scholarly journals Optimal Resource Allocation Model to Prevent, Prepare, and Respond to Multiple Disruptions, with Application to the Deepwater Horizon Oil Spill and Hurricane Katrina

2021 ◽  
pp. 381-403
Author(s):  
Cameron A. MacKenzie ◽  
Amro Al Kazimi
Keyword(s):  
Resource Allocation ◽  
Hurricane Katrina ◽  
Oil Spill ◽  
Deepwater Horizon ◽  
Optimal Resource Allocation ◽  
Deepwater Horizon Oil Spill ◽  
Allocation Model ◽  
Resource Allocation Model ◽  
Optimal Resource

scholarly journals Optimal Resource Allocation Model and Algorithm for Elastic Enterprise Applications Migration to the Cloud

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 909 ◽  
Author(s):  
Shiyong Li ◽  
Yue Zhang ◽  
Wei Sun
Keyword(s):  
Resource Allocation ◽  
Bandwidth Allocation ◽  
Migration Time ◽  
Optimal Resource Allocation ◽  
Allocation Model ◽  
Resource Allocation Model ◽  
Cloud Migration ◽  
Cloud Resource ◽  
Enterprise Applications ◽  
Optimal Resource

Cloud computing has been widely used in various industries in recent years. However, when migrating enterprise applications into the cloud, enterprise users face a problem with minimizing migration time and cloud resource providers face a dilemma of resource allocation problem, with the objective of maximizing the migration utility of enterprise users while minimizing the cost of cloud resource providers. In order to achieve them, this paper considered cloud migration objectives including cloud migration time, cloud migration utility, and cloud data center cost, and proposed a resource allocation model for enterprise applications migration into the cloud. The model is divided into two stages: the bandwidth allocation for enterprise applications migration to the cloud and the physical resource allocation of cloud resource providers for enterprise applications deployment into the cloud. In the first stage, we aim to minimize the cloud migration time for enterprise applications, and propose a scheme of bandwidth allocation for each component of applications. In the second stage, we present the resource allocation of cloud resource providers and propose a gradient-based algorithm which can achieve optimal resource allocation. Finally, we give some numerical simulation results to illustrate the performance of the proposed algorithm.

Community Resilience, Psychological Resilience, and Depressive Symptoms: An Examination of the Mississippi Gulf Coast 10 Years After Hurricane Katrina and 5 Years After the Deepwater Horizon Oil Spill

2017 ◽  
Vol 12 (2) ◽  
pp. 241-248 ◽  
Author(s):  
Joohee Lee ◽  
Bret J. Blackmon ◽  
David M. Cochran ◽  
Bandana Kar ◽  
Timothy A. Rehner ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Hurricane Katrina ◽  
Oil Spill ◽  
Gulf Coast ◽  
Community Resilience ◽  
Deepwater Horizon ◽  
Psychological Resilience ◽  
Deepwater Horizon Oil Spill ◽  
Mississippi Gulf Coast

AbstractObjectiveThis study examined the role of community resilience and psychological resilience on depressive symptoms in areas on the Mississippi Gulf Coast that have experienced multiple disasters.MethodsSurvey administration took place in the spring of 2015 to a spatially stratified, random sample of households. This analysis included a total of 294 subjects who lived in 1 of the 3 counties of the Mississippi Gulf Coast at the time of both Hurricane Katrina in 2005 and the Deepwater Horizon oil spill in 2010. The survey included the Communities Advancing Resilience Toolkit (CART) scale, the Connor-Davidson Resilience Scale (CD-RISC 10), and the Center for Epidemiologic Studies Depression Scale (CES-D).ResultsThere was a significant inverse relationship between psychological resilience and depressive symptoms and a significant positive relationship between community resilience and psychological resilience. The results also revealed that community resilience was indirectly related to depressive symptoms through the mediating variable of psychological resilience.ConclusionsThese findings highlight the importance of psychological resilience in long-term disaster recovery and imply that long-term recovery efforts should address factors associated with both psychological and community resilience to improve mental health outcomes. (Disaster Med Public Health Preparedness. 2018;12:241–248)

An Efficient Kernel FCM and Artificial Fish Swarm Optimization-Based Optimal Resource Allocation in Cloud

2020 ◽  
Vol 29 (16) ◽  
pp. 2050253
Author(s):  
Pravin Albert ◽  
Manikandan Nanjappan
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Optimal Resource Allocation ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Artificial Fish Swarm ◽  
Optimal Resource ◽  
And Task

Cloud computing model allows service-oriented system that fulfills the needs of the consumers. Capable resource management and task allocation are the important issues in cloud computing. Performance of the task scheduling method directly interrupts the utilization of cloud computing resources and the quality of experience of users. For that reason, reasonable virtual machine (VM) allocation and task scheduling are extremely important. In this paper, an efficient resource allocation model is proposed. Initially, the virtual machines are clustered with the help of Kernel Fuzzy [Formula: see text]-Means Clustering (KFCM) algorithm to reduce the complexity. After the clustering process, the user tasks are allocated to the particular VM using artificial fish swarm optimization (AFSO) algorithm. A multi-objective function is designed to achieve an optimal resource allocation. The performance of the suggested technique is tested in terms of different metrics.

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