M. Usman Ashraf

Work place: GC Women University Sialkot/ Department of Computer Science & Information Technology, Pakistan

E-mail: usman.ashraf@gcwus.edu.pk


Research Interests: Software Construction, Software Engineering, Ubiquitous Computing, Parallel Computing, Mathematics of Computing


Dr. M. Usman Ashraf was born in Sialkot, Pakistan in 1988. He received the B.Sc. degree in Mathematics from The University of Punjab, Pakistan in 2007, M.S. degrees in Computer Science from the University of Lahore, Pakistan, in 2014 and currently doing Ph.D. degree in Computer Science from King Abdulaziz University, Jeddah, Saudi Arabia. He is Assistant Professor in the Departmentof Computer Science, GC Women University, Sialkot, Pakistan. He is also a member of Software Engineering group at King Abdulaziz University Jeddah, Saudi Arabia. From 2010 to 2014, he was a Senior Software Engineer (SSE) at Coeus Software solutions, GmbH. His research interests include High Performance Computing (HPC), Parallel Computing, Exascale Computing, Ubiquitous computing, Software Engineering, Location Based Service Systems and Recommender Systems.

Author Articles
Prediction and Monitoring Agents using Weblogs for improved Disaster Recovery in Cloud

By Rushba Javed Sidra Anwar Khadija Bibi M. Usman Ashraf Samia Siddique

DOI: https://doi.org/10.5815/ijitcs.2019.04.02, Pub. Date: 8 Apr. 2019

Disaster recovery is a continuous dilemma in cloud platform. Though sudden scaling up and scaling down of user’s resource requests is available, the problem of servers down still persists getting users locked at vendor’s end. This requires such a monitoring agent which will reduce the chances of disaster occurrence and server downtime. To come up with an efficient approach, previous researchers’ techniques are analyzed and compared regarding prediction and monitoring of outages in cloud computing. A dual functionality Prediction and Monitoring Agent is proposed to intelligently monitor users’ resources requests and to predict coming surges in web traffic using Linear Regression algorithm. This solution will help to predict the user’s future requests’ behavior, to monitor current progress of resources’ usage, server virtualization and to improve overall disaster recovery process in Cloud Computing.

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