Journal articles: 'Resource allocation model'

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Wang, Yanyan, and Baiqing Sun. "A Multiobjective Allocation Model for Emergency Resources That Balance Efficiency and Fairness." Mathematical Problems in Engineering 2018 (October 14, 2018): 1–8. http://dx.doi.org/10.1155/2018/7943498.

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Efficiency and fairness are two important goals of disaster rescue. However, the existing models usually unilaterally consider the efficiency or fairness of resource allocation. Based on this, a multiobjective emergency resource allocation model that can balance efficiency and fairness is proposed. The object of the proposed model is to minimize the total allocating costs of resources and the total losses caused by insufficient resources. Then the particle swarm optimization is applied to solve the model. Finally, a computational example is conducted based on the emergency relief resource allocation after Ya’an earthquake in China to verify the applicability of the proposed model.

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Sathish, Kuppani, and A. Rama Mohan Reddy. "Resource Allocation Mechanism with New Models for Grid Environment." International Journal of Grid and High Performance Computing 5, no. 2 (April 2013): 1–26. http://dx.doi.org/10.4018/jghpc.2013040101.

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Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

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Kaushik, Achal, and Deo Prakash Vidyarthi. "Green Energy Model for Grid Resource Allocation." International Journal of Grid and High Performance Computing 6, no. 2 (April 2014): 52–73. http://dx.doi.org/10.4018/ijghpc.2014040104.

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The computational grid helps in faster execution of compute intensive jobs. Many characteristic parameters are intended to be optimized while making resource allocation for job execution in computational grid. Most often, the green energy aspect, in which one tries for better energy utilization, is ignored while allocating the grid resources to the jobs. The conventional systems, which propose energy efficient scheduling strategies, ignore other Quality of Service parameters while scheduling the jobs. The proposed work tries to optimize the energy in resource allocation to make it a green energy model. It explores how effectively the jobs submitted to the grid can be executed for optimal energy uses making no compromise on other desired related characteristic parameters. A graph theoretic model has been developed for this purpose. The performance study of the proposed green energy model has been experimentally evaluated by simulation. The result reveals the benefits and gives an insight for an energy efficient resource allocation.

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Wang, Shi-long, Zhe-qi Zhu, and Ling Kang. "Resource allocation model in cloud manufacturing." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 10 (April 9, 2015): 1726–41. http://dx.doi.org/10.1177/0954406215582016.

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Allocation of resources in cloud manufacturing is one of the key points of cloud manufacturing technology. To optimize cloud manufacturing resource management, it is indispensable to improve the process and efficiency of scheduling by matching jobs with resources according to the size of the job and establishing a four-level structure for resources based on the enterprise level, workshop level, primitive cell and service level. A resource scheduling model containing four indicators of cost, time, quality and risk with their own mathematical expressions is proposed. We also simulate the model with a new swap-shuffled leap-frog algorithm (SSFLA). Finally, we test the algorithm with different example scales and different end conditions and compare it with particle swarm optimization (PSO) and genetic algorithm (GA). The result shows that SSFLA performs well in convergence speed and robustness and does much better than PSO and GA. This algorithm provides an alternative choice for allocation of resources in cloud manufacturing model.

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Pujiyanta, Ardi, Lukito Edi Nugroho, and Widyawan Widyawan. "Resource allocation model for grid computing environment." International Journal of Advances in Intelligent Informatics 6, no. 2 (July 12, 2020): 185. http://dx.doi.org/10.26555/ijain.v6i2.496.

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Grid computing is a collection of heterogeneous resources that is highly dynamic and unpredictable. It is typically used for solving scientific or technical problems that require a large number of computer processing cycles or access to substantial amounts of data. Various resource allocation strategies have been used to make resource use more productive, with subsequent distributed environmental performance increases. The user sends a job by providing a predetermined time limit for running that job. Then, the scheduler gives priority to work according to the request and scheduling policy and places it in the waiting queue. When the resource is released, the scheduler selects the job from the waiting queue with a specific algorithm. Requests will be rejected if the required resources are not available. The user can re-submit a new request by modifying the parameter until available resources can be found. Eventually, there is a decrease in idle resources between work and resource utilization, and the waiting time will increase. An effective scheduling policy is required to improve resource use and reduce waiting times. In this paper, the FCFS-LRH method is proposed, where jobs received will be sorted by arrival time, execution time, and the number of resources needed. After the sorting process, the work will be placed in a logical view, and the job will be sent to the actual resource when it executes. The experimental results show that the proposed model can increase resource utilization by 1.34% and reduce waiting time by 20.47% when compared to existing approaches. This finding could be beneficially implemented in cloud systems resource allocation management.

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Vijayaraj, N., and T. Senthil Murugan. "Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 1008–19. http://dx.doi.org/10.2174/2213275912666190404160733.

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Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

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BINTZ, JASON, and SUZANNE LENHART. "OPTIMAL RESOURCE ALLOCATION FOR A DIFFUSIVE POPULATION MODEL." Journal of Biological Systems 28, no. 04 (December 2020): 945–76. http://dx.doi.org/10.1142/s0218339020500230.

