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ARMCO: Advanced topics in resource management for ubiquitous cloud computing: An adaptive approach
Affiliation:1. University Politehnica of Bucharest, Romania;2. UPMC Sorbonne Universités, Paris, France;1. School of Statistics, University of International Business and Economics, Beijing 100029, PR China;2. Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA;1. Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Computer Science Department, Luis Enrique Erro No. 1, Santa María Tonantzintla, Puebla 72840, Mexico;2. Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Computer Science Department, Evolutionary Computation Group (EVOCINV), Av. IPN No. 2508, San Pedro Zacatenco, Mexico City 07360, Mexico;1. University of Tennessee, Knoxville, United States;2. The Ohio State University, United States;1. INSERM, U1142, LIMICS, F-75006, Paris, France;2. Sorbonne Universités, UPMC Univ. Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France;3. Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142), F-93430, Villetaneuse, France;4. Assistance Publique-Hôpitaux de Paris, 75004, Paris, France
Abstract:Cloud Computing can be seen as one of the latest major evolution in computing offering unlimited possibility to use ICT in various domains: business, smart cities, medicine, environmental computing, mobile systems, design and implementation of cyber-infrastructures. The recent expansion of Cloud Systems has led to adapting resource management solutions for large number of wide distributed and heterogeneous datacenters. The adaptive methods used in this context are oriented on: self-stabilizing, self-organizing and autonomic systems; dynamic, adaptive and machine learning based distributed algorithms; fault tolerance, reliability, availability of distributed systems. The pay-per-use economic model of Cloud Computing comes with a new challenge: maximizing the profit for service providers, minimizing the total cost for customers and being friendly with the environment.This special issue presents advances in virtual machine assignment and placement, multi-objective and multi-constraints job scheduling, resource management in federated Clouds and in heterogeneous environments, dynamic topology for data distribution, workflow performance improvement, energy efficiency techniques and assurance of Service Level Agreements.
Keywords:Resource management  Task scheduling  Adaptive methods  Cloud computing  Ubiquitous systems
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