Towards Scale-out Capability on Social Graphs

The development of cloud storage and computing has facilitated the rise of various big data applications. As a representative high performance computing (HPC) workload, graph processing is becoming a part of cloud computing. However, scalable computing on large graphs is still dominated by HPC solutions, which require high performance all-to-all collective operations over torus (or mesh) networking. Implementing those torus-based algorithms on commodity clusters, e.g., cloud computing infrastructures, can result in great latency due to inefficient communication. Moreover, designing a highly scalable system for large social graphs, is far from being trivial, as intrinsic features of social graphs, e.g., degree skewness and lacking of locality, often profoundly limit the extent of parallelism. To resolve the challenges, we explore the iceberg of developing a scalable system for processing large social graphs on commodity clusters. In particular, we focus on the scale-out capability of the system. We propose a novel separator-combiner based query processing engine which provides native load-balancing and very low communication overhead, such that increasinglylarger graphs can be simply addressed by adding more computing nodes to the cluster.The proposed system achieves remarkable scale-out capability in processing large social graphs with skew degree distributions, while providing many critical features for big data analytics, such as easy-to-use API, fault-tolerance and recovery. We implement the system as a portable and easily configurable library, and conduct comprehensive experimental studies to demonstrate its effectiveness and efficiency.