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The spatial distribution of resources for diffusive populations can have a strong effect on population abundance. We investigate the optimal allocation of resources for a diffusive population. Population dynamics are represented by a parabolic partial differential equation with density-dependent growth and resources are represented through their space- and time-varying influence on the growth function. We consider both local and integral constraints on resource allocation. The goal is to maximize the abundance of the population while minimizing the cost of resource allocation. After characterizing the optimal control in terms of the population solution and the adjoint functions, we illustrate several scenarios numerically. The effects of initial and boundary conditions are important for the optimal allocation of resources.

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BINTZ, JASON, and SUZANNE LENHART. "OPTIMAL RESOURCE ALLOCATION FOR A DIFFUSIVE POPULATION MODEL." Journal of Biological Systems 28, no. 04 (December 2020): 945–76. http://dx.doi.org/10.1142/s0218339020500230.

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The spatial distribution of resources for diffusive populations can have a strong effect on population abundance. We investigate the optimal allocation of resources for a diffusive population. Population dynamics are represented by a parabolic partial differential equation with density-dependent growth and resources are represented through their space- and time-varying influence on the growth function. We consider both local and integral constraints on resource allocation. The goal is to maximize the abundance of the population while minimizing the cost of resource allocation. After characterizing the optimal control in terms of the population solution and the adjoint functions, we illustrate several scenarios numerically. The effects of initial and boundary conditions are important for the optimal allocation of resources.

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Manzoor, Muhammad Faraz, Adnan Abid, Muhammad Shoaib Farooq, Naeem A. Azam, and Uzma Farooq. "Resource Allocation Techniques in Cloud Computing: A Review and Future Directions." Elektronika ir Elektrotechnika 26, no. 6 (December 18, 2020): 40–51. http://dx.doi.org/10.5755/j01.eie.26.6.25865.

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Cloud computing has become a very important computing model to process data and execute computationally concentrated applications in pay-per-use method. Resource allocation is a process in which the resources are allocated to consumers by cloud providers based on their flexible requirements. As the data is expanding every day, allocating resources efficiently according to the consumer demand has also become very important, keeping Service Level Agreement (SLA) between service providers and consumers in prospect. This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. In the light of the uniqueness of the models and techniques, the main aim of the resource allocation is to limit the overhead/expenses associated with it. This research aims to present a comprehensive, structured literature review on different aspects of resource allocation in cloud computing, including strategic, target resources, optimization, scheduling and power. More than 50 articles, between year 2007 and 2019, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and they are reviewed under clearly defined objectives. It presents a topical taxonomy of resource allocation dimensions, and articles under each category are discussed and analysed. Lastly, salient future directions in this area are discussed.

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Krishna, Siva Rama, and Mohammed Ali Hussain. "An Efficient Multi-Core Resource Allocation using the Multi-Level Objective Functions in Cloud Environment." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 957–64. http://dx.doi.org/10.2174/2666255813666200213105651.

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Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.

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Bliss, Tony, Jagadish Guria, Wayne Jones, and Nigel Rockliffe. "A road safety resource allocation model." Transport Reviews 19, no. 4 (January 1999): 291–303. http://dx.doi.org/10.1080/014416499295402.

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Hadi-Vencheh, Abdollah, Ali Asghar Foroughi, and Majid Soleimani-damaneh. "A DEA model for resource allocation." Economic Modelling 25, no. 5 (September 2008): 983–93. http://dx.doi.org/10.1016/j.econmod.2008.01.003.

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Moselhi, Osama, and Pasit Lorterapong. "Least impact algorithm for resource allocation." Canadian Journal of Civil Engineering 20, no. 2 (April 1, 1993): 180–88. http://dx.doi.org/10.1139/l93-023.

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A new heuristic-based resource-scheduling algorithm called the least impact model is developed. Unlike available heuristic models, the least impact model allocates resources to a set or a group of activities simultaneously rather than sequentially to individual activities, so as to minimize the negative impact on the remaining total float calculated from a project CPM-type network. A new parameter called future float is introduced as an indicator for assigning scheduling priorities to the sets of activities being considered. Activity sets are generated by first considering all possible combinations of current activities experiencing resource conflict and then narrowing them down to those feasible, which in turn are assigned priorities for allocation of resources based on the least negative impact on the duration of the project. Two examples are worked out to illustrate the use and capabilities of the present model. The results indicate that the least impact model is capable of producing better solutions than those generated from the commonly used total float and the recently proposed current float techniques. Key words: planning and scheduling, resource allocation, resource-constraints scheduling, heuristic scheduling.

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Mao, Li, De Yu Qi, Wei Wei Lin, Bo Liu, and Ye Da Li. "An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization." International Journal of Grid and High Performance Computing 8, no. 2 (April 2016): 43–57. http://dx.doi.org/10.4018/ijghpc.2016040103.

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With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

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Ma, Lu, Xiangming Wen, Luhan Wang, Zhaoming Lu, Raymond Knopp, and Irfan Ghauri. "A Biological Model for Resource Allocation and User Dynamics in Virtualized HetNet." Wireless Communications and Mobile Computing 2018 (September 27, 2018): 1–11. http://dx.doi.org/10.1155/2018/1745904.

