134 research outputs found
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Cloud computing systems promise to offer subscription-oriented,
enterprise-quality computing services to users worldwide. With the increased
demand for delivering services to a large number of users, they need to offer
differentiated services to users and meet their quality expectations. Existing
resource management systems in data centers are yet to support Service Level
Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to
realize cloud computing and utility computing. In addition, no work has been
done to collectively incorporate customer-driven service management,
computational risk management, and autonomic resource management into a
market-based resource management system to target the rapidly changing
enterprise requirements of Cloud computing. This paper presents vision,
challenges, and architectural elements of SLA-oriented resource management. The
proposed architecture supports integration of marketbased provisioning policies
and virtualisation technologies for flexible allocation of resources to
applications. The performance results obtained from our working prototype
system shows the feasibility and effectiveness of SLA-based resource
provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE
International Conference on Cloud and Service Computing (CSC 2011, IEEE
Press, USA), Hong Kong, China, December 12-14, 201
Fog Device-as-a-Service (FDaaS): A Framework for Service Deployment in Public Fog Environments
Meeting the requirements of future services with time sensitivity and
handling sudden load spikes of the services in Fog computing environments are
challenging tasks due to the lack of publicly available Fog nodes and their
characteristics. Researchers have assumed that the traditional autoscaling
techniques, with lightweight virtualisation technology (containers), can be
used to provide autoscaling features in Fog computing environments, few
researchers have built the platform by exploiting the default autoscaling
techniques of the containerisation orchestration tools or systems. However, the
adoption of these techniques alone, in a publicly available Fog infrastructure,
does not guarantee Quality of Service (QoS) due to the heterogeneity of Fog
devices and their characteristics, such as frequent resource changes and high
mobility. To tackle this challenge, in this work we developed a Fog as a
Service (FaaS) framework that can create, configure and manage the containers
which are running on the Fog devices to deploy services. This work presents the
key techniques and algorithms which are responsible for handling sudden load
spikes of the services to meet the QoS of the application. This work provides
an evaluation by comparing it with existing techniques under real scenarios.
The experiment results show that our proposed approach maximises the satisfied
service requests by an average of 1.9 times in different scenarios.Comment: 10 Pages, 13 Figure
Multiple Linear Regression-Based Energy-Aware Resource Allocation in the Fog Computing Environment
Fog computing is a promising computing paradigm for time-sensitive Internet
of Things (IoT) applications. It helps to process data close to the users, in
order to deliver faster processing outcomes than the Cloud; it also helps to
reduce network traffic. The computation environment in the Fog computing is
highly dynamic and most of the Fog devices are battery powered hence the
chances of application failure is high which leads to delaying the application
outcome. On the other hand, if we rerun the application in other devices after
the failure it will not comply with time-sensitiveness. To solve this problem,
we need to run applications in an energy-efficient manner which is a
challenging task due to the dynamic nature of Fog computing environment. It is
required to schedule application in such a way that the application should not
fail due to the unavailability of energy. In this paper, we propose a multiple
linear, regression-based resource allocation mechanism to run applications in
an energy-aware manner in the Fog computing environment to minimise failures
due to energy constraint. Prior works lack of energy-aware application
execution considering dynamism of Fog environment. Hence, we propose A multiple
linear regression-based approach which can achieve such objectives. We present
a sustainable energy-aware framework and algorithm which execute applications
in Fog environment in an energy-aware manner. The trade-off between
energy-efficient allocation and application execution time has been
investigated and shown to have a minimum negative impact on the system for
energy-aware allocation. We compared our proposed method with existing
approaches. Our proposed approach minimises the delay and processing by 20%,
and 17% compared with the existing one. Furthermore, SLA violation decrease by
57% for the proposed energy-aware allocation.Comment: 8 Pages, 9 Figure
Future Path Toward TB Vaccine Development: Boosting BCG or Re-educating by a New Subunit Vaccine
Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (Mtb), kills 5,000 people per day globally. Rapid development and spread of various multi drug-resistant strains of Mtb emphasize that an effective vaccine is still the most cost-effectives and efficient way of controlling and eradicating TB. Bacillus Calmette-Guerin (BCG), the only licensed TB vaccine, still remains the most widely administered human vaccine, but is inefficient in protecting from pulmonary TB in adults. The protective immunity afforded by BCG is thought to wane with time and considered to last only through adolescent years. Heterologous boosting of BCG-primed immune responses using a subunit vaccine represents a promising vaccination approach to promote strong cellular responses against Mtb. In our earlier studies, we discovered lipopeptides of ESAT-6 antigen with strong potential as a subunit vaccine candidate. Here, we have investigated that potential as a booster to BCG vaccine in both a pre-exposure preventive vaccine and a post-exposure therapeutic vaccine setting. Surprisingly, our results demonstrated that boosting BCG with subunit vaccine shortly before Mtb challenge did not improve the BCG-primed immunity, whereas the subunit vaccine boost after Mtb challenge markedly improved the quantity and quality of effector T cell responses and significantly reduced Mtb load in lungs, liver and spleen in mice. These studies suggest that ESAT-6 lipopeptide-based subunit vaccine was ineffective in overcoming the apparent immunomodulation induced by BCG vaccine in Mtb uninfected mice, but upon infection, the subunit vaccine is effective in re-educating the protective immunity against Mtb infection. These important results have significant implications in the design and investigation of effective vaccine strategies and immunotherapeutic approaches for individuals who have been pre-immunized with BCG vaccine but still get infected with Mtb
Convalescent Plasma: An Evidence-Based Old Therapy to Treat Novel Coronavirus Patients
Novel Coronavirus (nCoV-2019) is a highly infectious viral outbreak that has so far infected more than 110 million people worldwide. Fast viral transmission and high infection rates have severely affected the entire population, especially the old aged and comorbid individuals leaving significantly less time to find some effective treatment strategy. In these challenging times, convalescent plasma (CP) therapy came as a ray of hope to save humankind. It is a form of passive immunization that has been used to treat various infectious diseases since 1890, including the 1918 Spanish flu, 2002/03 SARS-CoV, 2009 H1N1, 2012 MERS-CoV, and 2014 Ebola outbreak. The transfusion includes administration of CP containing a high value of neutralizing antibodies against the virus in hospitalized patients. This chapter summarizes the potential outcome of CP therapy in the treatment of nCoV-2019 patients
A survey of detection and mitigation for fake images on social media platforms
Recently, the spread of fake images on social media platforms has become a significant
concern for individuals, organizations, and governments. These images are often created using
sophisticated techniques to spread misinformation, influence public opinion, and threaten national
security. This paper begins by defining fake images and their potential impact on society, including
the spread of misinformation and the erosion of trust in digital media. This paper also examines the
different types of fake images and their challenges for detection. We then review the recent approaches
proposed for detecting fake images, including digital forensics, machine learning, and deep learning.
These approaches are evaluated in terms of their strengths and limitations, highlighting the need
for further research. This paper also highlights the need for multimodal approaches that combine
multiple sources of information, such as text, images, and videos. Furthermore, we present an
overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection.
This paper concludes by discussing future directions for fake image detection research, such as
developing more robust and explainable methods, cross-modal fake detection, and the integration
of social context. It also emphasizes the need for interdisciplinary research that combines computer
science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake
images. This survey paper will be a valuable resource for researchers and practitioners working on
fake image detection on social media platforms.peer-reviewe
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