From the webpage:
What is Mizan?
Mizan is an advanced clone to Google’s graph processing system Pregel that utilizes online graph vertex migrations to dynamically optimizes the execution of graph algorithms. You can use our Mizan system to develop any vertex centric graph algorithm and run in parallel over a local cluster or over cloud infrastructure. Mizan is compatible with Pregel’s API, written in C++ and uses MPICH2 for communication. You can download a copy of Mizan and start using it today on your local machine or try Mizan on EC2. We also welcome programers who are interested to go deeper into our Mizan code to optimize or tweak.
Mizan is published in EuroSys 13 as “Mizan: A System for Dynamic Load Balancing in Large-scale Graph Processing“. We have an earlier work of Mizan as “Mizan: Optimizing Graph Mining in Large Parallel Systems“, which we recently changed it to Libra to avoid confusions. We show below the abstract for Mizan’s EuroSys 13 paper. We also include Mizan’s general architecture and its API available for users.
Pregel was recently introduced as a scalable graph mining system that can provide significant performance improvements over traditional MapReduce implementations. Existing implementations focus primarily on graph partitioning as a preprocessing step to balance computation across compute nodes. In this paper, we examine the runtime characteristics of a Pregel system. We show that graph partitioning alone is insufficient for minimizing end-to-end computation. Especially where data is very large or the runtime behavior of the algorithm is unknown, an adaptive approach is needed. To this end, we introduce Mizan, a Pregel system that achieves efficient load balancing to better adapt to changes in computing needs. Unlike known implementations of Pregel, Mizan does not assume any a priori knowledge of the structure of the graph or behavior of the algorithm. Instead, it monitors the runtime characteristics of the system. Mizan then performs efficient fine-grained vertex migration to balance computation and communication. We have fully implemented Mizan; using extensive evaluation we show that—especially for highly-dynamic workloads— Mizan provides up to 84% improvement over techniques leveraging static graph pre-partitioning.
Post like this one make me want to build a local cluster at home. 😉