2015/10/19 by Muhammad Anis Uddin Nasir, Gianmarco De Francisci Morales, Nasir, Muhammad Anis Uddin +5 · 1 citation
Computer Science · #Cloud Computing and Resource Management #Data Stream Mining Techniques #Distributed #FOS: Computer and information sciences #Parallel #Software System Performance and Reliability #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.1510.05714
12 pages, 14 Figures, this paper is accepted and will be published at ICDE 2016
openalex publication_date 2015/10/19 · arxiv created 2016/01/27 · arxiv updated 2016/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world workloads are skewed: some tuples in the stream are associated to keys which are significantly more frequent than others. Skew is remarkably more problematic in large deployments: more workers implies fewer keys per worker, so it becomes harder to "average out" the cost of hot keys with cold keys. We propose a novel load balancing technique that uses a heaving hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to d ≥ 2 choices to ensure a balanced load, where d is tuned automatically to minimize the memory and computation cost of operator replication. The technique works online and does not require the use of routing tables. Our extensive evaluation shows that our technique can balance real-world workloads on large deployments, and improve throughput and latency by 150% and 60% respectively over the previous state-of-the-art when deployed on Apache Storm.