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Mean Value Analysis Approximation for multiple server queueing networks
1988
Performance evaluation (Print)
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This approximation method has all the advantages of classical mean value analysis; specifically, it is easy to implement and has a very short run time. ...
A comparison against classical mean value analysis allows us to determine the accuracy of our algorithm. ...
Acknowledgment We are grateful to the reviewers for their suggestions and constructive criticisms. Special thanks are due to Dr. Martin Reiser for his invaluable advice on the manuscript. ...
doi:10.1016/0166-5316(88)90015-6
fatcat:iijauutfsncdbp72pr5fyx3ory
MapReduce Approach to Collective Classification for Networks
[chapter]
2012
Lecture Notes in Computer Science
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We introduced a proposal for implementation of label propagation algorithm in the network. The method was examined on real dataset in telecommunication domain. ...
The results indicated that it can be used to classify nodes in order to propose new offerings or tariffs to customers. 1 ...
We introduced a proposal for implementation of Iterative Label Propagation algorithm in the network. Thanks to that, the method can perform complicated calculation using big data sets. ...
doi:10.1007/978-3-642-29347-4_76
fatcat:iqlpmcfoajc2le4awtymhlyitm
Approximate analysis of load dependent general queueing networks
1988
IEEE Transactions on Software Engineering
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The technique demonstrated is an extension of the well-known method of Marie. A new formula for the conditional throughputs is derived. ...
After each iteration a check is performed to guarantee that the results obtained are within a tolerance level E . These iterations are repeated whenever invalid results are detected. ...
Termination TestAfter each iteration we check to see if the sum of the mean number of jobs is equal to the total number of jobs in the given network within a tolerance level E :Additional check is made ...
doi:10.1109/32.9042
fatcat:vqyukj6ogbhetkyu6bjtsbamqy
Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling
2000
IEEE Transactions on Neural Networks
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In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions ...
This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction ...
ACKNOWLEDGMENT The authors would like to thank the reviewers for their helpful comments and suggestions that contributed to improve the quality of this paper. ...
doi:10.1109/72.839016
pmid:18249776
fatcat:3z3ptgj3t5d2tiswbgu36i7uoq
A neural network model for scheduling problems
1996
European Journal of Operational Research
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This paper proposes a new neural network approach to solve the single machine mean tardiness scheduling problem and the minimum makespan job shop schedUling problem. ...
Artificial neural networks (ANNs) have been successfully applied to solve a variety of problems. ...
Acknowledgements The authors wish to thank the referees for their detailed comments and constructive criticisms of the initial draft. ...
doi:10.1016/0377-2217(96)00041-0
fatcat:vyuu4qi3kvfjfidd72w3xs26re
MapReduce approach to relational influence propagation in complex networks
2012
Pattern Analysis and Applications
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The method we propose estimates class probability in relational domain in the networks. The method was examined on large real telecommunication data set. ...
The results indicated that it could be used successfully to classify networks' nodes and, thanks to that, new offerings or tariffs might be proposed to customers who belong to other providers. ...
Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) ...
doi:10.1007/s10044-012-0294-6
fatcat:qhifpc3ihbgfdmxy4sa2mgep6i
PDAC: A data parallel algorithm for the performance analysis of closed queueing networks
1993
Parallel Computing
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The computational cost of the PDAC algorithm is shown to be of polynomial order with a lower degree than the cost of the serial implementation of the DAC algorithm. ...
The PDAC algorithm uses data parallel computation of the summation indices needed to compute the joint queue length probabilities. ...
Then each chain contains a single job and number of chains in the network equals the number of jobs. The recursion of the RECAL algorithm is based on adding one chain at a time. ...
doi:10.1016/0167-8191(93)90080-5
fatcat:6k7ixovyoffa3d4zfloexyasha
Mean value analysis of re-entrant line with batch machines and multi-class jobs
2002
Computers & Operations Research
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In this paper, we propose an approximation method based on the mean value analysis for estimating the mean cycle time of each class of jobs, the mean queue length of each bu! ...
The system may not be modeled by a product form queueing network due to the inclusion of the batch machines and the multi-class jobs with di!erent processing times. ...
