Shared Determinants for Human Papillomavirus and COVID-19 Vaccination Intention: An Opportunity for Resource Consolidation

Purpose This study aimed at targeting shared factors that influence the prevention of multiple diseases, which can help address various health problems simultaneously. We identified correlates of human papillomavirus (HPV) vaccination that overlap with COVID-19 vaccination. Design Cross-sectional survey data. Setting Online Qualtrics recruitment panel. Subject Religious parents of 342 adolescents who were unvaccinated for HPV (response rate was 68.1%). Measures Outcome variables were COVID-19 vaccination intention for (1) self, (2) child, and (3) HPV vaccination intention for child. Independent variables were psychological factors. Covariates were sociodemographic and socioeconomic factors. Analysis We conducted multivariate linear regressions on each outcome variable after controlling for covariates. Result Some psychological correlates of HPV overlapped as protective factors for all three outcomes. Higher perceived vulnerability of child to HPV was associated with higher vaccination intention against COVID-19 for self (β = .37, 95% confidence interval [CI] = .25–.48), child (β = .32, .21–.44), and HPV for child (β = .38, .27–.49). Higher perceived response efficacy of HPV vaccine was associated with greater vaccination intention against COVID-19 for self (β = .46, .33–.59), child (β = .41, .28–.53), and HPV for child (β = .75, .64–.85). Conclusion Given the overlap in HPV and COVID-19 vaccination correlates, interventions should target shared factors that address both diseases to maximize public health efforts. A major limitation of this study is the inability to measure the actual vaccination behavior.

An Intelligent and Cost-Efficient Resource Consolidation Algorithm in Nanoscale Computing Environments

Because the Internet of things (IoT) and fog computing are prevalent, an efficient resource consolidation scheme in nanoscale computing environments is urgently needed. In nanoscale environments, a great many small devices collaborate to achieve a predefined goal. The representative case would be the edge cloud, where small computing servers are deployed close to the cloud users to enhance the responsiveness and reduce turnaround time. In this paper, we propose an intelligent and cost-efficient resource consolidation algorithm in nanoscale computing environments. The proposed algorithm is designed to predict nanoscale devices’ scheduling decisions and perform the resource consolidation that reconfigures cloud resources dynamically when needed without interrupting and disconnecting the cloud user. Because of the large number of nanoscale devices in the system, we developed an efficient resource consolidation algorithm in terms of complexity and employed the hidden Markov model to predict the devices’ scheduling decision. The performance evaluation shows that our resource consolidation algorithm is effective for predicting the devices’ scheduling decisions and efficiency in terms of overhead cost and complexity.

Phantom: Towards Vendor-Agnostic Resource Consolidation in Cloud Environments

Mobile-oriented internet technologies such as mobile cloud computing are gaining wider popularity in the IT industry. These technologies are aimed at improving the user internet usage experience by employing state-of-the-art technologies or their combination. One of the most important parts of modern mobile-oriented future internet is cloud computing. Modern mobile devices use cloud computing technology to host, share and store data on the network. This helps mobile users to avail different internet services in a simple, cost-effective and easy way. In this paper, we shall discuss the issues in mobile cloud resource management followed by a vendor-agnostic resource consolidation approach named Phantom, to improve the resource allocation challenges in mobile cloud environments. The proposed scheme exploits software-defined networks (SDNs) to introduce vendor-agnostic concept and utilizes a graph-theoretic approach to achieve its objectives. Simulation results demonstrate the efficiency of our proposed approach in improving application service response time.

v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers

Cloud computing systems are popular in computing industry for their ease of use and wide range of applications. These systems offer services that can be used over the Internet. Due to their wide popularity and usage, cloud computing systems and their services often face issues resource management related challenges. In this paper, we present v-Mapper, a resource consolidation scheme which implements network resource management concepts through software-defined networking (SDN) control features. The paper makes three major contributions: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a cloud service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. We tested our scheme with other techniques in terms of average task processing time, service delay and bandwidth usage. Our results demonstrate that v-Mapper outperforms other techniques and delivers significant improvement in system’s performance.