Academic literature on the topic 'Spare Capacity Allocation'
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Journal articles on the topic "Spare Capacity Allocation"
Liu, Yu, David Tipper, and Korn Vajanapoom. "Spare Capacity Allocation in Two-Layer Networks." IEEE Journal on Selected Areas in Communications 25, no. 5 (June 2007): 974–86. http://dx.doi.org/10.1109/jsac.2007.070610.
Full textYu Liu, D. Tipper, and P. Siripongwutikorn. "Approximating optimal spare capacity allocation by successive survivable routing." IEEE/ACM Transactions on Networking 13, no. 1 (February 2005): 198–211. http://dx.doi.org/10.1109/tnet.2004.842220.
Full textShahriar, Nashid, Shihabur Rahman Chowdhury, Reaz Ahmed, Aimal Khan, Siavash Fathi, Raouf Boutaba, Jeebak Mitra, and Liu Liu. "Virtual Network Survivability Through Joint Spare Capacity Allocation and Embedding." IEEE Journal on Selected Areas in Communications 36, no. 3 (March 2018): 502–18. http://dx.doi.org/10.1109/jsac.2018.2815430.
Full textLiu, William, Krzysztof Pawlikowski, and Harsha Sirisena. "Algebraic connectivity metric for spare capacity allocation problem in survivable networks." Computer Communications 34, no. 12 (August 2011): 1425–35. http://dx.doi.org/10.1016/j.comcom.2010.12.012.
Full textLiu, Victor Yu, and David Tipper. "Spare capacity allocation using shared backup path protection for dual link failures." Computer Communications 36, no. 6 (March 2013): 666–77. http://dx.doi.org/10.1016/j.comcom.2012.09.007.
Full textJordán, Tibor, and Ildikó Schlotter. "Parameterized complexity of spare capacity allocation and the multicost Steiner subgraph problem." Journal of Discrete Algorithms 30 (January 2015): 29–44. http://dx.doi.org/10.1016/j.jda.2014.11.005.
Full textHo, Pin-Han, and H. T. Mouftah. "Framework of spare capacity re-allocation with S-SLSP for mesh WDM networks." Computer Networks 40, no. 1 (September 2002): 167–79. http://dx.doi.org/10.1016/s1389-1286(02)00272-4.
Full textTridandapani, S., A. K. Somani, and U. R. Sandadi. "Low overhead multiprocessor allocation strategies exploiting system spare capacity for fault detection and location." IEEE Transactions on Computers 44, no. 7 (July 1995): 865–77. http://dx.doi.org/10.1109/12.392845.
Full textHarle, D., and S. Albarrak. "Spare capacity allocation and optimisation in a distributed GMPLS-based IP/WDM mesh network." Computer Communications 30, no. 16 (November 2007): 3085–95. http://dx.doi.org/10.1016/j.comcom.2007.05.057.
Full textAhmad, Md Tanweer, and Sandeep Mondal. "Dynamic supplier selection approach for mining equipment company." Journal of Modelling in Management 14, no. 1 (February 11, 2019): 77–105. http://dx.doi.org/10.1108/jm2-04-2018-0046.
Full textDissertations / Theses on the topic "Spare Capacity Allocation"
Liu, Zhiyong. "Ant Based Algorithm and Robustness Metric in Spare Capacity Allocation for Survivable Routing." Thesis, University of Canterbury. Electrical and Computer Engineering, 2010. http://hdl.handle.net/10092/4099.
Full textAtak, Erman. "Incentive, Subsidy, Penalty Mechanisms And Pooled, Unpooled Allocation Of Production Capacity In Service Parts Management Systems." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613408/index.pdf.
