Interactive Application Deployment Planning for Heterogeneous Computing Continuums

2021 ◽  
pp. 551-560
Author(s):  
Daniel Hass ◽  
Josef Spillner
Keyword(s):  
Heterogeneous Computing ◽  
Application Deployment ◽  
Interactive Application ◽  
Deployment Planning

scholarly journals INTEGRATION OF CLOUD COMPUTING PLATFORM TO GRID INFRASTRUCTURE

2014 ◽  
pp. 333-339
Author(s):  
Vladislav Falfushinsky ◽  
Olena Skarla ◽  
Vadim Tulchinsky
Keyword(s):  
Large Scale ◽  
Heterogeneous Computing ◽  
Service Providers ◽  
Hadron Collider ◽  
Virtual Organizations ◽  
Grid Infrastructure ◽  
Computing Platform ◽  
Grid Approach ◽  
Application Deployment ◽  
Technical Solutions

Both grid and cloud are used to organize large scale calculations and data processing on remote computers. Grid which became a basic computing infrastructure for the Large Hadron Collider experiments provides unified technical solutions for sharing and merging distributed heterogeneous computing resources within big collaboration groups. Cloud became popular among data centers and computing service providers because of flexibility, manageability and efficient hardware utilization. Both share common ideology “computing as a service”, so one can expect additional benefits from their integration. The paper describes our approach to the integration. We propose to use cloud within grid sites for acceleration of application deployment and easy support of multiple virtual organizations by grid sites. The cloud in grid approach has been implemented and tested in Ukrainian National Grid, a part of European Grid Infrastructure.

scholarly journals Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 308
Author(s):  
Juncal Alonso ◽  
Leire Orue-Echevarria ◽  
Eneko Osaba ◽  
Jesús López Lobo ◽  
Iñigo Martinez ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Heterogeneous Computing ◽  
Economies Of Scale ◽  
Fog Computing ◽  
Edge Computing ◽  
Continuous Control ◽  
Data Path ◽  
Self Healing ◽  
Application Deployment ◽  
Self Learning

The current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.

scholarly journals Scheduling Challenges in Mixed Critical Real-Time Heterogeneous Computing Platforms

2013 ◽  
Vol 18 ◽  
pp. 1891-1898
Author(s):  
Chetan Kumar N G ◽  
Sudhanshu Vyas ◽  
Ron K. Cytron ◽  
Christopher D. Gill ◽  
Joseph Zambreno ◽  
...  
Keyword(s):  
Real Time ◽  
Heterogeneous Computing ◽  
Computing Platforms

Multi-task Scheduling for PIM-based Heterogeneous Computing System

2020 ◽  
Author(s):  
Dawen Xu ◽  
Cheng Chu ◽  
Cheng Liu ◽  
Ying Wang ◽  
Xianzhong Zhou ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Computing System ◽  
Heterogeneous Computing System

5G EVE a European platform for 5G Application deployment

2020 ◽  
Author(s):  
Fabrizio Moggio ◽  
Mauro Boldi ◽  
Silvia Canale ◽  
Vincenzo Suraci ◽  
Claudio Casetti ◽  
...  
Keyword(s):  
Application Deployment
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