P. Dhyani

Work place: Banasthali Vidyapith, Computer Science Department, Jaipur-302019, India

E-mail: dhyani_p@yahoo.com


Research Interests: Applied computer science, Computer systems and computational processes, Theoretical Computer Science


Praveen Dhyani received a Ph.D. degree from Birla Institute of Technology and Science (BITS), Pilani, India. Currently he is a Professor of Computer Science and Executive Director at Banasthali University Jaipur Campus. Previously Dr. Dhyani established and headed national and international centers of BIT MESRA at Jaipur, Bahrain, Muscat, and RAK (UAE). His R&D accomplishments include electronic devices to aid foot drop patients and development of voice operated wheelchair. He is also a member of the Program me Execution Committee (PEC), UIDAI Biometric Centre of Competence (UBCC), Unique Identification Authority of India (UIDAI), Planning Commission, Government of India.

Author Articles
A Multi-Objective Optimization of Cloud Based SLA-Violation Prediction and Adaptation

By Vivek Gaur P. Dhyani O.P. Rishi

DOI: https://doi.org/10.5815/ijitcs.2016.06.08, Pub. Date: 8 Jun. 2016

Monitoring of Cloud services is vital for both service providing organizations and consumers. The service providers need to maintain the quality of service to comply their services with the QoS parameters defined in SLA's such as response time, throughput, delay through continuous monitoring of services. The dynamic monitoring involves prediction of SLA violations and subsequent adaptation of the service compositions. The task of adaptation is in fact the task of discovering another plausible composition in the face of services recorded to have generated QoS violations. QoS- Driven Utility based service composition approach considers the individual user's priorities for QoS parameters and determines the overall utility measure of the service composition for the end user. In this work we present the problem of service composition adaptation as a multi-objective assignment optimization problem, which in turn is a NP-hard problem. The evolutionary algorithm GA with Tabu has been formulated as a Memetic and Pareto optimal approach for the adaptation problem and analyzed for efficiency in solving the problem.

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