QoS-aware service selection for customisable multi-tenant service-based systems : Maturity and approaches
- Authors: He, Qiang , Han, Jun , Chen, Feifei , Wang, Yanchun , Vasa, Rajesh , Yang, Yun , Jin, Hai
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE 8th International Conference on Cloud Computing (CLOUD) p. 237-244
- Full Text: false
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- Description: Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar and yet customised functionalities with potentially different quality-of service (QoS) values. Thus, existing approaches to service selection for single-tenant SBSs are no longer suitable. Furthermore, the target multi-tenancy maturity level also needs to be considered in the service selection approach for an SBS. In this paper, we propose three novel QoS-aware service selection approaches for composing multi-tenant SBSs that achieve three different multi-tenancy maturity levels. Extensive and comprehensive experiments are conducted and the experimental results show that our approaches outperform the existing approach in both effectiveness and efficiency.
Application of optimisation-based data mining techniques to medical data sets: A comparative analysis
- Authors: Dzalilov, Zari , Bagirov, Adil , Mammadov, Musa
- Date: 2012
- Type: Text , Conference paper
- Relation: IMMM 2102: The Second International Conference on Advances in Information Mining and Management p. 41-46
- Full Text: false
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- Description: Abstract - Computational methods have become an important tool in the analysis of medical data sets. In this paper, we apply three optimisation-based data mining methods to the following data sets: (i) a cystic fibrosis data set and (ii) a tobacco control data set. Three algorithms used in the analysis of these data sets include: the modified linear least square fit, an optimization based heuristic algorithm for feature selection and an optimization based clustering algorithm. All these methods explore the relationship between features and classes, with the aim of determining contribution of specific features to the class outcome. However, the three algorithms are based on completely different approaches. We apply these methods to solve feature selection and classification problems. We also present comparative analysis of the algorithms using computational results. Results obtained confirm that these algorithms may be effectively applied to the analysis of other (bio)medical data sets
Learning sparse kernel classifiers in the primal
- Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
- Date: 2012
- Type: Text , Conference paper
- Relation: Joint IAPR International Workshop, SSPR&SPR 2012; Hiroshima, Japan; 7th-9th November 2012; published in Structural, Syntactic, and Statistical Pattern Recognition (part of the Lecture Notes in Computer Science) Vol. 7626, p. 60-69
- Full Text: false
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- Description: The increasing number of classification applications in large data sets demands that efficient classifiers be designed not only in training but also for prediction. In this paper, we address the problem of learning kernel classifiers with reduced complexity and improved efficiency for prediction in comparison to those trained by standard methods. A single optimisation problem is formulated for classifier learning which optimises both classifier weights and eXpansion Vectors (XVs) that define the classification function in a joint fashion. Unlike the existing approach of Wu et al, which performs optimisation in the dual formulation, our approach solves the primal problem directly. The primal problem is much more efficient to solve, as it can be converted to the training of a linear classifier in each iteration, which scales linearly to the size of the data set and the number of expansions. This makes our primal approach highly desirable for large-scale applications, where the dual approach is inadequate and prohibitively slow due to the solution of cubic-time kernel SVM involved in each iteration. Experimental results have demonstrated the efficiency and effectiveness of the proposed primal approach for learning sparse kernel classifiers that clearly outperform the alternatives.
Resonant frequency band estimation using adaptive wavelet decomposition level selection
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Conference paper
- Relation: 2011 IEEE International Conference on Mechatronics and Automation (ICMA) p. 376-381
- Full Text: false
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- Description: The vibrations induced by machine faults help in diagnosis and prognosis of the machine. It is crucial for the fault diagnostic system to extract resonant frequency band which carries useful information about the defect frequencies and contains maximum signal to noise ratio. The spectral orientation of the resonant frequency band varies with the variation in machine dynamics. The existing techniques which employ wavelet transformation to exploit the signal energy distribution among different frequency sub-bands, are based on fixed decomposition level and do not optimize the wavelet parameters according to varying machine dynamics. The proposed study develops a novel technique: Adaptive Wavelet Decomposition and Resonance Frequency Estimation (AWRE) which estimates the positioning of the resonant frequency band based on adaptive selection of the wavelet decomposition levels. The results for the simulated as well as actual vibration data demonstrate that the proposed technique estimates the bandwidth of the resonant frequency band quite effectively.
