M
Mohsen Amini Salehi
Researcher at University of Louisiana at Lafayette
Publications - 108
Citations - 1313
Mohsen Amini Salehi is an academic researcher from University of Louisiana at Lafayette. The author has contributed to research in topics: Cloud computing & Computer science. The author has an hindex of 18, co-authored 95 publications receiving 1028 citations. Previous affiliations of Mohsen Amini Salehi include Islamic Azad University of Mashhad & Islamic Azad University.
Papers
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Book ChapterDOI
Adapting market-oriented scheduling policies for cloud computing
TL;DR: This work proposes two market-oriented scheduling policies that aim at satisfying the application deadline by extending the computational capacity of local resources via hiring resource from Cloud providers that are implemented in Gridbus broker as a user-level broker.
Proceedings ArticleDOI
A Comprehensive Survey on Text Summarization Systems
TL;DR: This paper presents a taxonomy of summarization systems and defines the most important criteria for a summary which can be generated by a system and goes through main criteria for evaluating a text summarization.
Journal ArticleDOI
Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services
TL;DR: In this article, the authors propose a QoS-aware scheduling component that maps transcoding tasks to the Virtual Machines (VMs) by considering the affinity of the transcoding task with the allocated heterogeneous VMs.
Proceedings ArticleDOI
VLSC: Video Live Streaming Using Cloud Services
TL;DR: The feasibility of cloud-based transcoding for live video streams is demonstrated and the efficacy of the proposed scheduling method in satisfying viewers' QoS demands without imposing extra cost to the stream provider is demonstrated.
Journal ArticleDOI
Stochastic-based robust dynamic resource allocation for independent tasks in a heterogeneous computing system
Mohsen Amini Salehi,Jay Smith,Anthony A. Maciejewski,Howard Jay Siegel,Edwin K. P. Chong,Jonathan Apodaca,Luis Diego Briceno,Timothy Renner,Vladimir Shestak,Joshua Ladd,Andrew M. Sutton,David Janovy,Sudha Govindasamy,Amin Alqudah,Rinku Dewri,Puneet Prakash +15 more
TL;DR: A stochastic robustness measure to facilitate resource allocation decisions in a dynamic environment where tasks are subject to individual hard deadlines and each task requires some input data to start execution and designs novel resource allocation techniques that work in immediate and batch modes.