Phoenix: A Substrate for Resilient Distributed Graph Analytics

(Download publication here) (Download slides here) (Watch lightning talk here)

Abstract

This paper presents Phoenix, a communication and synchronization substrate that implements a novel protocol for recovering from fail-stop faults when executing graph analytics applications on distributed-memory machines. The standard recovery technique in this space is checkpointing, which rolls back the state of the entire computation to a state that existed before the fault occurred. The insight behind Phoenix is that this is not necessary since it is sufficient to continue the computation from a state that will ultimately produce the correct result. We show that for graph analytics applications, the necessary state adjustment can be specified easily by the programmer using a thin API supported by Phoenix.

Phoenix has no observable overhead during fault-free execution, and it is resilient to any number of faults while guaranteeing that the correct answer will be produced at the end of the computation. This is in contrast to other systems in this space which may either have overheads even during fault-free execution or produce only approximate answers when faults occur during execution.

We incorporated Phoenix into D-Galois, the state-of-the-art distributed graph analytics system, and evaluated it on two production clusters. Our evaluation shows that in the absence of faults, Phoenix is ~24x faster than GraphX, which provides fault tolerance using the Spark system. Phoenix also outperforms the traditional checkpoint-restart technique implemented in D-Galois: in fault-free execution, Phoenix has no observable overhead, while the checkpointing technique has 31\% overhead. Furthermore, Phoenix mostly outperforms checkpointing when faults occur, particularly in the common case when only a small number of hosts fail simultaneously.