  We propose a serverless computing mechanism for distributed computation based
on polar codes. Serverless computing is an emerging cloud based computation
model that lets users run their functions on the cloud without provisioning or
managing servers. Our proposed approach is a hybrid computing framework that
carries out computationally expensive tasks such as linear algebraic operations
involving large-scale data using serverless computing and does the rest of the
processing locally. We address the limitations and reliability issues of
serverless platforms such as straggling workers using coding theory, drawing
ideas from recent literature on coded computation. The proposed mechanism uses
polar codes to ensure straggler-resilience in a computationally effective
manner. We provide extensive evidence showing polar codes outperform other
coding methods. We have designed a sequential decoder specifically for polar
codes in erasure channels with full-precision input and outputs. In addition,
we have extended the proposed method to the matrix multiplication case where
both matrices being multiplied are coded. The proposed coded computation scheme
is implemented for AWS Lambda. Experiment results are presented where the
performance of the proposed coded computation technique is tested in
optimization via gradient descent. Finally, we introduce the idea of partial
polarization which reduces the computational burden of encoding and decoding at
the expense of straggler-resilience.
