Relevant bibliographies by topics / Allocation algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Allocation algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Allocation algorithms"

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Shioura, Akiyoshi, Ning Sun, and Zaifu Yang. "EFFICIENT STRATEGY PROOF FAIR ALLOCATION ALGORITHMS." Journal of the Operations Research Society of Japan 49, no. 2 (2006): 144–50. http://dx.doi.org/10.15807/jorsj.49.144.

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MARION-POTY, VIRGINIE, and SERGE MIGUET. "DATA ALLOCATION STRATEGIES FOR PARALLEL IMAGE PROCESSING ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 04 (1995): 615–34. http://dx.doi.org/10.1142/s0218001495000237.

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This paper discusses several data allocation strategies used for the parallel implementation of basic imaging operators. It shows that depending on the operator (sequential or parallel, with regular or irregular execution time), the image data must be partitioned in very different manners: The square sub-domains are best adapted for minimizing the communication volume, but rectangles can perform better when we take into account the time for constructing messages. Block allocations are well adapted for inherently parallel operators since they minimize interprocessor interactions, but in the cas
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Bolouri, S., A. Vafaeinejad, A. Alesheikh, and H. Aghamohammadi. "INVESTIGATING THE EFFECT OF CAPACITY CRITERION ON THE OPTIMAL ALLOCATION OF EMERGENCY FACILITIES IN GIS ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 211–17. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-211-2019.

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Abstract. Location-allocation analysis is one of the most GIS useful analysis, especially in allocating demands to facilities. One of these facilities is the fire stations, which the correct locations and optimal demand allocations to those have most importance. Each facility has a specific capacity that should be considered in locating the facilities and allocating the demand to those. In recent years, the use of unified models in solving allocation problems is too common because these models can solve a variety of problems, but in most of these models, the capacity criterion for facilities h
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George Fernandez, I., and J. Arokia Renjith. "Resource allocation, scheduling and auto-scaling algorithms for enhancing the performance of cloud using Grey Wolf Optimization and Fuzzy rules." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7449–67. http://dx.doi.org/10.3233/jifs-200787.

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Cloud computing technology is playing a major role in the industry and real-life, for providing fast services such as data sharing and allocating the cloud resources that are paid and truly required. In this scenario, the cloud users are scheduled according to the rule-based systems for attempting to automate the matching between computing requirements and resources. Even though, the majority auto-scaling algorithms only helped as indicators for simple resource utilization and also not considered both cloud user needs and budget concerns. For this purpose, we propose a new model which is the c
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Ma, Ding, M. Onderwater, F. Wetzels, et al. "Cost-Efficient Allocation of Additional Resources for the Service Placement Problem in Next-Generation Internet." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/517409.

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One of the major challenges in next-generation Internet is to allocate services to nodes in the network. This problem, known as theservice placement problem, can be solved by layered graph approach. However, due to the existence of resource bottleneck, the requests are rejected from the beginning in the resource constrained network. In this paper we propose two iterative algorithms for efficient allocation of additional resources in order to improve the ratio of accepted service placement requests. To this end, we (1) introduce a new concept of sensitivity for each service node to locate the b
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RAFTOPOULOU, PARASKEVI, MANOLIS KOUBARAKIS, KOSTAS STERGIOU, and PETER TRIANTAFILLOU. "FAIR RESOURCE ALLOCATION IN A SIMPLE MULTI-AGENT SETTING: SEARCH ALGORITHMS AND EXPERIMENTAL EVALUATION." International Journal on Artificial Intelligence Tools 14, no. 06 (2005): 887–99. http://dx.doi.org/10.1142/s0218213005002454.

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We study the problem of fair resource allocation in a simple cooperative multi-agent setting where we have k agents and a set of n objects to be allocated to those agents. Each object is associated with a weight represented by a positive integer or real number. We would like to allocate all objects to the agents so that each object is allocated to only one agent and the weight is distributed fairly. We adopt the fairness index popularized by the networking community as our measure of fairness, and study centralized algorithms for fair resource allocation. Based on the relationship between our
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Velarde Martinez, Apolinar. "Scheduling in Heterogeneous Distributed Computing Systems Based on Internal Structure of Parallel Tasks Graphs with Meta-Heuristics." Applied Sciences 10, no. 18 (2020): 6611. http://dx.doi.org/10.3390/app10186611.

