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BINARY PROGRAMMING MODELS FOR THE SERVER CONSOLIDATION PROBLEM IN DATA CENTERS
Abstract
The last decade has witnessed an exponential increasing in the number and the size of data centers. This expansion was driven by the increasing demand for digital products and services from the various sectors of the economy. Data center operators are faced with an increasing pressure for managing their facilities more efficiently and cost effectively such that they achieve 100 percent uptime. This is a difficult task to overcome, taking into account that energy consumption is the fastest growing component of data center. In an effort to answer this challenge, data center operators are using a virtualization technology. It enables each user to work with his own virtual machine that has its own operating system. Instead that the users have several independent computers, they run at the same time on the same server. Server consolidation is a technology that simplifies the administration of a data center. It improves energy efficiency by improving resource utilizations and reducing the number of active servers (physical machines) in the contemporary service-oriented data centers. We formulate several binary programming models for the static server consolidation problem that determine the best placement of virtual machines on servers (physical machines) in a data center. One model minimizes the energy consumption of the data center. In case that the servers are of the same type this is equivalent with the minimization of the number of active servers. Another model maximizes the use of server resources (CPU, memory, bandwidth etc). Decision variables are represented by a binary vector that shows which servers are active and by a binary matrix that shows the allocation of virtual machines to servers.
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References10
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