Academic literature on the topic 'Dynamic allocation of ressources'
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Journal articles on the topic "Dynamic allocation of ressources":
Hung, Nguyen Manh. "L’efficacité économique du mode d’allocation des ressources naturelles." Articles 51, no. 3 (July 15, 2009): 405–19. http://dx.doi.org/10.7202/800630ar.
MUKANDILA KALOMBO, Pascal. "PROBLEMATIQUE DES ACCOUCHEMENTS DYSTOCIQUES AU CENTRE DE SANTE MATERNEL ET INFANTILE (CSMI) DE LUBAO, EN REPUBLIQUE DEMOCRATIQUE DU CONGO." Tanganyika Journal Of Science 2, no. 1 (October 15, 2022): 17–24. http://dx.doi.org/10.59296/tgjs.2221033.
Balassa, Bela. "Politiques agricoles et allocation internationale des ressources." Économie rurale 189, no. 1 (1989): 22–28. http://dx.doi.org/10.3406/ecoru.1989.3948.
Audibert, Gérard, Hélène Gebel, and Michel Hasselmann. "Allocation de ressources médicales rares : enjeux éthiques et citoyens." Revue française d'éthique appliquée N° 12, no. 1 (June 13, 2022): 171–82. http://dx.doi.org/10.3917/rfeap.012.0171.
Gamel, Claude. "Comment financer l'allocation universelle? La stratégie de Van Parijs (1995) en question." Recherches économiques de Louvain 70, no. 3 (2004): 287–315. http://dx.doi.org/10.1017/s0770451800010769.
Kang, Hyun. "Random allocation and dynamic allocation randomization." Anesthesia and Pain Medicine 12, no. 3 (July 31, 2017): 201–12. http://dx.doi.org/10.17085/apm.2017.12.3.201.
Singh, Ishmeet. "Dynamic asset allocation." International Journal of Recent Scientific Research 08, no. 05 (May 28, 2017): 17204–8. http://dx.doi.org/10.24327/ijrsr.2017.0805.0304.
Fong, H. Gifford. "Dynamic Asset Allocation." ICFA Continuing Education Series 1987, no. 1 (January 1987): 82–85. http://dx.doi.org/10.2469/cp.v1987.n1.12.
Madhogarhia, Pawan K., and Marco Lam. "Dynamic asset allocation." Journal of Asset Management 16, no. 5 (August 26, 2015): 293–302. http://dx.doi.org/10.1057/jam.2015.4.
Carter, Patrick, Fabien Postel-Vinay, and Jonathan Temple. "Dynamic aid allocation." Journal of International Economics 95, no. 2 (March 2015): 291–304. http://dx.doi.org/10.1016/j.jinteco.2014.11.005.
Dissertations / Theses on the topic "Dynamic allocation of ressources":
Lyazidi, Mohammed Yazid. "Dynamic resource allocation and network optimization in the Cloud Radio Access Network." Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066549.
Cloud Radio Access Network (C-RAN) is a future direction in wireless communications for deploying cellular radio access subsystems in current 4G and next-generation 5G networks. In the C-RAN architecture, BaseBand Units (BBUs) are located in a pool of virtual base stations, which are connected via a high-bandwidth low latency fronthaul network to Radio Remote Heads (RRHs). In comparison to standalone clusters of distributed radio base stations, C-RAN architecture provides significant benefits in terms of centralized resource pooling, network flexibility and cost savings. In this thesis, we address the problem of dynamic resource allocation and power minimization in downlink communications for C-RAN. Our research aims to allocate baseband resources to dynamic flows of mobile users, while properly assigning RRHs to BBUs to accommodate the traffic and network demands. This is a non-linear NP-hard optimization problem, which encompasses many constraints such as mobile users' resources demands, interference management, BBU pool and fronthaul links capacities, as well as maximum transmission power limitation. To overcome the high complexity involved in this problem, we present several approaches for resource allocation strategies and tackle this issue in three stages. Obtained results prove the efficiency of our proposed strategies in terms of throughput satisfaction rate, number of active RRHs, BBU pool processing power, resiliency, and operational budget cost
Lyazidi, Mohammed Yazid. "Dynamic resource allocation and network optimization in the Cloud Radio Access Network." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066549/document.
