The rapid growth in the size and capacity of data centers driven by a continual rise in the number of servers and other IT equipment is causing an exponential increase in the demand for power. All data centers are plagued by the operational presence of thousands of servers as major components. These servers consume a huge amount of power while performing little in terms of useful work. In an average server environment, 30% of servers are “zombies”- they merely consume power while having a utilization ratio of only 5 to 10 %. Server virtualization contributes to this problem by offering an opportunity to consolidate multiple underutilized volume servers into a single physical server, thereby reducing the physical and environmental footprint of data centers. This paper suggests implementing “server virtualization” to achieve energy efficient data centers. The proposed technique increases the utilization ratio of underutilized servers up to 50%, saving a huge amount of power and at the same time reducing the emission of greenhouse gases.
Przedmiotem artykułu jest wirtualizacja serwera Omówiono różne typy wirtualizacji. Przeprowadzono testy wydajnościowe wybranych usług w sieciach komputerowych opartych na wirtualnych serwerach Windows Server 2008 i Debian GNU/Linux.
EN
This paper is devoted to server virtualization services. Different types of virtualizations are presented. Efficiency tests of selected services in computer networks based on virtual servers Windows Server 2008 iDebian GNU/Linux were made.
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This work analyses the performance of Hadoop, an implementation of the MapReduce programming model for distributed parallel computing, executing on a virtualisation environment comprised of 1+16 nodes running the VMWare workstation software. A set of experiments using the standard Hadoop benchmarks has been designed in order to determine whether or not significant reductions in the execution time of computations are experienced when using Hadoop on this virtualisation platform on a departmental cloud. Our findings indicate that a significant decrease in computing times is observed under these conditions. They also highlight how overheads and virtualisation in a distributed environment hinder the possibility of achieving the maximum (peak) performance.
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