Decentralized Uncoded Storage Elastic Computing with Heterogeneous Computation Speeds

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.