Section: New Results

Run-time/middle-ware level

In [19] , we present BtrPlace as an application of the dynamic bin packing problem with a focus on its dynamic and heterogeneous nature. We advocate flexibility to answer these issues and present the theoretical aspects of BtrPlace and its modeling using Constraint Programming.

We also continued our work on scheduling VM migrations. In [14] , [17] , we propose a model for VM migration that consider their memory workload and the network topology. This model was then implemented in place of the previous migration scheduler in BtrPlace. Experiments on a real testbed show the new scheduler outperforms state-of-the-art approaches that cap the migration parallelism by a constant to reduce the completion time. Besides an optimal capping, it reduces the migration duration by 20.4% on average and the completion time by 28.1%. In a maintenance operation involving 96 VMs to migrate between 72 servers, it saves 21.5% Joules against the native BtrPlace. Finally, its current library of 6 constraints allows administrators to address temporal and energy concerns, for example to adapt the schedule and fit a power budget.

Finally, in [10] we transfer the principles of using Constraint Programming to propose a multi-objective job placement algorithm devoted to High Performance Computing (HPC). One of the key decisions made by both MapReduce and HPC cluster management frameworks is the placement of jobs within a cluster. To make this decision, they consider factors like resource constraints within a node or the proximity of data to a process. However, they fail to account for the degree of collocation on the cluster's nodes. A tight process placement can create contention for the intra-node shared resources, such as shared caches, memory, disk, or network bandwidth. A loose placement would create less contention, but exacerbate network delays and increase cluster-wide power consumption. Finding the best job placement is challenging, because among many possible placements, we need to find one that gives us an acceptable trade-off between performance and power consumption. We then propose to tackle the problem via multi-objective optimization. Our solution is able to balance conflicting objectives specified by the user and efficiently find a suitable job placement.