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Virtualization technology is considered an effective measure to enhance resource utilization and interference management via radio resource abstraction in heterogeneous networks (HetNet). The critical challenge in wireless virtualization is virtual resource allocation on which substantial works have been done. However, most existing researches on virtual resource allocation focus on improving total utility. Different from the existing works, we investigate the dynamic-aware virtual radio resource allocation in virtualization based HetNet considering utility and fairness. A virtual radio resource management framework is proposed, where the radio resources of different physical networks are virtualized into a virtual resource pool and mobile virtual network operators (MVNOs) compete for virtual resources from the pool to provide service to users. A virtual radio resource allocation algorithm based on biological model is developed, considering system utility, fairness, and dynamics. Simulation results are provided to verify that the proposed virtual resource allocation algorithm not only converges within a few iterations, but also achieves a better trade-off between total utility and fairness than existing algorithm. Besides, it can also be utilized to analyze the population dynamics of system.

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Oyamaguchi, Natsumi, Hiroyuki Tajima, and Isamu Okada. "Model of Multi-branch Trees for Efficient Resource Allocation." Algorithms 13, no. 3 (March 1, 2020): 55. http://dx.doi.org/10.3390/a13030055.

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Although exploring the principles of resource allocation is still important in many fields, little is known about appropriate methods for optimal resource allocation thus far. This is because we should consider many issues including opposing interests between many types of stakeholders. Here, we develop a new allocation method to resolve budget conflicts. To do so, we consider two points—minimizing assessment costs and satisfying allocational efficiency. In our method, an evaluator’s assessment is restricted to one’s own projects in one’s own department, and both an executive’s and mid-level executives’ assessments are also restricted to each representative project in each branch or department they manage. At the same time, we develop a calculation method to integrate such assessments by using a multi-branch tree structure, where a set of leaf nodes represents projects and a set of non-leaf nodes represents either directors or executives. Our method is incentive-compatible because no director has any incentive to make fallacious assessments.

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Zhu, Li Li, and Yi Feng Duan. "Research on the Resource Allocation Model for the Satellite Constellation Communication System." Advanced Materials Research 121-122 (June 2010): 669–77. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.669.

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Satellite constellation, emerging as a new paradigm for next-generation communicating, enables large-scale application of the geographically and spatially distributed heterogeneous resources for solving problems in science, engineering, and military affairs. The resource allocation in such a large-scale distributed environment is a complex task. Due to the factors that trigger the deployment of resources in satellite constellation communication system, the artificial immune theory is applied to resource allocation field to propose the task-oriented common mathematic model about resource allocation of communication system, which is aimed at the purpose of improving the effectiveness of resource allocation and is based on the 2 important indicators that are communication task’s effectiveness factors and the degree of satisfaction in the communication system. As the immune system has characteristics of self-adaptive, self-learning and self-organization, an immune allocation algorithm that fuzzy processing time is presented by applying the immune theory to resource allocation. Simulation results show that these methods are feasible and efficient in solving the problems of resource allocation for satellite constellation communication system, and the research on this object is a meaningful exploring.

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Wang, Haoyu, Lina Wang, Zhichao Zhou, Xueqiang Tao, Giovanni Pau, and Fabio Arena. "Blockchain-Based Resource Allocation Model in Fog Computing." Applied Sciences 9, no. 24 (December 16, 2019): 5538. http://dx.doi.org/10.3390/app9245538.

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Fog computing makes up for the shortcomings of cloud computing. It brings many advantages, but various peculiarities must be perceived, such as security, resource management, storage, and other features at the same time. This paper investigates the resource contribution model between the fog node and cloud or users when fog computing introduces blockchain. The proposed model practices the reward and punishment mechanism of the blockchain to boost the fog nodes to contribute resources actively. The behavior of the fog node in contributing resources and the completion degree of the task also for contributing resources are packaged into blocks and stored in the blockchain system to form a transparent, open, and tamper-free service evaluation index. The differential game method is employed to model and solve the above process and address the interaction between the optimal resource contribution strategy of the fog node and the optimal benefit under the optimal resource contribution strategy. Indirectly, this service evaluation index also brings long-term economic benefits to fog service providers. Besides, taking advantage of the performance characteristics of the collective maintenance of blockchain and the ability to establish a credible consensus mechanism in an untrusted environment, fog computing nodes, under the proposed architecture, can have specific security protection capabilities.

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Judi, J. Antony, F. Ezhil Mary Arasi, and Dr S. Govindarajan. "Utility Based Resource Allocation Model for Cloud Services." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 3 (October 30, 2013): 855–60. http://dx.doi.org/10.24297/ijmit.v6i3.723.

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Minimizing Resource allocation problems under the demand and price uncertainty in cloud computing environments is the motivation to explore a resource provisioning strategy for cloud consumers. In this paper a utilization-based optimal cloud (UBOC) algorithm is proposed to minimize the total cost for provisioning resources in a certain time period. To make an optimal decision, the demand uncertainty from cloud consumer side and price uncertainty from cloud providers are taken into account to adjust the tradeoff between on-demand and oversubscribed costs. Using this UBOC user can share cloud resources and pay based on the usage and the results show that this algorithm can minimize the total cost under uncertainty. It also provisions the resources to remove the demand uncertainty.

