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PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING
Anti-Load Balancing to Reduce Energy Consumption
C. Thiam and G. Dacosta
University Paul Sabatier, Toulouse, France
C. Thiam, G. Dacosta, "Anti-Load Balancing to Reduce Energy Consumption", in , (Editors), "Proceedings of the Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 30, 2011. doi:10.4203/ccp.95.30
Keywords: energy, heuristic, virtual machines, grid.
power consumption as a result of computational servers and data centers. We will consider the opposite problem of load balancing: to concentrate the load on a minimum number of machines. Some solutions, algorithms, include numerous approaches, through the network protocols , modeling of consumption , scheduling of tasks by predicting idle times . To evaluate improvements we choose to use a grid simulator. We present here a series of experiments on the anti-load balancing for significantly reducing energy consumption.
To analyse the results of our simulations, we focus on the energy of the system. Indeed, the energy of the system will direct a guide to assess the impact of our method on the reduction of energy. We first present the evolution of the energy of a system to observe how it behaves when we vary the number of guests. In the first series of experiments we first run two jobs on two machines, then on two different machines. In the second experiment we vary the load of resources by performing a task migration. VMs on hosts that are at minimum load (in our case a load less than 50%) are selected as potential node to migrate their load.
In the second experiment we check the evolution of energy in the execution of jobs with no migration and with migration. We note that the evolution of the gap as the number of jobs increases. As might be expected, where the number of jobs is high, around 103, the energy is the same for the two versions. This is due to saturation of the data center, when migration can no longer be done.
For the third experiment, we try to avoid having a heating point. With a low load, energy consumption rises as the number of jobs increases. However, energy consuption decreases if nodes are not overloaded, whatever the load is. With the last experiment we study three different aggressive modes:
The use of anti-load balancing provides a power gain in managing data centers. Our solution is based on a centralized architecture. The results of our simulations show that our solution fits better when the grid size increases. A logical extension of this work, to better optimize our approach is to integrate quality of service (QoS) but also to address the distributed solution.
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