scholarly journals Service Level Agreement Based Secured Data Analytics Framework for Healthcare Systems

2022 ◽  
Vol 32 (2) ◽  
pp. 1277-1291
Author(s):  
S. Benila ◽  
N. Usha Bhanu
Keyword(s):  
Data Analytics ◽  
Service Level Agreement ◽  
Service Level ◽  
Healthcare Systems ◽  
Secured Data

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

scholarly journals Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

2021 ◽  
Vol 18 (3) ◽  
pp. 1327
Author(s):  
Leonardo J. Gutierrez ◽  
Kashif Rabbani ◽  
Oluwashina Joseph Ajayi ◽  
Samson Kahsay Gebresilassie ◽  
Joseph Rafferty ◽  
...  
Keyword(s):  
Mental Health ◽  
Internet Of Things ◽  
Data Acquisition ◽  
Identity Management ◽  
Service Level Agreement ◽  
Service Level ◽  
Self Organization ◽  
Future Research ◽  
Study Results ◽  
Open Issues

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.

scholarly journals HealthTEC Innovation Design - a proposal for a novel Master degree program based on Unmet Clinical Need, global Healthcare Challenges, and 21st century skills

2020 ◽  
Vol 6 (3) ◽  
pp. 599-603
Author(s):  
Michael Friebe
Keyword(s):  
Machine Learning ◽  
21St Century ◽  
21St Century Skills ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Data Gathering ◽  
Healthcare Systems ◽  
Disruptive Innovation ◽  
Incremental Innovation ◽  
Healthcare Provision

AbstractThe effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.

scholarly journals Supporting SLA via Adaptive Mapping and Heterogeneous Storage Devices in Ceph

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 847
Author(s):  
Sopanhapich Chum ◽  
Heekwon Park ◽  
Jongmoo Choi
Keyword(s):  
Positive Impact ◽  
Distributed Storage ◽  
Storage System ◽  
Hard Disk Drives ◽  
Service Level Agreement ◽  
Service Level ◽  
Cost Ratio ◽  
Solid State Drives ◽  
Storage Devices ◽  
Management Scheme

This paper proposes a new resource management scheme that supports SLA (Service-Level Agreement) in a bigdata distributed storage system. Basically, it makes use of two mapping modes, isolated mode and shared mode, in an adaptive manner. In specific, to ensure different QoS (Quality of Service) requirements among clients, it isolates storage devices so that urgent clients are not interfered by normal clients. When there is no urgent client, it switches to the shared mode so that normal clients can access all storage devices, thus achieving full performance. To provide this adaptability effectively, it devises two techniques, called logical cluster and normal inclusion. In addition, this paper explores how to exploit heterogeneous storage devices, HDDs (Hard Disk Drives) and SSDs (Solid State Drives), to support SLA. It examines two use cases and observes that separating data and metadata into different devices gives a positive impact on the performance per cost ratio. Real implementation-based evaluation results show that this proposal can satisfy the requirements of diverse clients and can provide better performance compared with a fixed mapping-based scheme.

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