  The problem of MPI programs execution time prediction on a certain set of
computer installations is considered. This problem emerges with orchestration
and provisioning a virtual infrastructure in a cloud computing environment over
a heterogeneous network of computer installations: supercomputers or clusters
of servers (e.g. mini data centers). One of the key criteria for the
effectiveness of the cloud computing environment is the time staying by the
program inside the environment. This time consists of the waiting time in the
queue and the execution time on the selected physical computer installation, to
which the computational resource of the virtual infrastructure is dynamically
mapped. One of the components of this problem is the estimation of the MPI
programs execution time on a certain set of computer installations. This is
necessary to determine a proper choice of order and place for program
execution. The article proposes two new approaches to the program execution
time prediction problem. The first one is based on computer installations
grouping based on the Pearson correlation coefficient. The second one is based
on vector representations of computer installations and MPI programs, so-called
embeddings. The embedding technique is actively used in recommendation systems,
such as for goods (Amazon), for articles (Arxiv.org), for videos (YouTube,
Netflix). The article shows how the embeddings technique helps to predict the
execution time of a MPI program on a certain set of computer installations.
