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Automated application component placement in data centers using mathematical programming
Author(s) -
Zhu Xiaoyun,
Santos Cipriano,
Beyer Dirk,
Ward Julie,
Singhal Sharad
Publication year - 2008
Publication title -
international journal of network management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.373
H-Index - 28
eISSN - 1099-1190
pISSN - 1055-7148
DOI - 10.1002/nem.707
Subject(s) - solver , computer science , component (thermodynamics) , distributed computing , scheduling (production processes) , integer programming , data center , grid , server , ranging , linear programming , job shop scheduling , network topology , mathematical optimization , algorithm , computer network , programming language , physics , thermodynamics , telecommunications , geometry , mathematics , routing (electronic design automation)
In this article we address the application component placement (ACP) problem for a data center. The problem is defined as follows: for a given topology of a network consisting of switches, servers and storage devices with varying capabilities, and for a given specification of a component‐based distributed application, decide which physical server should be assigned to each application component, such that the application's processing, communication and storage requirements are satisfied without creating bottlenecks in the infrastructure, and that scarce resources are used most efficiently. We explain how the ACP problem differs from traditional task assignment in distributed systems, or existing grid scheduling problems. We describe our approach of formalizing this problem using a mathematical optimization framework and further formulating it as a mixed integer program (MIP). We then present our ACP solver using GAMS and CPLEX to automate the decision‐making process. The solver was numerically tested on a number of examples, ranging from a 125‐server real data center to a set of hypothetical data centers with increasing size. In all cases the ACP solver found an optimal solution within a reasonably short time. In a numerical simulation comparing our solver to a random selection algorithm, our solver resulted in much more efficient use of scarce network resources and allowed more applications to be placed in the same infrastructure. Copyright © 2008 John Wiley & Sons, Ltd.