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Battula, Sudheer Kumar, Saurabh Garg, Ranesh Kumar Naha, Parimala Thulasiraman, and Ruppa Thulasiram. "A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment." Sensors 19, no. 13 (July 4, 2019): 2954. http://dx.doi.org/10.3390/s19132954.

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Fog computing aims to support applications requiring low latency and high scalability by using resources at the edge level. In general, fog computing comprises several autonomous mobile or static devices that share their idle resources to run different services. The providers of these devices also need to be compensated based on their device usage. In any fog-based resource-allocation problem, both cost and performance need to be considered for generating an efficient resource-allocation plan. Estimating the cost of using fog devices prior to the resource allocation helps to minimize the cost and maximize the performance of the system. In the fog computing domain, recent research works have proposed various resource-allocation algorithms without considering the compensation to resource providers and the cost estimation of the fog resources. Moreover, the existing cost models in similar paradigms such as in the cloud are not suitable for fog environments as the scaling of different autonomous resources with heterogeneity and variety of offerings is much more complicated. To fill this gap, this study first proposes a micro-level compensation cost model and then proposes a new resource-allocation method based on the cost model, which benefits both providers and users. Experimental results show that the proposed algorithm ensures better resource-allocation performance and lowers application processing costs when compared to the existing best-fit algorithm.

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Xiong, Hui, Qixin Shi, Xianding Tao, and Wuhong Wang. "A Compromise Programming Model for Highway Maintenance Resources Allocation Problem." Mathematical Problems in Engineering 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/178651.

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This paper formulates a bilevel compromise programming model for allocating resources between pavement and bridge deck maintenances. The first level of the model aims to solve the resource allocation problems for pavement management and bridge deck maintenance, without considering resource sharing between them. At the second level, the model uses the results from the first step as an input and generates the final solution to the resource-sharing problem. To solve the model, the paper applies genetic algorithms to search for the optimal solution. We use a combination of two digits to represent different maintenance types. Results of numerical examples show that the conditions of both pavements and bridge decks are improved significantly by applying compromise programming, rather than conventional methods. Resources are also utilized more efficiently when the proposed method is applied.

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Moummadi, Kamal, Rachida Abidar, and Hicham Medromi. "Distributed Resource Allocation." International Journal of Mobile Computing and Multimedia Communications 4, no. 2 (April 2012): 49–62. http://dx.doi.org/10.4018/jmcmc.2012040104.

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The growth of technological capabilities of mobile devices, the evolution of wireless communication technologies, and the maturity of embedded systems contributed to expand the Machine to machine (M2M) concept. M2M refers to data communication between machines without human intervention. The objective of this paper is to present the grand schemes of a model to be used in an agricultural Decision support System. The authors start by explaining and justifying the need for a hybrid system that uses both Multi-Agent System (MAS) and Constraint Programming (CP) paradigms. Then, the authors propose an approach for Constraint Programming and Multi-Agent System mixing based on controller agent concept. The authors present concrete constraints and agents to be used in a distributed architecture based on the proposed approach for M2M services and agricultural decision support. The platform is built in Java using general interfaces of both MAS and Constraint Satisfaction Problem (CSP) platforms and the conception is made by agent UML (AUML).

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WU, JIE, and QINGXIAN AN. "NEW APPROACHES FOR RESOURCE ALLOCATION VIA DEA MODELS." International Journal of Information Technology & Decision Making 11, no. 01 (January 2012): 103–17. http://dx.doi.org/10.1142/s0219622012500058.

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This paper focuses on the problem of resource allocation through data envelopment analysis. We propose three integrated models for allocating resources. The first model aims at minimizing the input consumption, the second one aims at maximizing the total outputs within the current resources, and the last one aims at maximizing the total outputs using the predicted resources in the next production season. Since the number of inputs or outputs is usually more than one, the abovementioned issue is often a multiple objective linear programming (MOLP) problem. Through the proportion of inputs (outputs) of new decision making unit (DMU) to the total inputs (outputs) of all old DMUs, we transform the MOLP problem into a single objective linear programming model. We assume that decision maker must ensure that the expected outputs of each DMU after allocation in the next production season are not less than this production season. All these proposed models have the same advantage that the results gained from the models are Pareto efficient. A numerical example of 25 supermarkets is used to illustrate our proposed approach.

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Liu, Shu Shun, and Wei Tong Chen. "Construction Multi-Project Scheduling Model Considering Different Resource Allocation Behavior." Applied Mechanics and Materials 174-177 (May 2012): 2815–19. http://dx.doi.org/10.4028/www.scientific.net/amm.174-177.2815.

