scholarly journals MiCADO-Edge: Towards an Application-level Orchestrator for the Cloud-to-Edge Computing Continuum

2021 ◽  
Vol 19 (4) ◽  
Author(s):  
Amjad Ullah ◽  
Huseyin Dagdeviren ◽  
Resmi C. Ariyattu ◽  
James DesLauriers ◽  
Tamas Kiss ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Video Processing ◽  
Time Management ◽  
Realistic Case ◽  
Healthcare Data ◽  
Computing Environments ◽  
Management Policies ◽  
Run Time ◽  
Monitoring Information

AbstractAutomated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.

A Software Evolution Process Model under the Circumstances of Cloud Computing

2011 ◽  
Vol 55-57 ◽  
pp. 1370-1375 ◽  
Author(s):  
Jian Wang ◽  
Na Zhao ◽  
Jin Guo You ◽  
Ling Yu Li ◽  
Lei Zhao
Keyword(s):  
Cloud Computing ◽  
Process Model ◽  
Software Evolution ◽  
Software Process ◽  
Evolution Process ◽  
Wide Spread ◽  
Realistic Case ◽  
Independent Functions ◽  
Depth Analysis ◽  
Computing Environments

With the wide spread of cloud computing technologies, we assume that increasing amount of attentions will be given to the area of software evolution processes in the context of cloud computing environments. Some major problems we are here facing now include: What kind of process a software evolution model under the circumstances of cloud computing must possess? What properties a software process must have as an attribute in order to effectively support the software evolution? How to modeling a process in the context of software evolution? While aiming to solve these mentioned problems, we focused our research on establishing a software evolution process model with independent functions as a component. In this article, the detailed discussion of our thoughts has been given and the in-depth analysis of our model is shown. Through a realistic case to which this model is applied, we concluded that our model provided the software evolution process with an effective support under the circumstances of cloud computing.

TDRM: tensor-based data representation and mining for healthcare data in cloud computing environments

2017 ◽  
Vol 74 (2) ◽  
pp. 592-614 ◽  
Author(s):  
Rajinder Sandhu ◽  
Navroop Kaur ◽  
Sandeep K. Sood ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Data Representation ◽  
Healthcare Data ◽  
Computing Environments

ATSDS: adaptive two-stage deadline-constrained workflow scheduling considering run-time circumstances in cloud computing environments

2016 ◽  
Vol 73 (6) ◽  
pp. 2430-2455 ◽  
Author(s):  
Reihaneh Khorsand ◽  
Faramarz Safi-Esfahani ◽  
Naser Nematbakhsh ◽  
Mehran Mohsenzade
Keyword(s):  
Cloud Computing ◽  
Workflow Scheduling ◽  
Two Stage ◽  
Computing Environments ◽  
Run Time

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

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