GPU Accelerated TOPSIS Algorithm for QoS Aware Web Service Selection
M. Sri Yogalakshmi1, G. R., Karpagam2, N. G. Swetha3
1M Sri Yoga Lakshmi, PG Student, Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India.
2Dr G R Karpagam, Professor and Associate Head, Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India.
3N G Swetha, Assistant Professor, Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India. 

Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3159-3163 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6502018520/2020©BEIESP | DOI: 10.35940/ijrte.E6502.018520

Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Web service selection is the method of selecting the best web service based on non-functional aspects. With proliferation of web services, there is a demand for efficient approaches to select the best web services from the available pool. A number of Multi criteria decision making approaches are adopted for QoS aware web service selection. The exploitation in web service deployment increases the computation power in executing web service selection. Graphics processing unit (GPU) can achieve reduction in computation power by performing multiple tasks at a time. The independent tasks in the algorithm are parallelized by utilizing GPU in order to achieve more efficiency. This works proves that GPU acceleration can be utilized in parallelizing TOPSIS algorithm for web service selection.
Keywords: High Performance Computing, Parallel Computingo GPU, Parallel TOPSIS Algorithm, Web Service Selection, Multi-Criteria Decision Making (MCDM).
Scope of the Article: High Performance Computing.