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According to previous researches, an investigation through small to mid-size construction contractors showed that 84% of construction contractors execute their projects in a multi-project environment. In a multi-project environment, scheduling problems with resource constraints are much more complicated than those in a single project. One of the most important factors that influence multi-project scheduling problems is resource allocation policy, depending on the types of resources, which can be defined by the way of resource acquisition and sharing behavior. This paper discusses resource allocation mechanism for construction multi-project scheduling issues, and then presents an optimization-based model to resolve resource allocation problems. This research developed a CP-based (Constraint Programming) model, which is capable of handling different optimization objectives such as minimizing total cost, overall project duration, subject to resource assignment combinations for each activity. Based on research results, the influence of different types of resource quantity on multi-project duration is discussed. Moreover, resource competitive behavior among all projects is recognized. It concludes that the effective increment of critical resources can reduce overall project duration. The major goal of this research is to find the relation among duration-cost-resource in a multi-project environment, and provide systematic information for construction parties when making resource allocation decisions.

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Liu, Xu, Xiaoqiang Di, Jinqing Li, Huan Wang, Jianping Zhao, Huamin Yang, Ligang Cong, and Yuming Jiang. "Allocating Limited Resources to Protect a Massive Number of Targets Using a Game Theoretic Model." Mathematical Problems in Engineering 2019 (March 13, 2019): 1–16. http://dx.doi.org/10.1155/2019/5475341.

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Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a trade-off between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource is considered in the proposed model. Additionally, a bounded rational behavior model (quantal response: QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.

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Anazawa, Masahiro. "Inequality in resource allocation and population dynamics models." Royal Society Open Science 6, no. 7 (July 2019): 182178. http://dx.doi.org/10.1098/rsos.182178.

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The Hassell model has been widely used as a general discrete-time population dynamics model that describes both contest and scramble intraspecific competition through a tunable exponent. Since the two types of competition generally lead to different degrees of inequality in the resource distribution among individuals, the exponent is expected to be related to this inequality. However, among various first-principles derivations of this model, none is consistent with this expectation. This paper explores whether a Hassell model with an exponent related to inequality in resource allocation can be derived from first principles. Indeed, such a Hassell model can be derived by assuming random competition for resources among the individuals wherein each individual can obtain only a fixed amount of resources at a time. Changing the size of the resource unit alters the degree of inequality, and the exponent changes accordingly. As expected, the Beverton–Holt and Ricker models can be regarded as the highest and lowest inequality cases of the derived Hassell model, respectively. Two additional Hassell models are derived under some modified assumptions.

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Yiftachel, Peleg, Irit Hadar, Dan Peled, Eitan Farchi, and Dan Goldwasser. "The Study of Resource Allocation among Software Development Phases: An Economics-Based Approach." Advances in Software Engineering 2011 (January 12, 2011): 1–21. http://dx.doi.org/10.1155/2011/579292.

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This paper presents an economics-based approach for studying the problem of resource allocation among software development phases. Our approach is structured along two parallel axes: theoretical and empirical. We developed a general economic model for analyzing the allocation problem as a constrained profit maximization problem. The model, based on a novel concept of software production function, considers the effects of different allocations of development resources on output measures of the resulting software product. An empirical environment for evaluating and refining the model is presented, and a first exploratory study for characterizing the model's components and developers' resource allocation decisions is described. The findings illustrate how the model can be applied and validate its underlying assumptions and usability. Future quantitative empirical studies can refine and substantiate various aspects of the proposed model and ultimately improve the productivity of software development processes.

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Wang, Yanyan. "Multiperiod Optimal Allocation of Emergency Resources in Support of Cross-Regional Disaster Sustainable Rescue." International Journal of Disaster Risk Science 12, no. 3 (May 3, 2021): 394–409. http://dx.doi.org/10.1007/s13753-021-00347-5.

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AbstractCross-regional allocation is necessary for the rational utilization and optimal allocation of resources. It is also the key to effective and sustainable disaster relief. Existing research, however, generally centers on emergency resource allocation only within territories or regions. This article proposes a multiperiod allocation optimization model for emergency resources based on regional self-rescue and cross-regional collaborative rescue efforts. The model targets the shortest delivery time and lowest allocation costs as its efficiency goals and the maximum coverage rate of resource allocation in the disaster-affected locations as its equity goal. An objective weighting fuzzy algorithm based on two-dimensional Euclidean distance is designed to solve the proposed model. A case study based on the Wenchuan Earthquake of 12 May 2008 was conducted to validate the proposed model. The results indicate that our proposed model allows for optimal, multiperiod cross-regional resource allocation by combining interterritorial and nearby allocation principles. Cross-regional relief makes resource allocation more equitable, minimizes dissatisfaction, and prevents losses. Different decision preferences appear to significantly affect the choice of resource allocation scheme employed, which provides flexibility for decision making in different emergencies.

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Narayana, Vejendla Lakshman, and Divya Midhunchakkaravarthy. "Secured Resource Allocation for Authorized Users Using Time Specific Blockchain Methodology." International Journal of Safety and Security Engineering 11, no. 2 (April 30, 2021): 201–5. http://dx.doi.org/10.18280/ijsse.110209.

