Cost minimization for deadline-constrained bag-of-tasks applications in federated hybrid clouds

A mathematical programming model is proposed for a resource allocation problem in federated clouds, where bag-of-tasks (BoT) applications are assigned to instance types with different costs and performance levels. The proposed model is a binary linear programming problem containing deadline and reso...

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Vydáno v:Future generation computer systems Ročník 71; s. 113 - 128
Hlavní autoři: Abdi, Somayeh, PourKarimi, Latif, Ahmadi, Mahmood, Zargari, Farzad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.06.2017
Témata:
Bag-of-tasks applications
Binary linear programming
Cost minimization
Deadline constrained
Federated hybrid clouds
Scheduling
Cost minimization
Scheduling
Bag-of-tasks applications
Binary linear programming
Deadline constrained
Federated hybrid clouds
ISSN:0167-739X, 1872-7115
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Shrnutí:A mathematical programming model is proposed for a resource allocation problem in federated clouds, where bag-of-tasks (BoT) applications are assigned to instance types with different costs and performance levels. The proposed model is a binary linear programming problem containing deadline and resource constraints in the cloud federations and by the objective of minimizing the total cost of applications. These constraints and objective are explicitly expressed using mathematical functions, and the model is solved with the CPLEX solver. This paper also discusses a post-optimality analysis that deals with stability in assignment problems. Numerical results show that the optimal cost and optimal solutions in the cloud federations are lower and more stable, respectively, than those presented by single-provider clouds. In contrast to optimality in single-provider clouds, that in the cloud federations is less sensitive to input data. •The cost of bag-of-tasks applications in a federated cloud is minimized.•Cost minimization is modeled as a binary linear programming problem.•The proposed model is solved with the CPLEX solver.•The sensitivity of optimal solutions to input data is investigated.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2017.01.036