Sublinear Approximation Schemes for Scheduling Precedence Graphs of Bounded Depth

We study the classical scheduling problem on parallel machines %with precedence constraints where the precedence graph has the bounded depth $h$. Our goal is to minimize the maximum completion time. We focus on developing approximation algorithms that use only sublinear space or sublinear time. We develop the first one-pass streaming approximation schemes using sublinear space when all jobs' processing times differ no more than a constant factor $c$ and the number of machines $m$ is at most $\tfrac {2n \epsilon}{3 h c }$. This is so far the best approximation we can have in terms of $m$, since no polynomial time approximation better than $\tfrac{4}{3}$ exists when $m = \tfrac{n}{3}$ unless P=NP. %the problem cannot be approximated within a factor of $\tfrac{4}{3}$ when $m = \tfrac{n}{3}$ even if all jobs have equal processing time. The algorithms are then extended to the more general problem where the largest $\alpha n$ jobs have no more than $c$ factor difference. % for some constant $0 < \alpha \le 1$. We also develop the first sublinear time algorithms for both problems. For the more general problem, when $m \le \tfrac { \alpha n \epsilon}{20 c^2 \cdot h }$, our algorithm is a randomized $(1+\epsilon)$-approximation scheme that runs in sublinear time. This work not only provides an algorithmic solution to the studied problem under big data % and cloud computing environment, but also gives a methodological framework for designing sublinear approximation algorithms for other scheduling problems.