-Item Subgroup [abstract]
Abstract: Collaborative filter has been widely and successfully applied in recommendation system. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. Some previous studies have explored that there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items and subgroup analysis can get better accuracy. While, we find that geographical information of user have impacts on user group preference for items. Hence, In this paper, we propose a Bayesian generative model to describe the generative process of user-item subgroup preference under considering users' geographical information. Experimental results show the superiority of the proposed model.
Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo
90 Optimizing Shared-Memory Hyperheuristics on top of Parameterized Metaheuristics [abstract]
Abstract: This paper studies the auto-tuning of shared-memory hyperheuristics developed on top of a unified shared-memory metaheuristic scheme. A theoretical model of the execution time of the unified scheme is empirically adapted for particular metaheuristics and hyperheuristics through experimentation. The model is used to decide at running time the number of threads to obtain a reduced execution time. The number of threads is different for the different basic functions in the scheme, and depends on the problem to be solved, the metaheuristic scheme, the implementation of the basic functions and the computational system where the problem is solved. The applicability of the proposal is shown with a problem of minimization of electricity consumption in exploitation of wells. Experimental results show that satisfactory execution times can be achieved with auto-tuning techniques based on theoretical-empirical models of the execution time.
José Matías Cutillas Lozano, Domingo Gimenez