  Elasticity is one important feature in modern cloud computing systems and can
result in computation failure or significantly increase computing time. Such
elasticity means that virtual machines over the cloud can be preempted under a
short notice (e.g., hours or minutes) if a high-priority job appears; on the
other hand, new virtual machines may become available over time to compensate
the computing resources. Coded Storage Elastic Computing (CSEC) introduced by
Yang et al. in 2018 is an effective and efficient approach to overcome the
elasticity and it costs relatively less storage and computation load. However,
one of the limitations of the CSEC is that it may only be applied to certain
types of computations (e.g., linear) and may be challenging to be applied to
more involved computations because the coded data storage and approximation are
often needed. Hence, it may be preferred to use uncoded storage by directly
copying data into the virtual machines. In addition, based on our own
measurement, virtual machines on Amazon EC2 clusters often have heterogeneous
computation speed even if they have exactly the same configurations (e.g., CPU,
RAM, I/O cost). In this paper, we introduce a new optimization framework on
Uncoded Storage Elastic Computing (USEC) systems with heterogeneous computing
speed to minimize the overall computation time. Under this framework, we
propose optimal solutions of USEC systems with or without straggler tolerance
using different storage placements. Our proposed algorithms are evaluated using
power iteration applications on Amazon EC2.