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The problem of scheduling parallel tasks graphs (PTGs) represented by directed acyclic graphs (DAGs) in heterogeneous distributed computing systems (HDCSs) is considered an nondeterministic polynomial time (NP) problem due to the diversity of characteristics and parameters, generally opposed, intended to be optimized. The PTGs are scheduled by a scheduler that determines the best location for the sub-tasks that constitute the PTGs and is responsible for allocating the resources of the HDCS to the sub-tasks of the PTGs. To optimize scheduling and allocations, the scheduler extracts characterist
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Xiao, Mingyu, and Jiaxing Ling. "Algorithms for Manipulating Sequential Allocation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 2302–9. http://dx.doi.org/10.1609/aaai.v34i02.5608.

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Sequential allocation is a simple and widely studied mechanism to allocate indivisible items in turns to agents according to a pre-specified picking sequence of agents. At each turn, the current agent in the picking sequence picks its most preferred item among all items having not been allocated yet. This problem is well-known to be not strategyproof, i.e., an agent may get more utility by reporting an untruthful preference ranking of items. It arises the problem: how to find the best response of an agent? It is known that this problem is polynomially solvable for only two agents and NP-comple
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Shinoda, Ailton A., and Michel D. Yacoub. "New Dynamic Channel Allocation Algorithms." Journal of Communication and Information Systems 13, no. 1 (1998): 45–48. http://dx.doi.org/10.14209/jcis.1998.14.

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Luque, E., A. Ripoll, and T. Diez. "Heuristic algorithms for register allocation." IEE Proceedings E Computers and Digital Techniques 139, no. 1 (1992): 73. http://dx.doi.org/10.1049/ip-e.1992.0010.

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Dissertations / Theses on the topic "Allocation algorithms"

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Schor, James E. (James Edward). "Efficient algorithms for buffer allocation." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11458.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (leaves 127-131).<br>by James E. Schor.<br>M.S.
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Tripathi, Pushkar. "Allocation problems with partial information." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44789.

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Allocation problems have been central to the development of the theory of algorithms and also find applications in several realms of computer science and economics. In this thesis we initiate a systematic study of these problems in situations with limited information. Towards this end we explore several modes by which data may be obfuscated from the algorithm. We begin by investigating temporal constraints where data is revealed to the algorithm over time. Concretely, we consider the online bipartite matching problem in the unknown distribution model and present the first algorithm that brea
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Munshi, Siddharth. "Faster algorithms for stable allocation problems." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1193080488/.

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Ku, May. "Resource allocation algorithms in AON network." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/35956.

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Turan, Ayse. "Algorithms For The Weapon - Target Allocation Problem." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614452/index.pdf.

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Within the air defense domain, the Weapon-Target Allocation problem is a fundamental problem. This problem deals with the allocation of a set ofiring units or weapons to a set of hostile targets so that the total expected effect on targets is maximized. The Weapon-Target Allocation problem has been proven to be NP-Complete by Lloyd and Witsenhausen [14]. In this thesis, the use of various algorithms including search algorithms, maximum marginal return algorithms, evolutionary algorithms and bipartite graph matching algorithms are demonstrated to solve the problem. Algorithms from the literatu
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Hojati, Mehran. "Network synthesis problem : cost allocation and algorithms." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/27318.

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This thesis is concerned with a network design problem which is referred to in the literature as the network synthesis problem. The objective is to design an undirected network, at a minimum cost, which satisfies known requirements, i.e., lower bounds on the maximum flows, between every pair of nodes. If the requirements are to be satisfied nonsimultaneously, i.e., one at a time, the problem is called the nonsimultaneous network synthesis problem, whereas if the requirements are to be satisfied simultaneously, the problem is called the simultaneous network synthesis problem. The total constru
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Leung, Christopher. "Distributed resource allocation algorithms for digital subscriber lines." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103633.

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The Digital Subscriber Line (DSL) environment is characterized by highly frequency-selective attenuation and by potentially large crosstalk between users. DSL resource allocation algorithms allow for an efficient use of the DSL network by managing the crosstalk-induced interference while also taking the frequency-selectivity into account. Previous resource allocation algorithms were based on worst-case situations. The more recent Dynamic Spectrum Management (DSM) resource allocation algorithms are able to constantly adapt to the channel characteristics. At one end of the DSM algorithms, there
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Al-Janabi, Muayad Sadik Croock. "Resource allocation software algorithms for AMC-OFDM systems." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1537.

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In recent years, adaptive modulation and coding (AMC) technologies, resource allocation strategies and user scheduling for single-cell downlink orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) systems have been widely researched in order to ensure that capacity and throughput are maximised. In terms of AMC technologies, the correlation between the channel coefficients corresponding to the transmitted sub-carriers has not been considered yet. In the literature of resource allocation and user scheduling, either channel coding is not cons
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Chung, Jong-Sun. "Fast Power Allocation Algorithms for Adaptive MIMO Systems." Thesis, University of Canterbury. Electrical and Computer Engineering, 2009. http://hdl.handle.net/10092/3764.

