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Journal articles on the topic 'Allocation algorithms'

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Published: 4 June 2021

Last updated: 11 February 2022

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1

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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2

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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8

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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9

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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10

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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11

Ellis, Carla Schlatter, and Thomas J. Olson. "Algorithms for parallel memory allocation." International Journal of Parallel Programming 17, no. 4 (1988): 303–45. http://dx.doi.org/10.1007/bf01407909.

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12

Saeidian, Bahram, Mohammad Saadi Mesgari, Biswajeet Pradhan, and Abdullah M. Alamri. "Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms." Water 11, no. 12 (2019): 2611. http://dx.doi.org/10.3390/w11122611.

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The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cul

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13

Wang, Heng, Aijun Liu, and Xiaofei Pan. "Optimization of Joint Power and Bandwidth Allocation in Multi-Spot-Beam Satellite Communication Systems." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/683604.

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Multi-spot-beam technique has been widely applied in modern satellite communication systems. However, the satellite power and bandwidth resources in a multi-spot-beam satellite communication system are scarce and expensive; it is urgent to utilize the resources efficiently. To this end, dynamically allocating the power and bandwidth is an available way. This paper initially formulates the problem of resource joint allocation as a convex optimization problem, taking into account a compromise between the maximum total system capacity and the fairness among the spot beams. A joint bandwidth and p

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14

He, Zhong Tang, Xiao Qing Zhang, Tong Kai Ji, and Zhi Wei Xu. "Energy Efficient Virtual Machine Allocation in Cloud." Applied Mechanics and Materials 389 (August 2013): 887–90. http://dx.doi.org/10.4028/www.scientific.net/amm.389.887.

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Cloud data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. When virtualized resources of cloud data centers are allocated, the energy consumption is a problem to be considered necessarily. In this paper, three energy efficient virtual machine allocation algorithms are carried on experimental analysis, including MAX, DVFS and dynamic deployment algorithm (DDA). We have evaluated these algorithms by conducting a performance evaluation study using the CloudSim toolkit. The experimental r

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15

Goldberg, Paul W., Alexandros Hollender, and Warut Suksompong. "Contiguous Cake Cutting: Hardness Results and Approximation Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1990–97. http://dx.doi.org/10.1609/aaai.v34i02.5570.

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We study the fair allocation of a cake, which serves as a metaphor for a divisible resource, under the requirement that each agent should receive a contiguous piece of the cake. While it is known that no finite envy-free algorithm exists in this setting, we exhibit efficient algorithms that produce allocations with low envy among the agents. We then establish NP-hardness results for various decision problems on the existence of envy-free allocations, such as when we fix the ordering of the agents or constrain the positions of certain cuts. In addition, we consider a discretized setting where i

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16

Sangaiah, Arun Kumar, Ali Asghar Rahmani Hosseinabadi, Morteza Babazadeh Shareh, Seyed Yaser Bozorgi Rad, Atekeh Zolfagharian, and Naveen Chilamkurti. "IoT Resource Allocation and Optimization Based on Heuristic Algorithm." Sensors 20, no. 2 (2020): 539. http://dx.doi.org/10.3390/s20020539.

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The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, t

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17

Sikora, Pavel, Tomas Horvath, Petr Munster, and Vaclav Oujezsky. "Efficiency Tests of DBA Algorithms in XG-PON." Electronics 8, no. 7 (2019): 762. http://dx.doi.org/10.3390/electronics8070762.

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Sophisticated dynamic bandwidth allocation (DBA) algorithms can dramatically improve or worsen throughput and delay in whole networks. It is very important to choose the right DBA algorithm. Our work tests static assignment and three DBA algorithms, namely GigaPON Access Network DBA, Hybrid Reporting Allocation, and modified Max–Min Fair. All tests were made on our simulator of ten-gigabit passive optical network DBA specially developed for testing DBA algorithms. The tests verify delay of each optical network unit and amount of waste with bandwidth. This paper describes how the used DBA algor

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18

Manekar, A. S., and Dr Pradeepini Gera. "Optimize Task Scheduling and Resource Allocation Using Nature Inspired Algorithms in Cloud based BDA." Webology 18, Special Issue 01 (2021): 127–36. http://dx.doi.org/10.14704/web/v18si01/web18049.

