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A Workload-Adaptive Streaming Partitioner for Distributed Graph Stores
2021
Data Science and Engineering
AbstractStreaming graph partitioning methods have recently gained attention due to their ability to scale to very large graphs with limited resources. However, many such methods do not consider workload and graph characteristics. This may degrade the performance of queries by increasing inter-node communication and computational load imbalance. Moreover, existing workload-aware methods cannot consistently provide good performance as they do not consider dynamic workloads that keep emerging in
doi:10.1007/s41019-021-00156-2
fatcat:6eajdxuz3ra7lo3rvqhbse5eyy