ZUNINO SUAREZ Alejandro Octavio
congresos y reuniones científicas
Task Scheduling for Processing Big Graphs in Heterogeneous Commodity Clusters
CORBELLINI, A.; GODOY, D.; MATEOS, C.; SCHIAFFINO, S.; ZUNINO, A.
Conferencia; The 4th the Latin American High Performance Computing Conference (CARLA 2017); 2017
Large-scale graph processing is a challenging problem since vertices can be arbitrarily connected, reducing locality and easily expanding the solution space. As a result, in recent years, a new breed of distributed frameworks that handle graphs efficiently has emerged. In large clusters with many resources (RAM, CPUs, network connectivity), these frameworks focus on exploiting the available resources as efficiently as possible. However, on situations where the cluster hardware is unbalanced or low in computing resources, the framework must correctly allocate tasks in order to complete execution. In this work, we compare three frameworks, the generic Fork-Join framework adapted to graph processing, and the Pregel and DPM frameworks that were originally designed for computing graphs. A link-prediction algorithm was used as case study to analyze several scheduling strategies that allocate tasks to servers in a cluster of heterogeneous characteristics. The dataset used for the experiments is a snapshot from the Twitter graph, and specifically, a subset of its users that pushed the memory requirements of the algorithm.