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Task Scheduling and Resource allocation is a prominent research topic in cloud computing. There are several objectives associated with Optimize Task Scheduling and Resource allocation as cloud computing systems are more complex than the traditional distributed system. There are several challenges like resolving the task mapped to the node on which task to be executed. A simplified but near optimal proposed nature inspired algorithms are focus in this paper. In this paper basic idea about optimization, reliability and complexity is considered while design a solution for modern BDA (Big Data App

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19

Russo, Daniel. "Simple Bayesian Algorithms for Best-Arm Identification." Operations Research 68, no. 6 (2020): 1625–47. http://dx.doi.org/10.1287/opre.2019.1911.

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This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure and observes noisy signals of their quality with the goal of confidently identifying the best design after a small number of measurements. Just as the multiarmed bandit problem crystallizes the tradeoff between exploration and exploitation, this “pure exploration” variant crystallizes the challenge of rapidly gathering information before committing to a final decision. The paper proposes several sim

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20

Petersen, John A. M., and Marc Bodson. "Interior-Point Algorithms for Control Allocation." Journal of Guidance, Control, and Dynamics 28, no. 3 (2005): 471–80. http://dx.doi.org/10.2514/1.5937.

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21

Struchenkov, V. I. "NEW ALGORITHMS FOR OPTIMAL RESOURSE ALLOCATION." Prikladnaya diskretnaya matematika, no. 10 (December 1, 2010): 73–78. http://dx.doi.org/10.17223/20710410/10/8.

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22

SAWA, Takaaki, Fujun HE, Akio KAWABATA, and Eiji OKI. "Algorithms for Distributed Server Allocation Problem." IEICE Transactions on Communications E103.B, no. 11 (2020): 1341–52. http://dx.doi.org/10.1587/transcom.2020ebp3006.

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23

Farias, Vivek F., and Benjamin Van Roy. "Approximation algorithms for dynamic resource allocation." Operations Research Letters 34, no. 2 (2006): 180–90. http://dx.doi.org/10.1016/j.orl.2005.02.006.

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24

Murray, S., J. Yu, R. A. Wolfe, et al. "Risk/benefit-based lung allocation algorithms." Journal of Heart and Lung Transplantation 22, no. 1 (2003): S128—S129. http://dx.doi.org/10.1016/s1053-2498(02)00864-1.

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25

Struchenkov, Valery I. "Combined Algorithms of Optimal Resource Allocation." Applied Mathematics 03, no. 01 (2012): 78–85. http://dx.doi.org/10.4236/am.2012.31013.

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26

Burn, Donald H., and Jeanne S. Yulianti. "Waste-Load Allocation Using Genetic Algorithms." Journal of Water Resources Planning and Management 127, no. 2 (2001): 121–29. http://dx.doi.org/10.1061/(asce)0733-9496(2001)127:2(121).

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27

Page, I., T. Jacob, and E. Chern. "Fast algorithms for distributed resource allocation." IEEE Transactions on Parallel and Distributed Systems 4, no. 2 (1993): 188–97. http://dx.doi.org/10.1109/71.207594.

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28

Chaudhuri, Kamalika, Anshul Kothari, Rudi Pendavingh, Ram Swaminathan, Robert Tarjan, and Yunhong Zhou. "Server Allocation Algorithms for Tiered Systems." Algorithmica 48, no. 2 (2007): 129–46. http://dx.doi.org/10.1007/s00453-007-0052-0.

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29

Dean, Brian C., and Siddharth Munshi. "Faster Algorithms for Stable Allocation Problems." Algorithmica 58, no. 1 (2010): 59–81. http://dx.doi.org/10.1007/s00453-010-9416-y.

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30

Adany, Ron, Sarit Kraus, and Fernando Ordonez. "Allocation algorithms for personal TV advertisements." Multimedia Systems 19, no. 2 (2012): 79–93. http://dx.doi.org/10.1007/s00530-012-0284-y.

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31

Bergamaschi, Reinaldo A., Raul Camposano, and Michael Payer. "Allocation algorithms based on path analysis." Integration 13, no. 3 (1992): 283–99. http://dx.doi.org/10.1016/0167-9260(92)90032-t.

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32

Koufos, Konstantinos, and Riku Jäntti. "Proportional Fair Power Allocation for Secondary Transmitters in the TV White Space." Journal of Electrical and Computer Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/272341.

