Yiwei Jiang

Online parallel machines scheduling with two hierarchies

This paper investigates an online hierarchical scheduling problem on m parallel identical machines. Each job, as well as each machine, has a hierarchy associated with it. A job can be scheduled on a machine only when its hierarchy is no higher than that of the machine. The objective is to minimize the makespan. In addition, we assume that there are only two hierarchies, and k machines have a higher hierarchy which can schedule all jobs. We present an online algorithm with a competitive ratio of 1+m^2-mm^2-km+k^2<73 for any k and m. The performance for some pairs of k and m is further improved by another algorithm. Lower bounds for various pairs of k and m are also presented.

Minimizing makespan and total completion time in MapReduce-like systems

Effectiveness of MapReduce as a big data processing framework depends on efficiencies of scale for both map and reduce phases. While most map tasks are preemptive and parallelizable, the reduce tasks typically are not easily decomposed and often become a bottleneck due to constraints of data locality and task complexity. By assuming that reduce tasks are non-parallelizable, we study offline scheduling of minimizing makespan and minimizing total completion time, respectively. Both preemptive and non-preemptive reduce tasks are considered. On makespan minimization, for preemptive version we design an algorithm and prove its optimality, for non-preemptive version we design an approximation algorithm with the worst ratio of 3/2-1/2h where h is the number of machines. On total complete time minimization, for non-preemptive version we devise an approximation algorithm with worst case ratio of 2-1/h, and for preemptive version we devise a heuristic. We confirm that our algorithms outperform state-of-art schedulers through experiments.

Better approximation algorithms for influence maximization in online social networks

Influence maximization is a classic and hot topic in social networks. In this paper, firstly we argue that in online social networks, due to the time sensitivity of popular topics, the assumption in IC or LT model that the influence propagates endlessly in the network, is not applicable. Based on this we consider influence transitivity and limited propagation distance in our new model. Secondly, under our model we propose Semidefinite based algorithms. While most existing algorithms rely on monotony and submodularity to obtain approximation ratio of

Single-server parallel-machine scheduling with loading and unloading times

1-1/e

Total completion time minimization in online hierarchical scheduling of unit-size jobs

1−1/e, when no size limitation exists on the number of seeds, our algorithm achieves approximation ratio with

An approximation algorithm for proportionate scheduling in the two-stage hybrid flow shop

0.857