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The utilization of energy in blockchain division is high as resource allocation models are using this technology and the rundown of resource utilization cases is continually developing. The communicated and permanent nature of blockchain innovation might be utilized to quicken the progressing change to increasingly decentralized and digitalized vitality frameworks and to address a portion of the difficulties the business is confronting in providing security in identification of authorized users and resource allocation transactions among the authorized users. The allocated resources to the users need to be recorded, otherwise the attackers may use them for malicious operations. In any case, blockchain is a developing innovation and it is viewed as a basic vulnerability by numerous users as the difficulties and chances of execution are still to a great extent. There is in this way an absence of information and shortage of dynamic gadgets for getting why, when and how the innovation can include significant worth. The proposed Resource Allocation for Authorized Users using Time specific Blockchain Methodology (RAAUTBM) performs resource allocation to authorized users to avoid malicious actions among blockchain-based use cases and increase practical information about how blockchain could be actualized. The RAAUTBM model verifies all the users for allotting access to the system. The proposed model allots the resources only to the authorized users and to identify the malicious users and remove them from the framework. The resources once allotted to a user remains for a time interval and then the resource is re-allotted to other authorized users for avoiding delay. Resource exchanges in this segment are known to be dull and wasteful, to a limited extent because of the absence of promoted straightforwardness. This research work centers around the advancement of a blockchain application that can improve the resource exchange procedure among authorized users. The proposed model is compared with the traditional methods and the results demonstrate that the proposed model is effective in allocating resources only to the authorized users.

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Smaiti, Marwane, and Mostafa Hanoune. "Problem Resolution and Operational Resource to Optimize Resource Allocation." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (January 3, 2018): 5. http://dx.doi.org/10.23956/ijarcsse.v7i12.491.

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The distribution of resources is a key to the success of a given production process and its maintenance. Indeed, companies can gain a decisive and immediate competitive advantage. We aim to model the allocation of resources in a power type production unit proposing improvements at a later stage. A model: workers, resources, tasks will be adopted as part of our model. Once developed, this model can be the starting point for further optimization efforts for the entire value chain component of any production process. CAPEX: Reduction of operating costs by dynamic elimination of losses. To our previous efforts, we add a problem resolution framework rendering it easier for the user to identify resource importance and resource leakage.

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Fang, Lei, and C.-Q. Zhang. "Resource allocation based on the DEA model." Journal of the Operational Research Society 59, no. 8 (August 2008): 1136–41. http://dx.doi.org/10.1057/palgrave.jors.2602435.

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Wang, Shouyang, and F. A. Lootsma. "A hierarchical optimization model of resource allocation." Optimization 28, no. 3-4 (January 1994): 351–65. http://dx.doi.org/10.1080/02331939408843928.

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Ciocan, Dragos Florin, and Vivek Farias. "Model Predictive Control for Dynamic Resource Allocation." Mathematics of Operations Research 37, no. 3 (August 2012): 501–25. http://dx.doi.org/10.1287/moor.1120.0548.

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Castanon, D. A., and J. M. Wohletz. "Model Predictive Control for Stochastic Resource Allocation." IEEE Transactions on Automatic Control 54, no. 8 (August 2009): 1739–50. http://dx.doi.org/10.1109/tac.2009.2024562.

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Ghobadi, Seaid. "A dynamic DEA model for resource allocation." International Journal of Mathematics in Operational Research 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijmor.2019.10029985.

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Ghobadi, Saeid. "A dynamic DEA model for resource allocation." International Journal of Mathematics in Operational Research 17, no. 1 (2020): 50. http://dx.doi.org/10.1504/ijmor.2020.109053.

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Bromiley, Philip. "A Prospect Theory Model of Resource Allocation." Decision Analysis 6, no. 3 (September 2009): 124–38. http://dx.doi.org/10.1287/deca.1090.0142.

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Shen, Xiaojing, Xu Wu, Xinmin Xie, Chuanjiang Wei, Liqin Li, and Jingjing Zhang. "Synergetic Theory-Based Water Resource Allocation Model." Water Resources Management 35, no. 7 (May 2021): 2053–78. http://dx.doi.org/10.1007/s11269-021-02766-x.

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Goelzer, Anne, and Vincent Fromion. "Resource allocation in living organisms." Biochemical Society Transactions 45, no. 4 (July 7, 2017): 945–52. http://dx.doi.org/10.1042/bst20160436.

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Quantitative prediction of resource allocation for living systems has been an intensive area of research in the field of biology. Resource allocation was initially investigated in higher organisms by using empirical mathematical models based on mass distribution. A challenge is now to go a step further by reconciling the cellular scale to the individual scale. In the present paper, we review the foundations of modelling of resource allocation, particularly at the cellular scale: from small macro-molecular models to genome-scale cellular models. We enlighten how the combination of omic measurements and computational advances together with systems biology has contributed to dramatic progresses in the current understanding and prediction of cellular resource allocation. Accurate genome-wide predictive methods of resource allocation based on the resource balance analysis (RBA) framework have been developed and ensure a good trade-off between the complexity/tractability and the prediction capability of the model. The RBA framework shows promise for a wide range of applications in metabolic engineering and synthetic biology, and for pursuing investigations of the design principles of cellular and multi-cellular organisms.