Cloud Radio Access Network (C-RAN) is a future direction in wireless communications for deploying cellular radio access subsystems in current 4G and next-generation 5G networks. In the C-RAN architecture, BaseBand Units (BBUs) are located in a pool of virtual base stations, which are connected via a high-bandwidth low latency fronthaul network to Radio Remote Heads (RRHs). In comparison to standalone clusters of distributed radio base stations, C-RAN architecture provides significant benefits in terms of centralized resource pooling, network flexibility and cost savings. In this thesis, we address the problem of dynamic resource allocation and power minimization in downlink communications for C-RAN. Our research aims to allocate baseband resources to dynamic flows of mobile users, while properly assigning RRHs to BBUs to accommodate the traffic and network demands. This is a non-linear NP-hard optimization problem, which encompasses many constraints such as mobile users' resources demands, interference management, BBU pool and fronthaul links capacities, as well as maximum transmission power limitation. To overcome the high complexity involved in this problem, we present several approaches for resource allocation strategies and tackle this issue in three stages. Obtained results prove the efficiency of our proposed strategies in terms of throughput satisfaction rate, number of active RRHs, BBU pool processing power, resiliency, and operational budget cost
Jmila, Houda. "Dynamic resource allocation and management in virtual networks and Clouds." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0023.
Cloud computing is a promising technology enabling IT resources reservation and utilization on a pay-as-you-go manner. In addition to the traditional computing resources, cloud tenants expect compete networking of their dedicated resources to easily deploy network functions and services. They need to manage an entire Virtual Network (VN) or infrastructure. Thus, Cloud providers should deploy dynamic and adaptive resource provisioning solutions to allocate virtual networks that reflect the time-varying needs of Cloud-hosted applications. Prior work on virtual network resource provisioning only focused on the problem of mapping the virtual nodes and links composing a virtual network request to the substrate network nodes and paths, known as the Virtual network embedding (VNE) problem. Little attention was paid to the resource management of the allocated resources to continuously meet the varying demands of embedded virtual networks and to ensure efficient substrate resource utilization. The aim of this thesis is to enable dynamic and preventive virtual network resources provisioning to deal with demand fluctuation during the virtual network lifetime, and to enhance the substrate resources usage. To reach these goals, the thesis proposes adaptive resource allocation algorithms for evolving virtual network requests. We adress the extension of an embedded virtual node requiring more resources and consider the substrate network profitability. We also deal with the bandwidth demand variation in embedded virtual links. We first provide a heuristic algorithm to deal with virtual nodes demand fluctuation. The work is extended by designing a preventive re-configuration scheme to enhance substrate network profitability. Finally, a distributed, local-view and parallel framework was devised to handle embedded virtual links bandwidth fluctuations. The approach is composed of a controller and three algorithms running in each substrate node in a distributed and parallel manner. The framework is based on the self-stabilization approach, and can manage various forms of bandwidth demand variations simultaneously
Jmila, Houda. "Dynamic resource allocation and management in virtual networks and Clouds." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0023/document.