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Recent research results have shown that the MIMO wireless communication architecture is a promising approach to achieve high bandwidth efficiencies. MIMO wireless channels can be simply defined as a link for which both the transmitting and receiving ends are equipped with multiple antenna elements. Adaptive modulation and power allocation could be used to further improve the performance of MIMO systems. This thesis focuses on developing a fast and high performance power allocation algorithm. Three power allocation algorithms are proposed in this thesis and their performances are compared in
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Rohwedder, Lars [Verfasser]. "Algorithms for Integer Programming and Allocation / Lars Rohwedder." Kiel : Universitätsbibliothek Kiel, 2019. http://d-nb.info/1196090823/34.

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Books on the topic "Allocation algorithms"

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Labbe, Martine. Approximation algorithms for the capacitated plant allocation problem. European Institute for Advanced Studies in Management, 1992.

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Hummel, Robert A. Dynamic processor allocation for parallel algorithms in image processing. Courant Institute of Mathematical Sciences, New York University, 1987.

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Kelly, John Clive. OS-1100 performance algorithms: A guide to the resource allocation algorithms of OS-1100. Datametrics Systems Corp., 1985.

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Corless, Martin J. AIMD dynamics and distributed resource allocation. Society for Industrial and Applied Mathematics, 2016.

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Stańczak, Sławomir. Fundamentals of resource allocation in wireless networks: Theory and algorithms. 2nd ed. Springer, 2009.

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Basta, T. Studies in distribution planning and operations. UMIST, 1993.

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Kazmi, Qasim D. The seat allocation algorithm. University of Manchester, Department of Computer Science., 1995.

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Naoki, Katoh, ed. Resource allocation problems: Algorithmic approaches. MIT Press, 1988.

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Marathe, Shailesh. Using local copy services: How the veritas storage foundation helps you get the most from online storage. Symantec, 2006.

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O'Brien, F. A. The equitable partitioning problem: A heuristic algorithm applied to the allocation of university student accomodation. University of Warwick. Warwick Business School Research Bureau, 1995.

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Book chapters on the topic "Allocation algorithms"

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Bar-Ilan, Judit, and David Peleg. "Distributed resource allocation algorithms." In Distributed Algorithms. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56188-9_19.

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Chakrabarty, Deeparnab. "Max-Min Allocation." In Encyclopedia of Algorithms. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_539.

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Chakrabarty, Deeparnab. "Max-Min Allocation." In Encyclopedia of Algorithms. Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_539-1.

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Kao, Ming-Yang. "Rate Adjustment and Allocation." In Encyclopedia of Algorithms. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_333.

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Abraham, David J., Robert W. Irving, and David F. Manlove. "The Student-Project Allocation Problem." In Algorithms and Computation. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24587-2_49.

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Attiya, Hagit, Hadas Shachnai, and Tami Tamir. "Local labeling and resource allocation using preprocessing." In Distributed Algorithms. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/bfb0020434.

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Nouyan, Shervin. "Agent-Based Approach to Dynamic Task Allocation." In Ant Algorithms. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45724-0_3.

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Raynal, Michel. "Distributed Resource Allocation." In Distributed Algorithms for Message-Passing Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38123-2_11.

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Xu, Shihong, and Hong Shen. "The Fault-Tolerant Facility Allocation Problem." In Algorithms and Computation. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10631-6_70.

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Abraham, David J., Katarína Cechlárová, David F. Manlove, and Kurt Mehlhorn. "Pareto Optimality in House Allocation Problems." In Algorithms and Computation. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11602613_115.

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Conference papers on the topic "Allocation algorithms"

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Bei, Xiaohui, Youming Qiao, and Shengyu Zhang. "Networked Fairness in Cake Cutting." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/508.

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We introduce a graphical framework for fair division in cake cutting, where comparisons between agents are limited by an underlying network structure. We generalize the classical fairness notions of envy-freeness and proportionality in this graphical setting. An allocation is called envy-free on a graph if no agent envies any of her neighbor's share, and is called proportional on a graph if every agent values her own share no less than the average among her neighbors, with respect to her own measure. These generalizations enable new research directions in developing simple and efficient algori
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Tang, Shaojie. "Robust Advertisement Allocation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/617.

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With the rapid growth of e-commerce and World Wide Web, internet advertising revenue has surpassed broadcast revenue very recently. As online advertising has become a major source of revenue for online publishers, such as Google and Amazon, one problem facing them is to optimize the ads selection and allocation in order to maximize their revenue. Although there is a rich body of work that has been devoted to this field, uncertainty about models and parameter settings is largely ignored in existing algorithm design. To fill this gap, we are the first to formulate and study the \emph{Robust Ad A
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Siraichi, Marcos Yukio, Fernando Magno Quintão Pereira, Vinicius Dos Santos, and Caroline Collange. "Qubit Allocation." In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2020.11368.

