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Towards Truly Elastic Distributed Graph Computing in the Cloud

  • Lu Lu
  • Xuanhua Shi
  • Hai JinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9464)

Abstract

Elasticity is very important to the scale-out distributed systems running on today’s large-scale multi-tenant clouds, regardless public or private. An elastic distributed data processing system must have the capability of: (1) dynamically balancing the computing load among workers due to their performance heterogeneity and dynamicity; (2) fast recovering the lost memory state of failure workers with acceptable overheads during the regular execution.

Unfortunately, we found that the design of the state-of-the-art distributed graph computing system only works well in small sized dedicated clusters. We implement a distributed graph computing prototype, X-Graph, and demonstrate the capabilities of being elastic in three ways. First, we present menger, a novel two-level graph partition framework, which further splits one worker-level partition into several sub-partitions as the basic migration units, and each has the “migration affinity” with one of the other workers. Second, we implement a dynamical load balancer based on menger, which prefers the worker that has the affinity of the sub-partition to be migrated as the destination, and completely avoids the costly sophistical graph re-partitioning algorithms. Third, we implement a differentiated replication frame-work, which supports parallel recovery for lost partitions just like general-purpose dataflow systems.

Keywords

Fault Tolerance Input Graph Execution Engine Distribute File System Dynamic Load Balance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This paper is partly supported by the NSFC under grant No. 61433019 and No. 61370104, International Science and Technology Cooperation Program of China under grant No. 2015DFE12860, MOE- Intel Special Research Fund of Information Technology under grant MOE-INTEL-2012-01, and Chinese Universities Scientific Fund under grant No. 2014TS008.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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