Mobility aware autonomic approach for the migration of application modules in fog computing environment

में बचाया:
ग्रंथसूची विवरण
में प्रकाशित:Journal of Ambient Intelligence and Humanized Computing vol. 11, no. 11 (Nov 2020), p. 5259
मुख्य लेखक: Martin, John Paul
अन्य लेखक: Kandasamy, A, Chandrasekaran, K
प्रकाशित:
Springer Nature B.V.
विषय:
ऑनलाइन पहुंच:Citation/Abstract
Full Text
Full Text - PDF
टैग: टैग जोड़ें
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!

MARC

LEADER 00000nab a2200000uu 4500
001 2920547202
003 UK-CbPIL
022 |a 1868-5137 
022 |a 1868-5145 
024 7 |a 10.1007/s12652-020-01854-x  |2 doi 
035 |a 2920547202 
045 2 |b d20201101  |b d20201130 
100 1 |a Martin, John Paul  |u National Institute of Technology Karnataka, Department of MACS, Surathkal, India (GRID:grid.444525.6) (ISNI:0000 0000 9398 3798) 
245 1 |a Mobility aware autonomic approach for the migration of application modules in fog computing environment 
260 |b Springer Nature B.V.  |c Nov 2020 
513 |a Journal Article 
520 3 |a The fog computing paradigm has emanated as a widespread computing technology to support the execution of the internet of things applications. The paradigm introduces a distributed, hierarchical layer of nodes collaboratively working together as the Fog layer. User devices connected to Fog nodes are often non-stationary. The location-aware attribute of Fog computing, deems it necessary to provide uninterrupted services to the users, irrespective of their locations. Migration of user application modules among the Fog nodes is an efficient solution to tackle this issue. In this paper, an autonomic framework MAMF, is proposed to perform migrations of containers running user modules, while satisfying the Quality of Service requirements. The hybrid framework employing MAPE loop concepts and Genetic Algorithm, addresses the migration of containers in the Fog environment, while ensuring application delivery deadlines. The approach uses the pre-determined value of user location for the next time instant, to initiate the migration process. The framework was modelled and evaluated in iFogSim toolkit. The re-allocation problem was also mathematically modelled as an Integer Linear Programming problem. Experimental results indicate that the approach offers an improvement in terms of network usage, execution cost and request execution delay, over the existing approaches. 
653 |a Forecasting techniques 
653 |a Linear programming 
653 |a Containers 
653 |a Internet of Things 
653 |a Genetic algorithms 
653 |a Integer programming 
653 |a Route optimization 
653 |a Edge computing 
653 |a Nodes 
653 |a Modules 
653 |a Performance evaluation 
700 1 |a Kandasamy, A  |u National Institute of Technology Karnataka, Department of MACS, Surathkal, India (GRID:grid.444525.6) (ISNI:0000 0000 9398 3798) 
700 1 |a Chandrasekaran, K  |u National Institute of Technology Karnataka, Department of CSE, Surathkal, India (GRID:grid.444525.6) (ISNI:0000 0000 9398 3798) 
773 0 |t Journal of Ambient Intelligence and Humanized Computing  |g vol. 11, no. 11 (Nov 2020), p. 5259 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2920547202/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/2920547202/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/2920547202/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch