Reference : Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service
Scientific journals : Article
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/30190
Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service
English
Tchernykh, Andrei mailto []
Lozano, Luz []
Schwiegelshohn, Uwe []
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Pecero, Johnatan []
Nesmachnow, Sergio []
Drosdov, Alexander []
1-Mar-2016
Journal of Grid Computing
Springer
14
Yes (verified by ORBilu)
International
1570-7873
1572-9184
Dordrecht
The Netherlands
[en] This paper focuses on a bi-objective experimental evaluation of online scheduling in the Infrastructure as a Service model of Cloud computing regarding income and power consumption objectives. In this model, customers have the choice between different service levels. Each service level is associated with a price per unit of job execution time, and a slack factor that determines the maximal time span to deliver the requested amount of computing resources. The system, via the scheduling algorithms, is responsible to guarantee the corresponding quality of service for all accepted jobs. Since we do not consider any optimistic scheduling approach, a job cannot be accepted if its service guarantee will not be observed assuming that all accepted jobs receive the requested resources. In this article, we analyze several scheduling algorithms with different cloud configurations and workloads, considering the maximization of the provider income and minimization of the total power consumption of a schedule. We distinguish algorithms depending on the type and amount of information they require: knowledge free, energy-aware, and speed-aware. First, to provide effective guidance in choosing a good strategy, we present a joint analysis of two conflicting goals based on the degradation in performance. The study addresses the behavior of each strategy under each metric. We assess the performance of different scheduling algorithms by determining a set of nondominated solutions that approximate the Pareto optimal set. We use a set coverage metric to compare the scheduling algorithms in terms of Pareto dominance.
University of Luxembourg: High Performance Computing - ULHPC ; SNT
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/30190
10.1007/s10723-015-9340-0
FnR ; FNR4770555 > Pascal Bouvry > Green@Cloud > Multi-Objective Metaheuristics for Energy-Aware Scheduling in Cloud Computing Systems > 01/10/2012 > 30/09/2015 > 2011

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