Cloud computing is a promising technology enabling IT resources reservation and utilization on a pay-as-you-go manner. In addition to the traditional computing resources, cloud tenants expect compete networking of their dedicated resources to easily deploy network functions and services. They need to manage an entire Virtual Network (VN) or infrastructure. Thus, Cloud providers should deploy dynamic and adaptive resource provisioning solutions to allocate virtual networks that reflect the time-varying needs of Cloud-hosted applications. Prior work on virtual network resource provisioning only focused on the problem of mapping the virtual nodes and links composing a virtual network request to the substrate network nodes and paths, known as the Virtual network embedding (VNE) problem. Little attention was paid to the resource management of the allocated resources to continuously meet the varying demands of embedded virtual networks and to ensure efficient substrate resource utilization. The aim of this thesis is to enable dynamic and preventive virtual network resources provisioning to deal with demand fluctuation during the virtual network lifetime, and to enhance the substrate resources usage. To reach these goals, the thesis proposes adaptive resource allocation algorithms for evolving virtual network requests. We adress the extension of an embedded virtual node requiring more resources and consider the substrate network profitability. We also deal with the bandwidth demand variation in embedded virtual links. We first provide a heuristic algorithm to deal with virtual nodes demand fluctuation. The work is extended by designing a preventive re-configuration scheme to enhance substrate network profitability. Finally, a distributed, local-view and parallel framework was devised to handle embedded virtual links bandwidth fluctuations. The approach is composed of a controller and three algorithms running in each substrate node in a distributed and parallel manner. The framework is based on the self-stabilization approach, and can manage various forms of bandwidth demand variations simultaneously
Morcos, Mira. "Auction-based dynamic resource orchestration in cloud-based radio access networks." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL003.
Network densification using small cells massively deployed over the macro-cell areas, represents a promising solution for future 5G mobile networks to cope with mobile traffic increase. In order to simplify the management of the heterogeneous Radio Access Network (RAN) that results from the massive deployment of small cells, recent research and industrial studies have promoted the design of novel centralized RAN architectures termed as Cloud-RAN (C-RAN), or Virtual RAN (V-RAN), by incorporating the benefits of cloud computing and Network Functions Virtualization (NFV). The DynaRoC project aims at (1) developing a theoretical framework of resource orchestration for C-RAN and deriving the fundamental performance limits as well as the tradeoffs among various system parameters, and (2) designing dynamic resource orchestration mechanisms based on the theoretical findings to achieve a desired performance balance, by taking into account various design challenges. The PhD student will investigate innovative resource optimization mechanisms to foster the deployment of C-RANs, improving their performance exploiting the enabling Network Functions Virtualization technology
Morcos, Mira. "Auction-based dynamic resource orchestration in cloud-based radio access networks." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL003.
Network densification using small cells massively deployed over the macro-cell areas, represents a promising solution for future 5G mobile networks to cope with mobile traffic increase. In order to simplify the management of the heterogeneous Radio Access Network (RAN) that results from the massive deployment of small cells, recent research and industrial studies have promoted the design of novel centralized RAN architectures termed as Cloud-RAN (C-RAN), or Virtual RAN (V-RAN), by incorporating the benefits of cloud computing and Network Functions Virtualization (NFV). The DynaRoC project aims at (1) developing a theoretical framework of resource orchestration for C-RAN and deriving the fundamental performance limits as well as the tradeoffs among various system parameters, and (2) designing dynamic resource orchestration mechanisms based on the theoretical findings to achieve a desired performance balance, by taking into account various design challenges. The PhD student will investigate innovative resource optimization mechanisms to foster the deployment of C-RANs, improving their performance exploiting the enabling Network Functions Virtualization technology
Luu, Quang Trung. "Dynamic Control and Optimization of Wireless Virtual Networks." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG039.
Network slicing is a key enabler for 5G networks. With network slicing, Mobile Network Operators (MNO) create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), efficiently sharing the resources across various slices is very important. In this thesis, taking the InP perspective, we propose several methods for provisioning resources for network slices. Previous best-effort approaches deploy the various Service Function Chains (SFCs) of a given slice sequentially in the infrastructure network. In this thesis, we provision aggregate resources to accommodate slice demands. Once provisioning is successful, the SFCs of the slice are ensured to get enough resources to be properly operated. This facilitates the satisfaction of the slice quality of service requirements. The proposed provisioning solutions also yield a reduction of the computational resources needed to deploy the SFCs
Unlu, Eren. "Dynamic Bandwidth allocation algorithms for an RF on-chip interconnect." Thesis, CentraleSupélec, 2016. http://www.theses.fr/2016SUPL0006/document.
