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Nanjangud C. Narendra
Researcher at Ericsson
Publications - 127
Citations - 1887
Nanjangud C. Narendra is an academic researcher from Ericsson. The author has contributed to research in topics: Web service & WS-Policy. The author has an hindex of 22, co-authored 125 publications receiving 1709 citations. Previous affiliations of Nanjangud C. Narendra include IBM & Cognizant.
Papers
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Proceedings ArticleDOI
Everything as a Service (XaaS) on the Cloud: Origins, Current and Future Trends
TL;DR: The vast stream of the state of the art in Everything as a Service (XaaS) is investigated and an integrated view of XaaS is explored to help propose approaches for migrating applications to the cloud and exposing them as services.
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What can context do for web services
TL;DR: Context-aware Web service would significantly benefit the interactions between human, applications, and the environment.
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A reference architecture for managing dynamic inter-organizational business processes
TL;DR: The eSourcing Reference Architecture eSRA is presented, which enables a quick evaluation of not only research-based B2B-architectures but also of industry application suits and shows the usability and applicability in that with the help of eS RA, system designers directly establish a comprehensive understanding of fundamental B 2B concepts and develop higher-quality domain-specific architectures.
Proceedings ArticleDOI
On the Enhancement of BPEL Engines for Self-Healing Composite Web Services
Sattanathan Subramanian,Philippe Thiran,Nanjangud C. Narendra,Ghita Kouadri Mostéfaoui,Zakaria Maamar +4 more
TL;DR: In this paper, the authors propose an approach for enhancing BPEL engines with self-healing mechanisms to handle functional failures of component Web services during runtime. But this approach is limited to activeBPEL.
Journal ArticleDOI
Dynamic resource demand prediction and allocation in multi-tenant service clouds
Manish Verma,G. R. Gangadharan,Nanjangud C. Narendra,Ravi Vadlamani,Vidyadhar Inamdar,Lakshmi Ramachandran,Rodrigo N. Calheiros,Rajkumar Buyya +7 more
TL;DR: This paper presents a dynamic resource demand prediction and allocation framework in multi‐tenant service clouds that prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction.