Load Balancing for Skewed Streams on Heterogeneous Cluster [article]

Muhammad Anis Uddin Nasir, Hiroshi Horii, Marco Serafini, Nicolas Kourtellis, Rudy Raymond, Sarunas Girdzijauskas, Takayuki Osogami
2017 arXiv   pre-print
Streaming applications frequently encounter skewed workloads and execute on heterogeneous clusters. Optimal resource utilization in such adverse conditions becomes a challenge, as it requires inferring the resource capacities and input distribution at run time. In this paper, we tackle the aforementioned challenges by modeling them as a load balancing problem. We propose a novel partitioning strategy called Consistent Grouping (CG), which enables each processing element instance (PEI) to
more » ... the workload according to its capacity. The main idea behind CG is the notion of small, equal-sized virtual workers at the sources, which are assigned to workers based on their capacities. We provide a theoretical analysis of the proposed algorithm and show via extensive empirical evaluation that our proposed scheme outperforms the state-of-the-art approaches, like key grouping. In particular, CG achieves 3.44x better performance in terms of latency compared to key grouping.
arXiv:1705.09073v2 fatcat:fl4aygsuibhg3hfhle2m3vuowa