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The key bottleneck for secondary spectrum usage is the aggregate interference to the primary system receivers due to simultaneous secondary transmissions. Existing power allocation algorithms for multiple secondary transmitters in the TV white space either fail to protect the TV service in all cases or they allocate extremely low power levels to some of the transmitters. In this paper, we propose a power allocation algorithm that favors equally the secondary transmitters and it is able to protect the TV service in all cases. When the number of secondary transmitters is high, the computational

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33

Bibi, Nazia, Zeeshan Anwar, and Ali Ahsan. "Comparison of Search-Based Software Engineering Algorithms for Resource Allocation Optimization." Journal of Intelligent Systems 25, no. 4 (2016): 629–42. http://dx.doi.org/10.1515/jisys-2015-0016.

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AbstractA project manager balances the resource allocation using resource leveling algorithms after assigning resources to project activities. However, resource leveling does not ensure optimized allocation of resources. Furthermore, the duration and cost of a project may increase after leveling resources. The objectives of resource allocation optimization used in our research are to (i) increase resource utilization, (ii) decrease project cost, and (iii) decrease project duration. We implemented three search-based software engineering algorithms, i.e. multiobjective genetic algorithm, multiob

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34

Koné, Yacouba Adama, Jacqueline Konaté, Oumar Y. Maïga, and Hamidou Tembiné. "Efficient Strategies Algorithms for Resource Allocation Problems." Algorithms 13, no. 11 (2020): 270. http://dx.doi.org/10.3390/a13110270.

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Strategic modelling with a panoramic view plays an important role in decision-making problems. It offers the possibility of generating different solutions before making a decision. This is particularly relevant in critical situations. This article addresses the problem of allocating resources, whether financial, material or human, so that it is optimal under a given set of constraints and inter-dependencies with other systems. To do this, existing strategies such as those of Colonel Blotto are studied in order to evaluate them according to some criteria, including the heterogeneity or homogene

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Jian, Wu, Liu Qingguo, Liu Xinxue, and Li Yaxiong. "Fuel-Efficient on-Orbit Service Vehicle Allocation Based on an Improved Discrete Particle Swarm Optimization Algorithm." Mathematical Problems in Engineering 2020 (April 15, 2020): 1–14. http://dx.doi.org/10.1155/2020/5683698.

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Given the limited fuel capacity of an on-orbit service vehicle (OSV), proper OSV allocation to satellites during each service mission is critical for economic fuel consumption. This allocation problem can be formulated as an optimization problem with many continuous and discrete design variables of wide domains. This problem can be effectively handled through the proposed approach that combines the tabu search with the discrete particle swarm optimization algorithm (DPSO-TS). First of all, Pontryagin’s minimum principle and genetic algorithm (GA) are exploited to find the most fuel-efficient t

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Al-Sharif, Lutfi, Ahmad Hammoudeh, and Jannat Al-Saidi. "Analysis and comparison of the two sectoring approaches in elevator traffic systems." Building Services Engineering Research and Technology 40, no. 5 (2019): 611–26. http://dx.doi.org/10.1177/0143624419826869.

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Sectoring is a group control algorithm that is used in elevator traffic control systems by grouping passengers that have common destinations or common origins into elevator cars that serve these floors. The building is split into sectors usually comprising contiguous floors. Two different alternative algorithms for sectoring are discussed in this paper. The first approach is based on dynamic allocation with equal sector allocation. The second approach is based on static allocation with unequal sector sizes. Under static allocation, the same elevator car is allocated to the same sector in every

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37

De-Graft Acquah, Henry, and Sarah Acquah. "Interior point algorithm for solving farm resource allocation problem." Applied Studies in Agribusiness and Commerce 11, no. 1-2 (2017): 45–49. http://dx.doi.org/10.19041/apstract/2017/1-2/6.

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This paper introduces interior point algorithm as an alternative approach to simplex algorithm for solving farm resource allocation problem. The empirical result of interior point algorithm is compared with that of the simplex algorithm. It goes further to address a profit maximization problem. The result revealed several relevant patterns. Results of the interior point algorithm is similar to that of the simplex algorithm. Findings indicated that in both algorithms, the farm is to produce peppers, wheat which is irrigated and weeded manually, hire additional month of labour, and also purchase

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38

Zhao, Long, Wei Xiang, Jie Mei, Hui Zhao, Hang Long, and Lin Li. "Energy-Efficient Dual-Iteration Power Allocation for Two-Phase Relay System with Massive Antennas." International Journal of Antennas and Propagation 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/543826.

