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.

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.

scholarly journals Optimizing resource allocation for approximate real-time query processing

2014 ◽  
Vol 11 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Anna Yarygina ◽  
Boris Novikov
Keyword(s):  
Resource Allocation ◽  
High Performance ◽  
Optimization Techniques ◽  
Approximate Algorithm ◽  
Query Evaluation ◽  
Resource Allocation Algorithm ◽  
Complex Queries ◽  
Optimal Resource ◽  
Approximate Query

Query optimization techniques are proved to be essential for high performance of database management systems. In the context of new querying paradigms, such as similarity based search, exact query evaluation is neither computationally feasible nor meaningful, and approximate query evaluation is the only reasonable option. In this paper a problem of resource allocation for approximate evaluation of complex queries is considered. An approximate algorithm for a near-optimal resource allocation is presented, providing the best feasible quality of the output subject to a limited total cost of a query. The results of experiments have shown that the approximate resource allocation algorithm is accurate and efficient.

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.

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