Full textdecentralized model, centralized model and decentralized model with incentive, subsidy, penalty designs. In all models, there are two dealers and one item is under consideration and lateral transshipments are allowed. Dealers operate with four inventory level decision (strategies) that consists of base stock level, rationing level, transshipment request level and customer rejection level. Under the decentralized system, a dealer sets its operating strategy according to the strategy of the other dealer and maximizes its own infinite horizon discounted expected profit. In the centralized system, a central authority (say manufacturer) exists, which considers the system-wide infinite horizon discounted expected profit, and makes all decisions. Under decentralized system with incentive, subsidy, penalty designs, manufacturer tries different designs on decentralized system namely revenue sharing, holding cost subsidy, request rejection penalty, transportation cost subsidy and commission subsidy in order to v align decentralized system with centralized system. According to the results obtained, this alignment works best with nearly 40% revenue sharing percentage, low rejection penalty, high transportation cost subsidy under low transportation cost and commission subsidy under very low or very high commissions. Holding cost subsidy, on the other hand, is not a good strategy since it declines decentralized system profit. Considering the second issue, two systems are examined
pooled system and unpooled system. Both systems are centrally managed. In the pooled system, all capacity is dynamically allocated to either dealer considering maximization of system profit. In the unpooled system, capacity is shared among dealers and dealers are always allocated same percentage of the capacity. Infinite horizon average expected profit is maximized in both systems. The dealer having lower holding cost is allocated higher capacity in both pooled and unpooled system
however, exceptions exist in the unpooled system under low arrival rate. Highrevenue dealer is always allocated higher capacity in both pooled and unpooled system. Arrival rate affects both systems such that total capacity utilization increases with increasing arrival rate. From the profit point of view, pooled system has great advantage under low demand rate in general.
Nascimento, Matheus Freire e. Silva do. "TV white space for digital inclusion in Brazil." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/26430.
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This work presents a technological opportunity to make efficient use of the spectrum and at the same time shows its importance to provide digital inclusion in Brazil. The method- ology is applied in a case study investigating the area of Cear ́a, a Brazilian state, where a significant amount of spectrum that can be unleashed via TV White Space exploitation is relevant to be considered, due to its geographic and economical conditions. These white spaces can fill up some holes in the state that do not present any mobile coverage and improve the telecommunication services in other areas. The technology concept, world regulation situation and possible applications using the resource are described throughout this work. The main purpose is to use TV White Spaces together with the LTE band, as a way to soften part of the poor coverage services of 3G, 4G.
Este trabalho apresenta uma oportunidade tecnológica para fazer o uso eficiente do espectro e, ao mesmo tempo, mostra sua importância para a inclusão digital no Brasil. A metodologia é aplicada em um estudo de caso que investiga a área do Ceará, estado brasileiro, onde uma quantidade significativa de espectro que pode ser desencadeada através da exploração dos Espaços em Brancos de TV é relevante a ser considerada, devido as suas condições geográficas e econômicas. Estes espaços em branco podem preencher alguns buracos no estado que não apresentam qualquer cobertura móvel e melhorar os serviços de telecomunicações em outras áreas. O conceito de tecnologia, situação de regulação mundial e possíveis aplicações usando o recurso são descritos ao longo deste trabalho. A principal finalidade é usar os Espaços em Brancos de TV juntamente com a banda do LTE, para tentar suavizar parte do péssimo serviço de cobertura das redes 3G e 4G.
Zulhasnine, Mohammad. "Availability-Aware Spare Capacity Allocation with Partially Protected Rings." Thesis, 2008. http://hdl.handle.net/10012/3584.
Full textLin, Chen-Hsin, and 林政勳. "Spare Capacity Allocation in Self-Healing Communication Networks : Using Clustering Algorithm." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/15910265952061569279.
Full text國立交通大學
資訊管理所
88
This study employs clustering algorithm to reduce the complexity of self-healing communication network spare capacity allocation problem. The self-healing communication network spare capacity allocation problem is NP-hard, implying exponential growth in execution time with network size increases. By using some attributes of node and link, some nodes of the network can form several “virtual nodes” and the original network can be transferred to a simple one, called “virtual network”. Comparing to the spare capacity allocation problem of original network, the new one of the “virtual network” is easier to solve. If the number of the links which covered by virtual nodes is higher, using clustering algorithm to solving spare capacity allocation problem is more efficiency.
Huang, Jung-Kai, and 黃中鍇. "An Evaluative Genetic Algorithm for Spare Capacity Allocation in Survivable Provisioning Networks." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/31335378569836124872.
