Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
Volley: Violation Likelihood Based State Monitoring for Datacenters
33rd IEEE Int. Conf. on Distributed Computing Systems (ICDCS’13), July 2013.
Shicong Meng, Arun Iyengar, Isabelle Rouvellou, Ling Liu*
IBM T.J. Watson Research Center
*Georgia Institute of Technology
Distributed state monitoring plays a critical role in Cloud datacenter management. One fundamental problem in distributed state monitoring is to minimize the monitoring cost while maximizing the monitoring accuracy at the same time. In this paper, we present Volley, a violation likelihood based approach for efficient distributed state monitoring in Cloud datacenters. Volley achieves both efficiency and accuracy with a flexible monitoring framework which uses dynamic monitoring intervals determined by the likelihood of detecting state violations. Volley consists of three unique techniques. It utilizes efficient node-level adaptation algorithms that minimize monitoring cost with controlled accuracy. Volley also employs a distributed scheme that coordinates the adaptation on multiple monitor nodes of the same task for optimal tasklevel efficiency. Furthermore, it enables multi-task level cost reduction by exploring state correlation among monitoring tasks. We perform extensive experiments to evaluate Volley with system, network and application monitoring tasks in a virtualized datacenter environment. Our results show that Volley can reduce considerable monitoring cost and still deliver user specified monitoring accuracy under various scenarios.
FULL PAPER: pdf