Multi-Economic Agent Grid Resource Allocation Strategies in the Signaling Game Theory Analysis Model

2010 ◽  
Vol 29-32 ◽  
pp. 1093-1099 ◽  
Author(s):  
Jun Xie ◽  
Ji Guang Li
Keyword(s):  
Resource Allocation ◽  
Analysis Model ◽  
Theory Analysis ◽  
Grid Resource ◽  
Trading Agents ◽  
Proposed Model ◽  
Finite Set ◽  
Optimal Resource ◽  
Resource Allocation Strategy ◽  
Allocation Strategies

The paper presents a market oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This paper is target to solve above issues by using utility-based optimization scheme. We firstly point out the factors that influence the resources’ prices; then make out the trading flow for resource consumer agents and provider agents. By doing these, the two trading agents can decide their price due to the dynamic changes of the Grid environment without any manmade interferences. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the consumer’s market optimization and provider’s market optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation.

Grid Resource Discovery Strategies Base on Multi-Economic Agent

2010 ◽  
Vol 29-32 ◽  
pp. 1100-1108
Author(s):  
Jun Xie ◽  
Ji Guang Li
Keyword(s):  
Resource Allocation ◽  
Resource Discovery ◽  
Optimal Resource Allocation ◽  
Grid Resource ◽  
Trading Agents ◽  
Proposed Model ◽  
Finite Set ◽  
Optimal Resource ◽  
Resource Allocation Strategy ◽  
Grid Resource Discovery

The paper presents a market oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This paper is target to solve above issues by using utility-based optimization scheme. We firstly point out the factors that influence the resources’ prices; then make out the trading flow for resource consumer agents and provider agents. By doing these, the two trading agents can decide their price due to the dynamic changes of the Grid environment without any manmade interferences. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the consumer’s market optimization and provider’s market optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation.

scholarly journals Recovery and Resource Allocation Strategies to Maximize Mobile Network Survivability by Using Game Theories and Optimization Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Pei-Yu Chen ◽  
Frank Yeong-Sung Lin
Keyword(s):  
Resource Allocation ◽  
Mobile Network ◽  
Critical Issue ◽  
Optimization Techniques ◽  
Network Survivability ◽  
Support Network ◽  
Network Attack ◽  
Damage Degree ◽  
Optimal Resource ◽  
Allocation Strategies

With more and more mobile device users, an increasingly important and critical issue is how to efficiently evaluate mobile network survivability. In this paper, a novel metric called Average Degree of Disconnectivity (Average DOD) is proposed, in which the concept of probability is calculated by the contest success function. The DOD metric is used to evaluate the damage degree of the network, where the larger the value of the Average DOD, the more the damage degree of the network. A multiround network attack-defense scenario as a mathematical model is used to support network operators to predict all the strategies both cyber attacker and network defender would likely take. In addition, the Average DOD would be used to evaluate the damage degree of the network. In each round, the attacker could use the attack resources to launch attacks on the nodes of the target network. Meanwhile, the network defender could reallocate its existing resources to recover compromised nodes and allocate defense resources to protect the survival nodes of the network. In the approach to solving this problem, the “gradient method” and “game theory” are adopted to find the optimal resource allocation strategies for both the cyber attacker and mobile network defender.

scholarly journals Distributed Training and Inference of Deep Learning Models for Multi-Modal Land Cover Classification

2020 ◽  
Vol 12 (17) ◽  
pp. 2670
Author(s):  
Maria Aspri ◽  
Grigorios Tsagkatakis ◽  
Panagiotis Tsakalides
Keyword(s):  
Resource Allocation ◽  
Land Cover ◽  
Network Architecture ◽  
Large Scale ◽  
Land Cover Classification ◽  
Processing Power ◽  
Proposed Model ◽  
Efficient Resource ◽  
Optimal Resource ◽  
Machine Communication

Deep Neural Networks (DNNs) have established themselves as a fundamental tool in numerous computational modeling applications, overcoming the challenge of defining use-case-specific feature extraction processing by incorporating this stage into unified end-to-end trainable models. Despite their capabilities in modeling, training large-scale DNN models is a very computation-intensive task that most single machines are often incapable of accomplishing. To address this issue, different parallelization schemes were proposed. Nevertheless, network overheads as well as optimal resource allocation pose as major challenges, since network communication is generally slower than intra-machine communication while some layers are more computationally expensive than others. In this work, we consider a novel multimodal DNN based on the Convolutional Neural Network architecture and explore several different ways to optimize its performance when training is executed on an Apache Spark Cluster. We evaluate the performance of different architectures via the metrics of network traffic and processing power, considering the case of land cover classification from remote sensing observations. Furthermore, we compare our architectures with an identical DNN architecture modeled after a data parallelization approach by using the metrics of classification accuracy and inference execution time. The experiments show that the way a model is parallelized has tremendous effect on resource allocation and hyperparameter tuning can reduce network overheads. Experimental results also demonstrate that proposed model parallelization schemes achieve more efficient resource use and more accurate predictions compared to data parallelization approaches.