Full text中原大學
資訊管理研究所
96
This thesis uses one backup path to protect each primary path to against single link failure in communication networks. This study proposes an MEGA to cooperate with SSR algorithm to improve the stability and effectiveness of solve the SCA (Spare Capacity Allocation) problems for SRLG (Shared Risk Link Groups) especially for randomized traffic requirements. The objective of SCA problem is to minimize the total spare capacity when all traffic demands require a 100% survivability or restoration level on different network topologies. Two kinds of experiments are performed in this thesis, the experiment I focus on a non-constrained situation and concentrates on finding the optimal solution. Furthermore, several experimental analyses of operational parameters are executed in this first experiment. The second experiment focus on: (1) nonuniform demands of origin-destination pairs are allowed; (2) “Spilt Flow” scheme is adopted to set up multiple backup paths for protecting one working path; (3) Given network topology and each link cost, the value of redundancy value is significantly influenced by different flow pattern. The experimental results of the first experiment show that MEGA can efficiently approach the best solution. The results of the non-constrained SCA problems also compares with several existing SCA algorithms and reveals that MEGA can enhance the performance of SSR algorithm to prevent the randomized defect of initial route ordering from influencing the result of SCA problem.. The results of the experiment II show that backup path with “Spilt Flow” scheme can effectively reduce total spare capacity required.
Nikjah, Reza. "Performance evaluation and protocol design of fixed-rate and rateless coded relaying networks." Phd thesis, 2010. http://hdl.handle.net/10048/1674.
Full textCommunications
Book chapters on the topic "Spare Capacity Allocation"
Mouftah, Hussein T., and Pin-Han Ho. "Spare Capacity Allocation." In Optical Networks, 149–210. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1169-4_7.
Full textWang, Zuxi, Li Li, Gang Sun, and Hanping Hu. "An Optimization Algorithm of Spare Capacity Allocation by Dynamic Survivable Routing." In Lecture Notes in Computer Science, 439–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13498-2_57.
Full textMa, Tao, Nairong Tan, Xiaolei Wang, Fanfan Zhang, and Hui Fang. "Spatial Carrying Capacity and Sustainability: Cities, Basins, Regional Transformation." In Environmental Change and Sustainability [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94130.
Full textConference papers on the topic "Spare Capacity Allocation"
He, Jianwu, Wenyan Jin, Mianfeng Lin, Wanyi Gu, and Jie Zhang. "Spare capacity allocation in optical networks." In Asia-Pacific Optical and Wireless Communications 2002, edited by Qian Mao, Shoa-Kai Liu, and Kwok-wai Cheung. SPIE, 2002. http://dx.doi.org/10.1117/12.481085.
Full textGonzalez-Montoro, Nehuen, and Jorge M. Finochietto. "Incremental spare capacity allocation for optical networks restoration." In 2015 XVI Workshop on Information Processing and Control (RPIC). IEEE, 2015. http://dx.doi.org/10.1109/rpic.2015.7497142.
Full textLiu, William, Harsha Sirisena, and Krzysztof Pawlikowski. "Efficacy of Fiedler value versus nodal degree in spare capacity allocation." In 2009 15th Asia-Pacific Conference on Communications (APCC). IEEE, 2009. http://dx.doi.org/10.1109/apcc.2009.5375511.
Full textLiu, Victor Yu, and David Tipper. "Spare capacity allocation using shared backup path protection for dual link failures." In 2011 8th International Workshop on the Design of Reliable Communication Networks (DRCN). IEEE, 2011. http://dx.doi.org/10.1109/drcn.2011.6076893.
Full textBril, Reinder J., and Erik J. Luit. "Experience Report: Combining Mixed-Criticality Support with Resource Reservation and Spare Capacity Allocation." In 2019 IEEE International Conference on Software Architecture Companion (ICSA-C). IEEE, 2019. http://dx.doi.org/10.1109/icsa-c.2019.00019.
Full textLiu, William, Harsha Sirisena, and Krzysztof Pawlikowski. "A novel distributed resilience matrix for arbitrary failures in spare capacity allocation problem." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397721.
Full textWang, X., and Q. Li. "Utilization of the spare capacity of exclusive bus lanes based on a dynamic allocation strategy." In URBAN TRANSPORT 2014. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/ut140151.
Full textZizhou, Wang, Zhu Guiwei, Wang Yafeng, and Dacheng Yang. "Adaptive Power Allocation for Regenerative Multi-Relay System Based on Capacity-Approaching Distributed Space-Time Codes." In 2007 IEEE 65th Vehicular Technology Conference. IEEE, 2007. http://dx.doi.org/10.1109/vetecs.2007.443.
Full textDjurdjanovic, Dragan, and Jie Zhu. "Stream of Variation Based Error Compensation Strategy in Multi-Stage Manufacturing Processes." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81550.
Full text