A new technique for global optimization methods
- Authors: Wu, Zhiyou , Pang, Xianglu
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 1st International Conference on Green Circuits and Systems, ICGCS 2010, Shanghai : 21st-23rd June 2010 p. 398-403
- Full Text: false
- Description: We know that the necessary local optimality conditions are the main tools for the development of efficient numerical methods in local optimization. In this paper, we propose a new technique for global optimization methods. First we will introduce some new approach to obtain some verifiable global optimality conditions including some necessary global optimality conditions and some sufficient global optimality conditions. Then we will introduce how to use the obtained necessary global optimality conditions to design a new optimization method called strongly local optimization method and combining the new strongly local optimization method, some methods to improve the current strongly local minimizer and the obtained sufficient global optimality conditions to design some global optimization methods with some stopping criteria. © 2010 IEEE.
Threshold-free pattern-based low bit rate video coding
- Authors: Paul, Manoranjan , Murshed, Manzur
- Date: 2008
- Type: Text , Conference paper
- Relation: 2008 15th IEEE International Conference on Image Processing p. 1584-1587
- Full Text: false
- Reviewed:
- Description: Pattern-based video coding (PVC) has already established its superiority over recent video coding standard H.264, at low bit rate because of an extra pattern-mode to segment out the arbitrary shape of the moving region within the macroblock (MB). To determine the pattern-mode, the PVC however uses three thresholds to reduce the number of MBs coded using the pattern- mode. By setting these content-sensitive thresholds to any predefined values, the technique risks ignoring some MBs that would otherwise be selected by the rate-distortion optimization function for this mode. Consequently, the ultimate achievable performance is sacrificed to save motion estimation times. In this paper, a novel PVC scheme is proposed by removing all thresholds to determine this mode and hence more efficient performance is achieved without knowing the content of the video sequences. To keep computational complexity in check, pattern motion is approximated from the motion vector of the MB. In addition, efficient pattern similarity metric and new Lagrangian multipliers are also developed. The experimental results confirm that this new scheme improves the image quality by at least 0.5 dB and 1.0 dB compared to the existing PVC and the H.264 respectively
A nonsmooth optimization approach to H-infinity synthesis
- Authors: Mammadov, Musa , Orsi, Robert
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the 44th IEEE Conference on Decision and Control and European Control Conference ECC 2005, Seville, Sp[ain, 12-15 December 2005, Seville, Spain : 12th - 15th December, 2005
- Full Text: false
- Reviewed:
- Description: A numerical method for solving the H∞ synthesis problem is presented. The problem is posed as an unconstrained, nonsmooth, nonconvex minimization problem. The optimization variables consist solely of the entries of the output feedback matrix. No additional variables, such as Lyapunov variables, need to be introduced. The optimization procedure uses a line search mechanism where the descent direction is defined by a recently introduced dynamical systems approach. Numerical results for various benchmark problems are included.
- Description: E1
- Description: 2003001386
A new global optimization algorithm based on a dynamical systems approach
- Authors: Mammadov, Musa
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ICOTA6: 6th International Conference on Optimization - Techniques and Applications, Ballarat, Victoria : 9th December, 2004
- Full Text: false
- Reviewed:
- Description: The purpose of the paper is to develop and study new techniques for global optimization based on dynamical systems approach. This approach uses the notion of relationship between variables which describes influences of the changes of the variables to each other. A numerical algorithm for global optimization is introduced.