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This paper considers the scenario where multiple source nodes communicate with multiple destination nodes simultaneously with the aid of an amplify-and-forward relay equipped with massive antennas. In order to achieve optimal energy efficiency (EE) of the entire relay system, this paper investigates the power allocation problem for the multiple pairs of nodes at both the source nodes and the relay node, where the relay employs the backward and forward zero-forcing filters. Since the EE optimization problem cannot be solved analytically, we propose a two-phase power allocation method. Given pow

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39

Yang, Xiao Long, Lin Ma, and Xue Zhi Tan. "Power-Weight Allocation Algorithm for OFDM-Based Cognitive Radio System." Advanced Materials Research 546-547 (July 2012): 932–36. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.932.

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Cognitive Radio (CR) has been proposed as one of the most promising technologies to provide efficient utilization of the limited wireless spectrum. In this paper, we investigate the power allocation for CR system based on orthogonal frequency division multiplexing (OFDM), and an improved allocation algorithm called power-weight algorithm is proposed. This algorithm performs initial allocation via Lagrange multiplier method, and then accomplishes second allocation based on power weight obtained in initial allocation. The analytical results reveal that the total transmission rate of all subcarri

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40

Ma, Yue Huai, Bin Zhang, Wei Zhang, and Meng Xiang Xu. "Joint Subcarrier and Power Allocation for Uplink Spectrum Sharing in Cognitive OFDM Networks: A Waterfilling Based Approach." Advanced Materials Research 756-759 (September 2013): 1979–83. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1979.

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In this paper, the subcarrier and power allocation problem for the orthogonal frequency division multiplexing (OFDM) cognitive radio network which coexists with the primary network is studied. A cognitive waterfilling (CWF) power allocation algorithm which based upon the classical waterfilling mechanism is proposed for the single SU scenario. As for multi-SU case, we present an efficient joint subcarrier and power allocation algorithm called MS-CWF algorithm. Simulation results show the effectiveness of the proposed CWF and MS-CWF algorithms.

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Goldberg, Paul, Alexandros Hollender, and Warut Suksompong. "Contiguous Cake Cutting: Hardness Results and Approximation Algorithms." Journal of Artificial Intelligence Research 69 (September 22, 2020): 109–41. http://dx.doi.org/10.1613/jair.1.12222.

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We study the fair allocation of a cake, which serves as a metaphor for a divisible resource, under the requirement that each agent should receive a contiguous piece of the cake. While it is known that no finite envy-free algorithm exists in this setting, we exhibit efficient algorithms that produce allocations with low envy among the agents. We then establish NP-hardness results for various decision problems on the existence of envy-free allocations, such as when we fix the ordering of the agents or constrain the positions of certain cuts. In addition, we consider a discretized setting where i

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42

Tutelian, Sergei, Dmitry Bankov, Dmitri Shmelkin, and Evgeny Khorov. "IEEE 802.11ax OFDMA Resource Allocation with Frequency-Selective Fading." Sensors 21, no. 18 (2021): 6099. http://dx.doi.org/10.3390/s21186099.

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This paper studies the usage of orthogonal frequency division multiple access (OFDMA) for uplink transmissions in IEEE 802.11ax networks. OFDMA enables simultaneous multi-user transmissions in Wi-Fi, but its usage requires efficient resource allocation algorithms. These algorithms should be able to adapt to the changing channel conditions, including the frequency-selective fading. This paper presents an OFDMA resource allocation algorithm for channels with frequency-selective fading and proposes an approach to adapt the user transmission power and modulation and coding schemes to the varying c

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Mulati,, Mauro, Carla Lintzmayer, and Anderson Da Silva. "The Hybrid ColorAnt-RT Algorithms and an Application to Register Allocation." Inteligencia Artificial 18, no. 55 (2015): 81. http://dx.doi.org/10.4114/intartif.vol18iss55pp81-111.