Acknowledgements The authors would like to thank the anonymous referees whose valuable comments helped us to enhance the presentation of the paper. ...
doi:10.1016/s0305-0548(00)00099-x
fatcat:m46d3fkbkvcnrboawbx36ifr4e
An Object Recognition Algorithm Based on Kernel Function Matching in Spatial Pyramid
2015
Journal of Information and Computational Science
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The parallel SPK algorithm runs over a cluster of computers and achieves less run time. The observed speed-up depends on the number of CPUs and their cores. ...
In the recent years, SPK has been one of the most usable kernel methods along with support vector machine classifier with high accuracy in object recognition. ...
Figure 6 . 6 Steps of Job 2
2. K-means algorithm: () O mkl , m: number of random images used in clustering, k: number of clusters, l: number of iterations. 3. ...
doi:10.12733/jics20106366
fatcat:pdop2epqlrfcdgjdwx6tsxsgva
5. Computational Issues in Statistical Data Analysis
2003
Journal of the Japanese Society of Computational Statistics
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In terms of this model, we evaluate turn-around time, related to amount of transferred data, load (described by execution time) on each slave computer and number of (part-)jobs. ...
Parallel Virtual Machine (PVM) is one of the popular computer libraries to make many computers, connected via computer network, one (virtual) parallel one. ...
In all experiments, B is a number of Bootstrap samples, N means number of processors, and t is a second for the system timer explained already on each slave. ...
doi:10.5183/jjscs1988.15.2_193
fatcat:psn5evf2ijawtcspfuix5omtjq
An improved constraint satisfaction adaptive neural network for job-shop scheduling
2009
Journal of Scheduling
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neural network in terms of computational time and the quality of schedules it produces. ...
The job-shop scheduling problem is one of the most difficult problems in scheduling. This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. ...
Acknowledgments The authors would like to thank the anony- ...
doi:10.1007/s10951-009-0106-z
fatcat:miacjmgrorb6fptmi76repuvae
Performance analysis of a parallel Dantzig-Wolfe decomposition algorithm for linear programming
2002
Computers and Mathematics with Applications
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This paper employs the Dantzig-Wolfe decomposition principle to solve linear programming models in a parallel-computing environment. ...
Adopting the queuing discipline, we showed that under very general conditions, the proposed algorithm speedup trends toward a limiting value as the number of processors increases. (~) ...
The method for numerical experiments in this study was similar to the one proposed by [12] , where the number of model constraints are 20, 50, and 120, and the number of model variables is 120. ...
doi:10.1016/s0898-1221(02)00267-5
fatcat:s57ne5lpsbhyve3bkzzkx5tuza
Investigating Bayesian Optimization for rail network optimization
2019
International Journal of Rail transportation
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The number of trains within the network is equal to the number of lines within the network so we only state the number of trains from here on in. ...
The number of algorithm iterations used by a GA, I GA ,isthefinal value of i in Algorithm 1. ...
Stages 5 and 6 demonstrate that maximizing α x ðÞand calculating μ x ðÞand σ x ðÞare two important processes that are repeated in every iteration of the BO approach. ...
doi:10.1080/23248378.2019.1669500
fatcat:s4a4ff74lvfubmgpdbm6lj7mpi
The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments
2020
Entropy
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A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. ...
In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/e22121410
pmid:33333717
fatcat:ovkezxmaznebddxmqwpkjdauie
BigFCM: Fast, precise and scalable FCM on hadoop
2017
Future generations computer systems
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2016 pre-print arXiv:1605.03047v1 fatcat:vslrah2xefgi7izkimiu57fbpy
However, a serious challenge in fuzzy clustering is the lack of scalability. ...
In this paper, a scalable Fuzzy C-Means (FCM) clustering named BigFCM is proposed and designed for the Hadoop distributed data platform. ...
Second, in our proposed method, just one map-reduce job works iteratively, and there is no need to execute new jobs. It would provide a substantial improvement in computation time. ...
doi:10.1016/j.future.2017.06.010
fatcat:gfqngmxc5zhfvj3hjakrswi5vy
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