Social influence between users (e.g., collaborating on a project) creates bursty behavior in the underlying high performance computing (HPC) workloads. Using representative HPC and cluster workloads, 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 workload prediction and scheduling, we propose a fast-converging, computationally-efficient online learning algorithm for identifying social groups. Extensive evaluation show that our online algorithm can identify stable social groups after observing only 1% of workload arrivals.
By: Shuai Zheng; Zon-Yin Shae; Xiangliang Zhang; Hani Jamjoom; Liana Fong
Published in: RC25164 in 2011
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