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Ant Colony Optimization is a metaheuristic used to create heuristic algorithms to find good solutions for combinatorial optimization problems. This metaheuristic is inspired on the effective behavior present in some species of ants of exploring the environment to find and transport food to the nest. Several works have proposed using Ant Colony Optimization algorithms to solve problems such as vehicle routing, frequency assignment, scheduling and graph coloring. The graph coloring problem essentially consists in finding a number k of colors to assign to the vertices of a graph, so that there ar

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Sun, Fengjie, Xianchang Wang, and Rui Zhang. "Fair Task Allocation When Cost of Task Is Multidimensional." Applied Sciences 10, no. 8 (2020): 2798. http://dx.doi.org/10.3390/app10082798.

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We consider the problem of fairly allocating indivisible tasks, focusing on a recently introduced notion of fairness called Minmax share guarantee. Minmax share (MMS) is a term of fairness guarantees that is defined to be the minimum cost that an agent can ensure for herself, if she were to partition the tasks into n bundles, and then receive the maximum cost bundle of tasks. However, the cost of tasks considered in previous work is single dimensional, and multidimensional situations have not been researched. In this work, we proposed an allocation algorithm that allocates tasks with multidime

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45

Du, Peng, and Yuan Zhang. "Joint Radio Resource Allocation and Base Station Location Selection in OFDMA Based Private Wireless Access Networks for Smart Grid." Journal of Electrical and Computer Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7948018.

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This paper studies the base stations deployment problem in orthogonal frequency-division multiple access (OFDMA) based private wireless access networks for smart grid (SG). Firstly, we analyze the differences between private wireless access networks for SG and public cellular access networks. Then, we propose scheduling and power control based algorithms for the radio resource allocation subproblem and K-means, simulated annealing (SA), and particle swarm optimization (PSO) based algorithms for the base station (BS) location selection subproblem and iterate over these two sets of algorithms to

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46

Liu, Gang, and Fang Li. "A New Testability Optimization Allocation Approach." Applied Mechanics and Materials 328 (June 2013): 444–49. http://dx.doi.org/10.4028/www.scientific.net/amm.328.444.

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This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimiz

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Hsu, Ivy, and Jean Walrand. "Dynamic bandwidth allocation for ATM switches." Journal of Applied Probability 33, no. 3 (1996): 758–71. http://dx.doi.org/10.2307/3215357.

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We explore a dynamic approach to the problems of call admission and resource allocation for communication networks with connections that are differentiated by their quality of service requirements. In a dynamic approach, the amount of spare resources is estimated on-line based on feedbacks from the network's quality of service monitoring mechanism. The schemes we propose remove the dependence on accurate traffic models and thus simplify the tasks of supplying traffic statistics required of network users. In this paper we present two dynamic algorithms. The objective of these algorithms is to f

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48

Hsu, Ivy, and Jean Walrand. "Dynamic bandwidth allocation for ATM switches." Journal of Applied Probability 33, no. 03 (1996): 758–71. http://dx.doi.org/10.1017/s0021900200100191.

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Abstract:

We explore a dynamic approach to the problems of call admission and resource allocation for communication networks with connections that are differentiated by their quality of service requirements. In a dynamic approach, the amount of spare resources is estimated on-line based on feedbacks from the network's quality of service monitoring mechanism. The schemes we propose remove the dependence on accurate traffic models and thus simplify the tasks of supplying traffic statistics required of network users. In this paper we present two dynamic algorithms. The objective of these algorithms is to f

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49

Liu, Jing Xue, Zhi Bo Li, and Hong Li. "Dynamic Allocation of Radio Frequency in the Battlefield Environment." Applied Mechanics and Materials 197 (September 2012): 496–501. http://dx.doi.org/10.4028/www.scientific.net/amm.197.496.

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Dynamic allocation of Radio Frequency (Hereafter called “RF”) is critical in the battlefield spectrum management. The article analyzes the conventional method of RF dynamic allocation in the battlefield environment, and set up the mathematic model of the RF dynamic allocation by using the results of spectrum detected in battlefield. It designs the algorithms with the combination of Genetic Algorithms and Tabu Search Algorithms. The simulation experiment proves the high efficiency of hybrid algorithms and it suit for solving the RF dynamic allocation problem in the battlefield environment.

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Shakil, Kashish Ara, Mansaf Alam, and Samiya Khan. "A latency-aware max-min algorithm for resource allocation in cloud." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 671. http://dx.doi.org/10.11591/ijece.v11i1.pp671-685.

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Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This pap

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