With rapidly increasing number of cores on a single chip, scalability problems have arised due to congestion and latency with conventional interconnects. In order to address these issues, WiNoCoD project (Wired RF Network-on-Chip Reconfigurable-on-Demand) has been initiated by the support of French National Research Agency (ANR). This thesis work contributes to WiNoCoD project. A special RF controller structure has been proposed for the OFDMA based wired RF interconnect of WiNoCoD. Based on this architecture, effective bandwidth allocation algorithms have been presented, concerning very specific requirements and constraints of on-chip environment. An innovative subcarrier allocation protocol for bimodal packet lengths of cache coherency traffic has been presented, which is proven to decrease average latency significantly. In addition to these, effective modulation order selection policies for this interconnect have been introduced, which seeks the optimal delay-power trade-off
Avranas, Apostolos. "Resource allocation for latency sensitive wireless systems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT021.
The new generation of wireless systems 5G aims not only to convincingly exceed its predecessor (LTE) data rate but to work with more dimensions. For instance, more user classes were introduced associated with different available operating points on the trade-off of data rate, latency, reliability. New applications, including augmented reality, autonomous driving, industry automation and tele-surgery, push the need for reliable communications to be carried out under extremely stringent latency constraints. How to manage the physical level in order to successfully meet those service guarantees without wasting valuable and expensive resources is a hard question. Moreover, as the permissible communication latencies shrink, allowing retransmission protocol within this limited time interval is questionable. In this thesis, we first pursue to answer those two questions. Concentrating on the physical layer and specifically on a point to point communication system, we aim to answer if there is any resource allocation of power and blocklength that will render an Hybrid Automatic ReQuest (HARQ) protocol with any number of retransmissions beneficial. Unfortunately, the short latency requirements force only a limited number of symbols to possibly be transmitted which in its turn yields the use of the traditional Shannon theory inaccurate. Hence, the more involved expression using finite blocklength theory must be employed rendering the problem substantially more complicate. We manage to solve the problem firstly for the additive white gaussian noise (AWGN) case after appropriate mathematical manipulations and the introduction of an algorithm based on dynamic programming. Later we move on the more general case where the signal is distorted by a Ricean channel fading. We investigate how the scheduling decisions are affected given the two opposite cases of Channel State Information (CSI), one where only the statistical properties of the channel is known, i.e. statistical CSI, and one where the exact value of the channel is provided to the transmitter, i.e., full CSI.Finally we ask the same question one layer above, i.e. the Medium Access Contron (MAC). The resource allocation must be performed now accross multiple users. The setup for each user remains the same, meaning that a specific amount of information must be delivered successfully under strict latency constraints within which retransmissions are allowed. As 5G categorize users to different classes users according to their needs, we model the traffic under the same concept so each user belongs to a different class defining its latency and data needs. We develop a deep reinforcement learning algorithm that manages to train a neural network model that competes conventional approaches using optimization or combinatorial algorithms. In our simulations, the neural network model actually manages to outperform them in both statistical and full CSI case
Zehendner, Elisabeth. "Operations management at container terminals using advanced information technologies." Phd thesis, Ecole Nationale Supérieure des Mines de Saint-Etienne, 2013. http://tel.archives-ouvertes.fr/tel-00972071.
Books on the topic "Dynamic allocation of ressources":
Liu, Jun. Dynamic asset allocation with event risk. Cambridge, MA: National Bureau of Economic Research, 2002.
Streufert, Peter A. Dynamic allocation with consistent intergenerational benevolence. Stanford, Calif: Institute for Mathematical Studies in the Social Sciences, Stanford University, 1985.
