Authors:
Hamdi Gabsi
;
Rim Drira
and
Henda Hajjami Ben Ghezala
Affiliation:
RIADI Laboratory, National School of Computer Sciences, University of Manouba, La Manouba, Tunisia
Keyword(s):
Cloud Services Discovery, Cloud Services Selection, Cloud-based Software Development, Cloud Data-set, Natural Language Processing, Cloud Services Clustering.
Abstract:
The surging popularity of cloud services has led to the emergence of numerous cloud providers who offer various services. The great variety and the exponential proliferation of cloud services over the Web introduce several functionally similar offers with heterogeneous descriptions and contrasting APIs (Application Programming Interfaces). Due to this heterogeneity, efficient and accurate service discovery and selection, based on developers-specific requirements and terminology, have become a significant challenge that requires a high level of expertise and a steep documentation curve. In order to assist developers in handling these issues, first, we propose a Cloud Services Discovery and Selection Assistant (DESCA) based on a developer’s query expressed in natural language. Second, we offer to the developers a cloud data-set, named ULID (Unified cLoud servIces Data-set), where services offered by different cloud providers are collected, unified and classified based on their function
al features. The effectiveness of our contributions and their valuable insights to improve cloud services discovery and selection are demonstrated through evaluation experimentation.
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