Analysis and Modeling of Social Influence in High Performance Computing Workloads
Abstract
Social influence among users (e.g., collaboration on a project) creates bursty behavior in the underlying high performance computing (HPC) workloads. Using representative HPC and cluster workload logs, this paper identifies, analyzes, and quantifies the level of social influence across HPC users. We show the existence of a social graph that is characterized by a pattern of dominant users and followers. This pattern also follows a power-law distribution, which is consistent with those observed in mainstream social networks. Given its potential impact on HPC workloads prediction and scheduling, we propose a fast-converging, computationally-efficient online learning algorithm for identifying social groups. Extensive evaluation shows that our online algorithm can (1) quickly identify the social relationships by using a small portion of incoming jobs and (2) can efficiently track group evolution over time.
Cite
@article{arxiv.1610.04676,
title = {Analysis and Modeling of Social Influence in High Performance Computing Workloads},
author = {Shuai Zheng and Zon-Yin Shae and Xiangliang Zhang and Hani Jamjoom and Liana Fong},
journal= {arXiv preprint arXiv:1610.04676},
year = {2016}
}
Comments
The 17th International European Conference on Parallel and Distributed Computing, Euro-Par 2011