Predator-induced allometric changes in the tail spine length of Daphnia: a distinct resource allocation strategy

2021 ◽  
Author(s):  
Lei Gu ◽  
Yang Xu ◽  
Tingting Yang ◽  
Shanshan Qin ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Developmental Stages ◽  
Individual Growth ◽  
Resource Allocations ◽  
Daphnia Species ◽  
Defensive Responses ◽  
Fish Kairomone ◽  
Resource Allocation Strategy ◽  
Species Specific ◽  
Allocation Strategies

Abstract Understanding resource allocation strategies underlying inducible defense is a challenging scientific issue, because of the difficulty in measuring resource allocations of defensive traits. We examined allometric changes to evaluate resource allocation strategies on the tail spine of Daphnia within and between species and further explore the allometric changes at different developmental stages and their relationship with growth and reproduction. We found that four Daphnia species (Daphnia magna, Daphnia sinensis, Daphnia galeata and Daphnia mitsukuri) can perform significant inducible defensive responses when exposed to fish kairomone. Different from the other Daphnia species, D. mitsukuri significantly enhanced the allometric slope of its tail spine when exposed to fish kairomone. We also found that allometric changes among different D. mitsukuri clones are significant in adult individuals. Furthermore, the allometric changes show a significant negative interaction with individual growth, indicating that a trade-off may exist between the resource allocations of tail spine elongation and growth. This study highlights the species-specific allometric changes in tail spine elongation and provides an explanation for this from resource allocations.

Dynamic Resource Allocation on Multi-Category Two-Sided Platforms

2020 ◽  
Author(s):  
Hui Li ◽  
Qiaowei Shen ◽  
Yakov Bart
Keyword(s):  
Resource Allocation ◽  
Network Effects ◽  
Early Stage ◽  
Scale Up ◽  
Sustainable Growth ◽  
Critical Question ◽  
Indirect Network Effects ◽  
Optimal Resource ◽  
Daily Deals ◽  
Allocation Strategies

Platform businesses are typically resource-intensive and must scale up their business quickly in the early stage to compete successfully against fast-emerging rivals. We study a critical question faced by such firms in the novel context of multicategory two-sided platforms: how to optimally make investment decisions across two sides, multiple categories, and different time periods to achieve fast and sustainable growth. We first develop a two-category two-period theoretical model and propose optimal resource allocation strategies that account for heterogeneous within-category direct and indirect network effects and cross-category interdependence. We find that the proposed strategy shares the spirit of the allocation rules for multiproduct nonplatform firms and single-product platform firms, yet it does not amount to a simple combination of the existing rules. Interestingly, the business model that platforms adopt crucially determines the optimal strategy. Platforms that charge by user should adopt a “reinforcing” rule for both within- and cross-category allocations by allocating more resources toward the stronger growth driver. Platforms that charge by transaction should also adopt the reinforcing rule for within-category allocation, but follow a “compensatory” rule for cross-category and intertemporal allocations by allocating more resources toward the weaker growth driver. We use data from the daily deals industry to empirically identify the network effects, propose alternative allocation strategies stemming from our theoretical findings, and use simulations to show the benefits of these strategies. For instance, we show that reallocating 10% of the average observed investment from Fitness to Beauty can increase profits by up to 15.5% for some cities. This paper was accepted by Matthew Shum, marketing.

scholarly journals Allocating Limited Resources to Protect a Massive Number of Targets Using a Game Theoretic Model

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Xu Liu ◽  
Xiaoqiang Di ◽  
Jinqing Li ◽  
Huan Wang ◽  
Jianping Zhao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Limited Resources ◽  
Theoretic Model ◽  
Quantal Response ◽  
Proposed Model ◽  
Comparison Results ◽  
Massive Number ◽  
Game Theoretic ◽  
Resource Allocation Strategy ◽  
Game Theoretic Model

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.

scholarly journals Predator–Prey Model Based Asymmetry Resource Allocation in Satellite–Terrestrial Network

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2113
Author(s):  
Zhipeng Li ◽  
Meng Li ◽  
Qian Wang
Keyword(s):  
Resource Allocation ◽  
Satellite Networks ◽  
Volterra Model ◽  
Network Resources ◽  
Predator Prey ◽  
Predator Prey Model ◽  
Prey Model ◽  
Proposed Model ◽  
Optimal Resource ◽  
Terrestrial Network

In the traditional satellite networks, network resources are mainly allocated among all the satellites based on the same allocation algorithm. This kind of symmetry model limits the increase of throughput. In this paper, we study an asymmetry resource allocation method in a satellite–terrestrial network and propose a Lotka–Volterra based predator–prey model to achieve optimal resource allocation among different satellites. In the proposed satellite–terrestrial network, we divide all the satellites into two groups, and we try to achieve load stability between these two satellites groups. Using the predator–prey model, one group is the prey–satellites, which can obtain service requirements from mobile users. The other group is considered as predator–satellites, which can only obtain the loads from the group of the prey–satellites. Once the satellites are divided into two groups using the Lotka–Volterra model, the resource allocation problem among these satellites in two groups would be asymmetry resource. We prove the existence of solutions to the proposed model. Numerical simulation results are given to show the correctness and effectiveness of the proposed model.

scholarly journals Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1908 ◽  
Author(s):  
Bingjie Liu ◽  
Haitao Xu ◽  
Xianwei Zhou
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Information Transmission ◽  
Unmanned Aerial Vehicle ◽  
Power Level ◽  
Dynamic Game ◽  
Proposed Model ◽  
Wireless Nodes ◽  
Optimal Resource ◽  
Aerial Vehicle

Most of the wireless nodes in the Internet of Things (IoT) environment face the limited energy problem and the way to provide a sustainable energy for these nodes has become an urgent challenge. In this paper, we present an unmanned aerial vehicle (UAV) to power the wireless nodes in the IoT and an investigation on the optimal resource allocation approach based on dynamic game theory. This IoT system consists of one UAV as the power source and information receiver. The wireless nodes can be powered and collected by the UAV. In order to distinguish the wireless nodes, the wireless nodes are divided into two categories based on the energy consumption. The UAV tries to power these two categories of nodes using a different power level based on the proposed approach, where the wireless nodes control the resources for information transmission. Based on Bellman dynamic programming, the optimal allocated resources for power transfer and information transmission are obtained for both the UAV and wireless nodes, respectively. In order to show the effectiveness of the proposed model and approach, we present numerical simulations.

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