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The availability of the first prototypes of quantum computers, in 2016, with free access through the cloud, brought much enthusiasm to the research community. Yet, programming said computers is difficult. One core challenge is the so called qubit allocation problem. This problem consists in mapping the virtual qubits that make up a logical quantum program onto the physical qubits that exist in the target quantum architecture. To deal with this challenge, we have proposed one of the first algorithms to solve qubit allocation. This algorithm, together with its ensuing formulations, is today avai
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Aziz, Haris, Hau Chan, and Bo Li. "Weighted Maxmin Fair Share Allocation of Indivisible Chores." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/7.

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We initiate the study of indivisible chore allocation for agents with asymmetric shares. The fairness concept we focus on is the weighted natural generalization of maxmin share: WMMS fairness and OWMMS fairness. We first highlight the fact that commonly-used algorithms that work well for allocation of goods to asymmetric agents, and even for chores to symmetric agents do not provide good approximations for allocation of chores to asymmetric agents under WMMS. As a consequence, we present a novel polynomial-time constant-approximation algorithm, via linear program, for OWMMS. For two special ca
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Petersen, John, and Marc Bodson. "Interior-Point Algorithms for Control Allocation." In AIAA Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-5566.

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Iyengar, Arun, Shudong Jin, and Jim Challenger. "Efficient Algorithms for Persistent Storage Allocation." In 2001 Eighteenth IEEE Symposium on Mass Storage Systems and Technologies (MSS). IEEE, 2001. http://dx.doi.org/10.1109/mss.2001.10009.

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Styer, Eugene, and Gary L. Peterson. "Improved algorithms for distributed resource allocation." In the seventh annual ACM Symposium. ACM Press, 1988. http://dx.doi.org/10.1145/62546.62567.

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An, Bo. "Game Theoretic Analysis of Security and Sustainability." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/732.

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Computational game theory has become a powerful tool to address critical issues in security and sustainability. Casting the security resource allocation problem as a Stackelberg game, novel algorithms have been developed to provide randomized security resource allocations. These algorithms have led to deployed security-game based decision aids for many real-world security domains including infrastructure security and wildlife protection. We contribute to this community by addressing several major research challenges in complex security resource allocation, including dynamic payoffs, uncertaint
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Grobler, Jacomine, Andries P. Engelbrecht, Graham Kendall, and V. S. S. Yadavalli. "Multi-method algorithms: Investigating the entity-to-algorithm allocation problem." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557619.

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Baun, Ward O. "Optimization of Component Reliability Allocation in a Complex Repairable System Using a System Total Reliability Risk Metric." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12826.

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The task of allocating failure rates to components within a complex repairable system is executed early in a product development process in order to set reliability targets for those components. This allocation process is often accomplished versus more than one constraint, for instance to achieve an overall system-level failure rate, λsys, and to achieve an overall system life cycle unplanned maintenance cost (LCUMC) target. Presumably, there exists an optimum component allocation solution that would most effectively meet those goals, while minimizing risk to the ultimate product. In this cont
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Reports on the topic "Allocation algorithms"

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Pruhs, Kirk, and Bala Kalyanasundaram. Dynamic Spectrum Allocation Algorithms. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada420598.

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Kalyanasundaram, Bala, and Kirk Pruhs. Dynamic Spectrum Allocation Algorithms. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada387970.

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Kalyanasundaram, Bala. Dynamic Spectrum Allocation Algorithms. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada418179.

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Ur, Shmuel. Analysis of Online Algorithms for Organ Allocation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada249361.

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Fecko, Mariusz, Kirk Chang, Andrzej Cichocki, et al. Dynamic Capacity Allocation Algorithms for iNET Link Manager. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada623310.

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Luo, Zhi-Quan. Optimization Algorithms and Equilibrium Analysis for Dynamic Resource Allocation. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada565198.

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Pang, Jong-Shi. Optimization Algorithms and Equilibrium Analysis for Dynamic Resource Allocation. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada577088.

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Page, Anthony B., and Marc L. Steinberg. Effects of Control Allocation Algorithms on a Nonlinear Adaptive Design. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada368467.

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Luo, Zhi-Quan. Complexity Analysis and Algorithms for Optimal Resource Allocation in Wireless Networks. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada579191.

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Gidas, Basilis, and Sumit Ghosh. Algorithms for Image Compression, Distributed Communication Networks and Distributed Resource Allocation. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada357863.

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