- Description: E1
- Description: 2003000892
Multi label classification and drug-reaction associations using global optimization techniques
- Authors: Mammadov, Musa , Yearwood, John , Aliyea, Leyla
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ICOTA6: 6th International Conference on Optimization - Techniques and Applications, Ballarat, Victoria : 9th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000890
On a class of abstract convex functions
- Authors: Rubinov, Alex , Hajilarov, Eldar
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ICOTA6: 6th International Conference on Optimization - Techniques and Applications, Ballarat, Victoria : 9th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000928
Optimization in wireless local area network
- Authors: Kouhbor, Shahnaz , Ugon, Julien , Kruger, Alexander , Rubinov, Alex , Branch, Philip
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ICOTA6: 6th International Conference on Optimization - Techniques and Applications, Ballarat, Victoria : 9th December, 2004
- Full Text: false
- Reviewed:
- Description: 2003000886
Optimization of feed forward MLPs using the discrete gradient method
- Authors: Bagirov, Adil , Yearwood, John , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2004: International Conference on Computational Intelligence for Modelling, Control & Automation, Gold Coast, Queensland : 12th July, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000845
A delay system approach to linear differential repetitive processes : Controllability and optimization
- Authors: Dymkou, Siarhei , Rogers, E. , Dymkov, M. , Galkowski, K. , Owens, D.
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the 4th IFAC Workshop on Time Delay Systems (TDS '03), Rocquencourt, France : 8th September, 2003
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000486
An optimization-based approach to patient grouping for acute healthcare in Australia
- Authors: Bagirov, Adil , Churilov, Leonid
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Computational Science - ICCS 2003 Conference, Melbourne : 2nd June, 2003
- Full Text: false
- Reviewed:
- Description: The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the cluster analysis problem based on the nonsmooth optimization techniques is developed. The issues of applying this algorithm to large data sets are discussed and a feature selection procedure is demonstrated. The algorithm is then applied to a hospital data set to generate new knowledge about different patterns of patients resource consumption.
- Description: E1
- Description: 2003000434
Approximation of the optimal control problem with the nonsmooth state constraints
- Authors: Dymkou, Siarhei
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the Symposium on the Industrial Optimisation and the 9th Australian Optimisation Day, Perth : 30th October, 2002
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000466
Controllability and optimization for differential linear repetitive processes
- Authors: Dymkou, Siarhei , Rogers, E. , Dymkov, M. , Galkowski, K. , Owens, D.
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the 42nd IEEE Conference on Decision and Control, Hawaii, USA : 8th December, 2003
- Full Text: false
- Reviewed:
- Description: Differential linear repetitive processes are a class of continuous-discrete 2D systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is the fact that information propagation in one of the two independent directions only occurs over a finite interval. In this paper we develop an operator theory approach for the study of basic systems theoretic structural and control properties of these processes. In particular, we first develop a characterization of the range space of an operator generated by dynamics of the processes under consideration and use it to characterize a controllability property. Also we extend this operator setting to produce new results for a (again physically relevant) linear-quadratic optimization problem for these processes and the resulting optimal feedback control law.
- Description: E1
- Description: 2003000483
Dynamic reconfiguration of telecommunication networks
- Authors: Dzalilov, Zari , Ouveysi, Iradj , Rubinov, Alex
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the Industrial Optimisation 2003 Conference, Perth : 30th October, 2002
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000451
Optimization in telecommunication network maintenance
- Authors: Jia, Long , Rubinov, Alex , Ouveysi, Iradj
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the Symposium on Industrial Optimisation and the 9th Australian Optimisation Day, Perth : 30th September, 2002
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000349
Parallelization of the discrete gradient method of non-smooth optimization and its applications
- Authors: Beliakov, Gleb , Tobon, Monsalve , Bagirov, Adil
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Computational Science ICCS 2003 Conference, Melbourne : 2nd June, 2003
- Full Text: false
- Reviewed:
- Description: We investigate parallelization and performance of the discrete gradient method of nonsmooth optimization. This derivative free method is shown to be an effective optimization tool, able to skip many shallow local minima of nonconvex nondifferentiable objective functions. Although this is a sequential iterative method, we were able to parallelize critical steps of the algorithm, and this lead to a significant improvement in performance on multiprocessor computer clusters. We applied this method to a difficult polyatomic clusters problem in computational chemistry, and found this method to outperform other algorithms.
- Description: E1
- Description: 2003000435
Penalty functions with a small penalty parameter : Numerical experiments
- Authors: Bagirov, Adil , Rubinov, Alex
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at Industrial Optimization Conference 2003, Perth : 30th September, 2002
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000432