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Saravanan, G., and N. Yuvaraj. "Cloud resource optimization based on poisson linear deep gradient learning for mobile cloud computing." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 787–97. http://dx.doi.org/10.3233/jifs-200799.

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Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms.

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Bi, Kexin, Kwangil An, and Xiang Li. "A Resource Optimization Allocation Strategy for China’s Shipbuilding Industry Green Innovation System." International Journal of Innovation and Technology Management 17, no. 04 (June 2020): 2050029. http://dx.doi.org/10.1142/s0219877020500297.

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In order to realize the resource optimization allocation in the green innovation system of China’s shipbuilding industry under the internet environment, to improve the level of green innovation and to reduce the resource consumption, a resource optimization allocation model and the corresponding allocation strategy are proposed. The model integrates and shares the innovation resource data through Internet of Things (IOT) technology, and optimizes the allocation decision by using the cooperative differential game and Particle Swarm Optimization (PSO) algorithm. At the same time, it ensures the robustness of green innovation system and realizes the optimization allocation of resources. A case study is given to illustrate the feasibility of the model. The results show that the green innovation subject can carry out strategic interaction by adjusting the allocation proportion of innovation resources through the proposed model, so as to optimize the overall green innovation benefits of the system.

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Sopha, Bertha Maya, and Anna Maria Sri Asih. "Human resource allocation for humanitarian organizations: a systemic perspective." MATEC Web of Conferences 154 (2018): 01048. http://dx.doi.org/10.1051/matecconf/201815401048.

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Human resource allocation appears to be one of the important factors toward effective humanitarian relief operations. Particularly in developing countries, the role of volunteers which is mostly managed by humanitarian organizations has become prominent. Due to the limited human resources, the humanitarian organizations are challenged to allocate their resources to both provide assistance for disaster victims and build organization capacity effectively. The present research aims at identifying effective human resources allocation policy in humanitarian organizations. A formal simulation model was developed using system dynamics approach. Two scenarios, i.e., constant relief demand and empirical relief demand, were developed and tested. Experiments were conducted to examine various allocation policies for both scenarios. Results indicate there is allocation trade-off when it comes to allocate human resources to relief operations and capacity building. Results highlight that allocating resources to capacity building is necessary to sustain relief operations in the long-term.

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Karpenko, М., and О. Stelma. "RESOURCE ALLOCATION MODELS IN HIERARCHICAL MANAGEMENT SYSTEMS." Municipal economy of cities 1, no. 154 (April 3, 2020): 120–25. http://dx.doi.org/10.33042/2522-1809-2020-1-154-120-125.

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The article proposes a mathematical model of the hierarchical system of volume-dynamic resource allocation. The model describes resource consumption processes in multi-layered systems and allows us to view the management of such systems from a single perspective, to reflect the interrelationship of decisions formed at different levels of the hierarchy. According to the proposed model, a production (or business) system is considered as a large dynamic resource allocation system that is characterized by the interaction of three components: processes, resources, and time (R, P, and T.). Each of these components is represented by many lower-level elements with a defined ratio of a partial order, which sets the structure of the corresponding systems. The article proposes the way of description and features of the system of resources, processes and time, rules of aggregation, and disaggregation taking into account the structure of R, P, and T systems. On the basis of the described models, a description of the production system at the lower level in the form of a binary function π0 , as well as procedures for the formation of appropriate descriptions for arbitrary levels of the hierarchy in the form of a set of tetra relations πi. An algorithm for the formation of the solution π0 , as well as procedures for its transformation to the model of an arbitrary level, is proposed. The use of formal methods to describe the procedures of resource allocation at different levels of the hierarchy allows building a single database, to develop a structured and compact system of requests for information in the formation of management decisions. In such a system, data for processing queries are represented by a tuple of three elements Kin (levels of input aggregation by process and time resources), the basic solution πб, a set of elements R, P, T of the corresponding level, a tuple Kout (three levels of output aggregation). Depending on the Kin and Kout, values, the system handles the πб base solution using either aggregation or dis-aggregation procedures, resulting in a final result. Keywords: management, resources, processes, model, resource allocation, aggregation, disaggregation, math-ematical programming, optimization.

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Maritan, Catherine A., and Gwendolyn K. Lee. "Bringing a Resource and Capability Lens to Resource Allocation." Journal of Management 43, no. 8 (October 17, 2017): 2609–19. http://dx.doi.org/10.1177/0149206317727585.

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This article highlights a perspective that has been underexplored in resource allocation research. By viewing resource allocation through a resource and capability lens, three connections are developed between resource-based theories of strategy and strategy research on resource allocation. First, the lens is applied to frame capital investments as investing in capabilities. This framing provides a theoretical path connecting the strategic purpose of investments, through value creation from resource commitments, to the creation of competitive advantage. Second, resource allocation for the purpose of capability development is related to a resource-based model of asset accumulation. Placing resource allocation decisions in the context of capability development suggests that key features of the asset accumulation process can usefully inform research on the resource allocation process. Last, corporate capital allocation is connected to resource redeployment in multibusiness firms. This connection explicates ways in which corporate headquarters can add to firm value. These connections illustrate the potential that resource-based theories have to contribute insights to resource allocation research.