Benmammar, Badr, and Asma Amraoui. Radio Resource Allocation and Dynamic Spectrum Access. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118575116.
Alpcan, Tansu, Holger Boche, Michael L. Honig, and H. Vincent Poor, eds. Mechanisms and Games for Dynamic Spectrum Allocation. Cambridge: Cambridge University Press, 2013. http://dx.doi.org/10.1017/cbo9781139524421.
Wang, Xinshang. Online Algorithms for Dynamic Resource Allocation Problems. [New York, N.Y.?]: [publisher not identified], 2017.
Nur, Cavdaroglu. Three Essays on Dynamic Pricing and Resource Allocation. [New York, N.Y.?]: [publisher not identified], 2012.
Hummel, Robert A. Dynamic processor allocation for parallel algorithms in image processing. New York: Courant Institute of Mathematical Sciences, New York University, 1987.
Kazari͡an, S. S. Skolʹzi͡ashchee raspredelenie resursa na seti metodom dinamicheskogo programmirovanii͡a. Moskva: Vychislitelʹnyĭ t͡sentr AN SSSR, 1986.
Ibaraki, Toshihide. Resource allocation problems: Algorithmic approaches. Cambridge, Mass: MIT Press, 1988.
Labbe, Martine. Approximation algorithms for the capacitated plant allocation problem. Brussels: European Institute for Advanced Studies in Management, 1992.
Book chapters on the topic "Dynamic allocation of ressources":
Weik, Martin H. "dynamic allocation." In Computer Science and Communications Dictionary, 471. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5723.
Chan, Raymond H., Yves ZY Guo, Spike T. Lee, and Xun Li. "Dynamic Asset Allocation." In Financial Mathematics, Derivatives and Structured Products, 351–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3696-6_26.
Ettinger, Jean. "Dynamic memory allocation." In Programming in C++, 97–110. London: Macmillan Education UK, 1994. http://dx.doi.org/10.1007/978-1-349-23304-5_9.
Wang, Shaowei. "Dynamic Resource Allocation." In Cognitive Radio Networks, 9–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08936-2_2.
Weik, Martin H. "dynamic buffer allocation." In Computer Science and Communications Dictionary, 472. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5731.
Weik, Martin H. "dynamic resource allocation." In Computer Science and Communications Dictionary, 473. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5747.
Weik, Martin H. "dynamic storage allocation." In Computer Science and Communications Dictionary, 474. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5754.
Olafsson, S. "Dynamic Task Allocation." In Modelling Future Telecommunications Systems, 285–310. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-2049-8_16.
Bartlett, Jonathan. "Dynamic Memory Allocation." In Learn to Program with Assembly, 173–86. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7437-8_14.
Jena, Sisir Kumar. "Dynamic Memory Allocation." In C Programming, 349–63. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003188254-13.
Conference papers on the topic "Dynamic allocation of ressources":
Boukerche, Azzedine, Khalil El-Khatib, and Tingxue Huang. "A performance comparison of dynamic channel and ressource allocation protocols for mobile cellular networks." In the second international workshop. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1023783.1023790.
Khabir, Abdelilah, Zoubir Elfelssoufi, and Hamid Azzouzi. "Flexible Allocation of Human Ressources Uder Constraints." In 2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). IEEE, 2019. http://dx.doi.org/10.1109/logistiqua.2019.8907314.
Zhioua, Ghayet El Mouna, Soumaya Hamouda, Philippe Godlewski, and Sami Tabbane. "A femtocells ressources allocation scheme in OFDMA based networks." In 2010 Second International Conference on Communications and Networking (ComNet). IEEE, 2010. http://dx.doi.org/10.1109/comnet.2010.5699812.
Nicolicin-Georgescu, Vlad, Vincent Benatier, Remi Lehn, and Henri Briand. "Ontology-Based Autonomic Computing for Decision Support Systems Management: Shared Ressources Allocation between Groups of Data Warehouses." In 2010 Third International Conference on Communication Theory, Reliability, and Quality of Service. IEEE, 2010. http://dx.doi.org/10.1109/ctrq.2010.46.
