QoS Modelling for Software Service Improvement using Adaptive Learning
Chinnam Subbarao1, I. Ramesh Babu2
1Chinnam Subbarao, Research Scholar, CSE, Acharya Nagarjuna University, Guntur.
2Dr. I. Ramesh Babu, Research Supervisor, CSE, Acharya Nagarjuna University, Guntur
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4920-4925 | Volume-8 Issue-12, October 2019. | Retrieval Number: L35531081219/2019©BEIESP | DOI: 10.35940/ijitee.L3553.1081219
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© 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: In this paper, we discussed about quality of service (QoS) related to software implementation for outsourced applications. Quality of service is key challenge to produce distributed software services and mainly focused on relative service provide based on possessions usage of distributed environment. In this work, we presented effective QOS Machine Learning approach to calculate and predict quality of service with respect to outside environment conditions. We also presented depth analysis on relative and successive correlations with enhanced performance of resource classification. Improve and maintain quality of service for dynamic resource utilization and perform application level semantic data relation. The objective of research work is to provide a hybrid learner’s resolution that increases the precision while keeping prototype complication passable. Our proposed experimental results give more accurate and efficiency with respect to traditional approaches.
Keywords: Adaptive Learning, Quality of service (QoS), Control Primitive, Hybrid Multi-Leaners, Cloud-based Software, Linear Regression and Machine Learning.
Scope of the Article: Machine Learning.