OPTIMAL RESOURCE ALLOCATION FOR A DIFFUSIVE POPULATION MODEL

2020 ◽  
Vol 28 (04) ◽  
pp. 945-976
Author(s):  
JASON BINTZ ◽  
SUZANNE LENHART
Keyword(s):  
Resource Allocation ◽  
Optimal Allocation ◽  
Growth Function ◽  
Parabolic Partial Differential Equation ◽  
Allocation Of Resources ◽  
Distribution Of Resources ◽  
Optimal Resource ◽  
Adjoint Functions ◽  
Dependent Growth ◽  
The Cost

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.

scholarly journals More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics

Biometrika ◽  
2021 ◽  
Author(s):  
Lorenzo Masoero ◽  
Federico Camerlenghi ◽  
Stefano Favaro ◽  
Tamara Broderick
Keyword(s):  
Optimal Allocation ◽  
Bayesian Nonparametrics ◽  
Allocation Of Resources ◽  
Experimental Conditions ◽  
Follow Up Study ◽  
Variant Discovery ◽  
Fixed Budget ◽  
The Cost ◽  
Genomic Variant

Abstract While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains non-trivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in the genome as possible. In this paper, we introduce a Bayesian nonparametric methodology to predict the number of new variants in a follow-up study based on a pilot study. When experimental conditions are kept constant between the pilot and follow-up, we find that our prediction is competitive with the best existing methods. Unlike current methods, though, our new method allows practitioners to change experimental conditions between the pilot and the follow-up. We demonstrate how this distinction allows our method to be used for more realistic predictions and for optimal allocation of a fixed budget between quality and quantity.

Bitcoin as an Innovative Payment Currency in Germany: Development of the e-Gold Standard

2017 ◽  
Vol 2 (2) ◽  
pp. 33-42 ◽  
Author(s):  
Aleksandar Arsov
Keyword(s):  
Resource Allocation ◽  
Transaction Costs ◽  
Gold Standard ◽  
Legal Status ◽  
Research Question ◽  
Legal Issues ◽  
Payment Systems ◽  
Optimal Resource Allocation ◽  
Optimal Resource ◽  
The Cost

Recent years have witnessed the advances of e-money systems such as Bitcoin, PayPal and various forms of stored-value cards. This paper adopts a mechanism design approach to identify some essential features of different payment systems that implement and improve the constrained optimal resource allocation in Germany. Bitcoin is a digital, decentralized, partially anonymous currency, not backed by German or any government or other legal entity, and not redeemable for gold or other commodities. Bitcoin relies on peer-to-peer networking and cryptography to maintain its integrity. Compared to most currencies or online payment services, such as PayPal, bitcoins are highly liquid, have low transaction costs, and can be used to make micropayments in Germany. Although the Bitcoin economy is flourishing, Bitcoin users are anxious about Bitcoin’s legal status. This paper examines a few relevant legal issues. The research question is to investigate how supplementary digital terminating currency Bitcoin can provide a superior fallback position as e-gold standard in Germany and worldwide. Digital self-liquidating e-Gold ounce could be distributed immediately to voters by using swipe cards used by some governments for transit facilities. Bitcoins as e-Gold ounce do not provide a viable medium of exchange because of the cost of their purchase, creation and/or exchange.

scholarly journals A forwards induction approach to candidate drug selection

2011 ◽  
Vol 43 (3) ◽  
pp. 649-665 ◽  
Author(s):  
S. Qu ◽  
J. C. Gittins
Keyword(s):  
Resource Allocation ◽  
Markov Decision Processes ◽  
Optimal Allocation ◽  
Pharmaceutical Research ◽  
Decision Processes ◽  
Drug Selection ◽  
Allocation Of Resources ◽  
Stochastic Resource Allocation ◽  
Candidate Drug ◽  
Markov Decision

A forwards induction policy is a type of greedy algorithm for Markov decision processes. It is straightforward to implement and is optimal for a large class of models, especially in stochastic resource allocation. In this paper we consider a model for the optimal allocation of resources in pre-clinical pharmaceutical research. We show that although they are not always strictly optimal, forwards induction policies perform well.

