Live Migration of C-Cells Sample Clauses

Live Migration of C-Cells. ‌ In the cloud-edge continuum, C-Cells must support continued execution between the cloud and the edge. This means not only being able to run in the cloud and in the edge, but also be able to begin executing somewhere, checkpoint and stop upon request, and resume execution elsewhere. In the literature this process is known as live migration [68]. Even though research in live migration is abundant [8, 69], CLOUDSKIN faces an additional difficulty: live migration needs to happen between heterogeneous hosts, potentially running different instruction sets. Traditional process migration tools like CRIU [8] do not support heterogeneity between migrated hosts. This is because the checkpointing mechanism relies heavily on the underlying operating sys- tem and will not support subtle changes in the environment, for example different shared libraries version. More modern migration tools, like pod checkpoint-restore in Kubernetes [70] will work as long as migration happens within the same instance of the deployed Kubernetes cluster. For CLOUDSKIN we deploy instances of the runtime in each host, with potentially different instruction sets, and use snapshots together with careful tracking of accesses to the host environment (through control points) to implement our WebAssembly-specific checkpoint-restore mechanism. For C-Cells that communicate via message passing or make use of shared memory (i.e. using some sort of multi- processing or multi-threading API), special care must be taken when checkpointing the state of the C-Cells, not to lose any in-flight messages or shared state. To this end, we only allow live migration to happen at barrier control points. These are control points that block all C-Cells of an applcation. C1 C2 C4 C6 Main VM Worker A Worker B C3 C5 C5 C6 C1 C2 C5 C3 C4 C6
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Live Migration of C-Cells. ‌ This experiment measures the benefit of migrating C-Cells at runtime (i.e., live migration). Note that native OpenMPI can not transparently migrate MPI processes while guaranteeing execution integrity (this is, that no messages are lost). As baselines, we run the same MPI application as before, LAMMPS, and a network-bound all-to-all kernel. The all-to-all kernel performs an accumulation and ������� ������� �������� �������� � ������� ��� ��� ��� ��� � � � � �� �� �� �� ������ �� ��� ��������� Figure 12: Speed-up executing an MPI application (LAMMPS) using C-Cells compared to native OpenMPI processes. Speed-up [No mig. / mig.]
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