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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 (November 19, 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 combination of auto-scaling algorithms, resource allocation and scheduling for allocating the appropriate resources and scheduled them. This model consists of three new algorithms namely Grey Wolf Optimization and Fuzzy rules based Resource allocation and Scheduling Algorithm (GWOFRSA), Auto-Scaling Algorithm for Cloud based Web Application (ASACWA) and Auto-Scaling Algorithm for handling Distributed Computing Tasks (ASADCT). Here, we introduce new auto-scaling algorithms for enhancing the performance of cloud services. In this work, the optimization technique is used to predict the cloud server workload, resource requirements and it also uses fuzzy rules for monitoring the resource utilization and the size of virtual machine allocation process. According to the workload prediction, the completion time is estimated for each cloud server. The experiments are conducted by using a simulator called CloudSim environment of Java programming and compared with the existing works available in this direction in terms of resource utilization and enhance the cloud performance with better Quality of Service of Virtual Machine allocation, Missed Deadline, Demand Satisfaction, Power Utilization, CPU Load and throughput.

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Xu, Jing, Bo Wang, and Gihong Min. "Research on Human Resource Allocation Model Based on SOM Neural Network." International Journal of Mobile Computing and Multimedia Communications 10, no. 1 (January 2019): 65–76. http://dx.doi.org/10.4018/ijmcmc.2019010105.

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With the fierce competition of the enterprise market, the human resource allocation of enterprises will face multiple risks. This article takes the connotation of human resource configuration management as the research object and establishes the human resource configuration model through SOM neural network. And the model is trained, learned, and tested. What's more, it is applied to human resources management to adjust the allocation of human resources for the enterprise in a timely manner. It provides a detailed basis for proposing coping strategies and has a great application value.

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He, Feng, Rui Chun He, Sheng Nan Sun, and Jiong Di Chen. "Research on the Model of Emergency Fire Rescue Vehicle Routing Selection and Resource Allocation." Applied Mechanics and Materials 641-642 (September 2014): 824–28. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.824.

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In order to arrive at the scene of the fire in the shortest time dealing with the accident, considering two fires occurred at the same time, taking optimal allocation of resources at the center of the fire fighting and the least time of driving to fireground as objective functions, based on automobile brake model and two-stage scheduling mechanism, to build the total time optimal model and resource allocation optimization model. Models obtain vehicle shortest transit time and the optimal resource allocation amount, it can effectively solve routing problem of the emergency rescue vehicle, resources shortage also can be solved when many accidents happen at the same time, to avoid the waste of resources and to ease the pressure of maintenance cost.

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Chen, Li Mei. "Grid Scheduling Model Based on Petri Net." Applied Mechanics and Materials 687-691 (November 2014): 2268–71. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2268.

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In order to solve the task allocation and scheduling problem, based on the analysis and research of hierarchical Petri net, this paper proposed a grid scheduling model based on hierarchical scheduling model. This model can ensure that the various elements of the loose coupling between the scheduling, to facilitate data resources deployment in grid environment; in addition, this paper proposed thought of the variable structure Petri network, through this technology can dynamic adjust work modeling and resource allocation, and to support the mission of exception handling; finally, to verify the rationality of the scheduling algorithm, and analyzes the performance and the efficiency of resource allocation algorithm.

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Xie, Jun, and Min Hua Wu. "Resource Allocation for Parallel Task in Grids." Advanced Materials Research 181-182 (January 2011): 866–72. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.866.

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Task running in Grids may require multiple types of resources simultaneously. Proposing and designing a resource discovery scheme based on Economic Agent. Base on the economic model and the technique in agent of grouping nodes sharing similar files to improve efficiency, this thesis suggests a resource discovery scheme based on economic agent, which is called EAGRD. Theoretical models on resource discovery are provided, under which EAGRD is compared with existing schemes theoretically. By controlling propagation of message into related communities, EAGRD improves time and network efficiency at the cost of topological maintenance overhead.

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Tohidi, Ghasem, Hamed Taherzadeh, and Sara Hajiha. "Undesirable Outputs’ Presence in Centralized Resource Allocation Model." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/675895.

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Data envelopment analysis (DEA) is a common nonparametric technique to measure the relative efficiency scores of the individual homogenous decision making units (DMUs). One aspect of the DEA literature has recently been introduced as a centralized resource allocation (CRA) which aims at optimizing the combined resource consumption by all DMUs in an organization rather than considering the consumption individually through DMUs. Conventional DEA models and CRA model have been basically formulated on desirable inputs and outputs. The objective of this paper is to present new CRA models to assess the overall efficiency of a system consisting of DMUs by using directional distance function when DMUs produce desirable and undesirable outputs. This paper initially reviewed a couple of DEA approaches for measuring the efficiency scores of DMUs when some outputs are undesirable. Then, based upon these theoretical foundations, we develop the CRA model when undesirable outputs are considered in the evaluation. Finally, we apply a short numerical illustration to show how our proposed model can be applied.

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Journal articles on the topic 'Resource allocation model'

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