  Elasticity plays an important role in modern cloud computing systems. Elastic
computing allows virtual machines (i.e., computing nodes) to be preempted when
high-priority jobs arise, and also allows new virtual machines to participate
in the computation. In 2018, Yang et al. introduced Coded Storage Elastic
Computing (CSEC) to address the elasticity using coding technology, with lower
storage and computation load requirements. However, CSEC is limited to certain
types of computations (e.g., linear) due to the coded data storage based on
linear coding. Then Centralized Uncoded Storage Elastic Computing (CUSEC) with
heterogeneous computation speeds was proposed, which directly copies parts of
data into the virtual machines. In all existing works in elastic computing, the
storage assignment is centralized, meaning that the number and identity of all
virtual machines possible used in the whole computation process are known
during the storage assignment. In this paper, we consider Decentralized Uncoded
Storage Elastic Computing (DUSEC) with heterogeneous computation speeds, where
any available virtual machine can join the computation which is not predicted
and thus coordination among different virtual machines' storage assignments is
not allowed. Under a decentralized storage assignment originally proposed in
coded caching by Maddah-Ali and Niesen, we propose a computing scheme with
closed-form optimal computation time. We also run experiments over MNIST
dataset with Softmax regression model through the Tencent cloud platform, and
the experiment results demonstrate that the proposed DUSEC system approaches
the state-of-art best storage assignment in the CUSEC system in computation
time.