scholarly journals ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 660
Author(s):  
Marios Avgeris ◽  
Dimitrios Spatharakis ◽  
Dimitrios Dechouniotis ◽  
Aris Leivadeas ◽  
Vasileios Karyotis ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Mobile Applications ◽  
Cloud Infrastructure ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Optimal Resource ◽  
Prediction Mechanism ◽  
The Cost ◽  
Different Characteristics ◽  
Task Offloading

Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

A forwards induction approach to candidate drug selection

2011 ◽  
Vol 43 (03) ◽  
pp. 649-665
Author(s):  
S. Qu ◽  
J. C. Gittins
Keyword(s):  
Resource Allocation ◽  
Markov Decision Processes ◽  
Optimal Allocation ◽  
Pharmaceutical Research ◽  
Decision Processes ◽  
Drug Selection ◽  
Allocation Of Resources ◽  
Stochastic Resource Allocation ◽  
Candidate Drug ◽  
Markov Decision

A forwards induction policy is a type of greedy algorithm for Markov decision processes. It is straightforward to implement and is optimal for a large class of models, especially in stochastic resource allocation. In this paper we consider a model for the optimal allocation of resources in pre-clinical pharmaceutical research. We show that although they are not always strictly optimal, forwards induction policies perform well.

scholarly journals Optimal Resource Allocation and Task Segmentation in IoT Enabled Mobile Edge Cloud

2021 ◽  
Author(s):  
Asad Mahmood ◽  
Yue Hong ◽  
Muhammad Khurram Ehsan ◽  
Shahid Mumtaz
Keyword(s):  
Optimization Problem ◽  
Optimal Allocation ◽  
Transmission Power ◽  
Allocation Of Resources ◽  
Optimal Resource Allocation ◽  
Convex Optimization Problem ◽  
Task Duration ◽  
Optimal Resource ◽  
Task Segmentation ◽  
And Task

<div>This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name Estimation of Optimal Resource Allocator (EORA) is designed to optimize the function by keeping track of statistics of each candidate of the population. </div>

scholarly journals Optimal Resource Allocation and Task Segmentation in IoT Enabled Mobile Edge Cloud

2021 ◽  
Author(s):  
Asad Mahmood ◽  
Yue Hong ◽  
Muhammad Khurram Ehsan ◽  
Shahid Mumtaz
Keyword(s):  
Optimization Problem ◽  
Optimal Allocation ◽  
Transmission Power ◽  
Allocation Of Resources ◽  
Optimal Resource Allocation ◽  
Convex Optimization Problem ◽  
Task Duration ◽  
Optimal Resource ◽  
Task Segmentation ◽  
And Task

<div>This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name Estimation of Optimal Resource Allocator (EORA) is designed to optimize the function by keeping track of statistics of each candidate of the population. </div>

Problem Resolution and Operational Resource to Optimize Resource Allocation

2018 ◽  
Vol 7 (12) ◽  
pp. 5
Author(s):  
Marwane Smaiti ◽  
Mostafa Hanoune
Keyword(s):  
Resource Allocation ◽  
Production Process ◽  
Value Chain ◽  
Operating Costs ◽  
Allocation Of Resources ◽  
Power Type ◽  
Chain Component ◽  
Problem Resolution ◽  
Starting Point ◽  
Distribution Of Resources

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.

Query Based Resource Allocation and Performance Prediction in Distributed Database

2016 ◽  
Vol 4 ◽  
pp. 16
Author(s):  
Jagannatha S. ◽  
T. V. Suresh Kumar ◽  
K. Rajanikanth
Keyword(s):  
Resource Allocation ◽  
Data Transfer ◽  
Distributed Database ◽  
Allocation Of Resources ◽  
Allocation Strategy ◽  
Research Issues ◽  
Total Data ◽  
Proper Design ◽  
And Performance ◽  
The Cost

Resource allocation is one of the main issues for providing services in an efficient way on distributed database. Improving the performance is one of the key research issues by proper design of efficient distributed database and proper usage of resources in information technology. The cost of each computational service depends on the amount of computations. The system resources have to be allocated in order to handle the workload and minimize the cost of computing by using proper allocation strategy. Performance is strongly related to the allocation of resources and data fragments in distributed environment. The query requires data to be accessed from one or multiple sites. The required data are fragmented and placed in various data center, where resources exist. In this paper we propose an algorithm to minimize the total data-transfer cost required for processing the queries by proper allocation of resources. We are finding the optimum allocation strategy, which predicts the performance by estimating the cost.

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