Bar-Noy, Amotz, Yishay Mansour, and Baruch Schieber. "Competitive dynamic bandwidth allocation." In the seventeenth annual ACM symposium. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/277697.277704.
Sultan, Saad, Abdullah Asad, M. Abubakar, Suleman Khalid, Shahab Ahmed, and Aamir Wali. "Dynamic Cloud Resources Allocation." In 2019 8th International Conference on Information and Communication Technologies (ICICT). IEEE, 2019. http://dx.doi.org/10.1109/icict47744.2019.9001996.
Kwang Moon Cho. "Dynamic channel allocation by using allocation function in PCS." In Proceedings of APCC/OECC'99 - 5th Asia Pacific Conference on Communications/4th Optoelectronics and Communications Conference. IEEE, 1999. http://dx.doi.org/10.1109/apcc.1999.824934.
Hwang, Hyun-Yong, Sung-Min Oh, Changhee Lee, Jae Heung Kim, and Jaesheung Shin. "Dynamic RACH preamble allocation scheme." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354660.
Radzi, N. A. M., N. M. Din, S. K. Sadon, and M. H. Al-Mansoori. "Dynamic bandwidth allocation EPON survey." In 2013 IEEE Student Conference on Research and Development (SCOReD). IEEE, 2013. http://dx.doi.org/10.1109/scored.2013.7002561.
Nascimento, A., J. Rodriguez, A. Gameiro, and C. Politis. "Dynamic Resource Allocation for IEEE802.16e." In 3rd International ICST Conference on Mobile Multimedia Communications. ICST, 2007. http://dx.doi.org/10.4108/icst.mobimedia2007.1992.
Reports on the topic "Dynamic allocation of ressources":
Kalyanasundaram, Bala. Dynamic Spectrum Allocation Algorithms. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada418179.
Pruhs, Kirk, and Bala Kalyanasundaram. Dynamic Spectrum Allocation Algorithms. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada420598.
Kalyanasundaram, Bala, and Kirk Pruhs. Dynamic Spectrum Allocation Algorithms. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada387970.
Hansen, Jeff, Scott Hissam, B. C. Meyers, Gabriel Moreno, Daniel M. Plakosh, Joseph Seibel, and Lutz Wrage. Resource Allocation in Dynamic Environments. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada609913.
Liu, Jun, Francis Longstaff, and Jun Pan. Dynamic Asset Allocation With Event Risk. Cambridge, MA: National Bureau of Economic Research, August 2002. http://dx.doi.org/10.3386/w9103.
Cui, Y., Q. Sun, I. Farrer, Y. Lee, Q. Sun, and M. Boucadair. Dynamic Allocation of Shared IPv4 Addresses. RFC Editor, August 2015. http://dx.doi.org/10.17487/rfc7618.
Kozlowski, Steve W., and Richard P. DeShon. Optimizing Dynamic Resource Allocation in Teamwork. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada478848.
Collin-Dufresne, Pierre, Kent Daniel, and Mehmet Saǧlam. Liquidity Regimes and Optimal Dynamic Asset Allocation. Cambridge, MA: National Bureau of Economic Research, January 2018. http://dx.doi.org/10.3386/w24222.
Hanna, S., B. Patel, and M. Shah. Multicast Address Dynamic Client Allocation Protocol (MADCAP). RFC Editor, December 1999. http://dx.doi.org/10.17487/rfc2730.
Kozlowski, Steve W., Richard P. DeShon, Guihyun Park, Paul Curran, Goran Kuljanin, and Brady Firth. Dynamic Resource Allocation and Adaptability in Teamwork. Fort Belvoir, VA: Defense Technical Information Center, August 2007. http://dx.doi.org/10.21236/ada475399.