The Allocation of Resources under Uncertainty

We study the effects of uncertainty on the allocation of resources in the standard, static, general equilibrium, two-sector, two-factor model. The elasticity of substitution in production vs that in consumption plays a key role in determining whether uncertainty attracts or repels resources. Risk aversion matters, but to a smaller extent, while factor endowments and factor intensities play a more limited role.Uncertainty; general equilibrium; two-sector model

Model of the optimal allocation of heterogeneous resources in a vertically integrated company

One of the main problems of management in a vertically integrated company – the allocation of heterogeneous resources between its organizational units in conditions a limited budget is considered. Mathematical models of the optimal allocation of heterogeneous resources are proposed, taking into account the importance of different types of resources and priorities of organizational units of the company that take into account the types of tasks they perform and the types of activity

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable

Allocating Limited Resources to Protect a Massive Number of Targets using a 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.Comment: 14 pages, 12 figures, 41 reference

Inequality and Procedural Justice in Social Dilemmas

This study investigates the influence of resource inequality and the fairness of the allocation procedure of unequal resources on cooperative behavior in social dilemmas. We propose a simple formal behavioral model that incorporates conflicting selfish and social motivations. This model allows us to predict how inequality influences cooperative behavior. Allocation of resources is manipulated by three treatments that vary in terms of procedural justice: allocating resources randomly, based on merit, and based on ascription. As predicted, procedural justice influences cooperation significantly. Moreover, gender is found to be an important factor interacting with the association between procedural justice and cooperative behavior.

Public Preferences about Fairness and the Ethics of Allocating Scarce Medical Interventions

This chapter examines how social- scientific research on public preferences bears on the ethical question of how those resources should in fact be allocated, and explain how social-scientific researchers might find an understanding of work in ethics useful as they design mechanisms for data collection and analysis. I proceed by first distinguishing the methodologies of social science and ethics. I then provide an overview of different approaches to the ethics of allocating scarce medical interventions, including an approach—the complete lives system—which I have previously defended, and a brief recap of social-scientific research on the allocation of scarce medical resources. Following these overviews, I examine different ways in which public preferences could matter to the ethics of allocation. Last, I suggest some ways in which social scientists could learn from ethics as they conduct research into public preferences regarding the allocation of scarce medical resources

Community-level Monitoring of HIV Spread

Health departments are using HIV data to monitor HIV growth in real time. The main purpose of this monitoring is to come up with policies for efficient allocation of medical resources. In order to achieve the efficient medical resources allocation, a method should be established for predicting where future transmissions of HIV will occur using the partial information of the transmission history. Validity of these predictions are of paramount importance as it affects the policy for allocation of medical resources. Indeed, the more accurate the prediction is, the more efficiently preventive care or other resources can be allocated to the network.The focus of this work is on community-level monitoring of HIV spread prevention. We have modeled the sexual network as communities of individuals and proposed community-level methods for prediction. Then, we have compared predictive power of the proposed methods in different settings of the network

A Novel Workload Allocation Strategy for Batch Jobs

The distribution of computational tasks across a diverse set of geographically distributed heterogeneous resources is a critical issue in the realisation of true computational grids. Conventionally, workload allocation algorithms are divided into static and dynamic approaches. Whilst dynamic approaches frequently outperform static schemes, they usually require the collection and processing of detailed system information at frequent intervals - a task that can be both time consuming and unreliable in the real-world. This paper introduces a novel workload allocation algorithm for optimally distributing the workload produced by the arrival of batches of jobs. Results show that, for the arrival of batches of jobs, this workload allocation algorithm outperforms other commonly used algorithms in the static case. A hybrid scheduling approach (using this workload allocation algorithm), where information about the speed of computational resources is inferred from previously completed jobs, is then introduced and the efficiency of this approach demonstrated using a real world computational grid. These results are compared to the same workload allocation algorithm used in the static case and it can be seen that this hybrid approach comprehensively outperforms the static approach

Network-constrained packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources.With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models.This work is supported by NSF CISE CNS Award #1347522, # 1239021, # 1012798