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.
Cake: Enabling High-level SLOs on Shared Storage Systems
ACM Symposium on Cloud Computing (SOCC'12), October 2012.
Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Randy Katz, Ion Stoica
University of California, Berkeley
Cake is a coordinated, multi-resource scheduler for shared distributed storage environments with the goal of achieving both high throughput and bounded latency. Cake uses a two-level scheduling scheme to enforce high-level service-level objectives (SLOs). Firstlevel schedulers control consumption of resources such as disk and CPU. These schedulers (1) provide mechanisms for differentiated scheduling, (2) split large requests into smaller chunks, and (3) limit the number of outstanding device requests, which together allow for effective control over multi-resource consumption within the storage system. Cake's second-level scheduler coordinates the first-level schedulers to map high-level SLO requirements into actual scheduling parameters. These parameters are dynamically adjusted over time to enforce high-level performance specifications for changing workloads. We evaluate Cake using multiple workloads derived from real-world traces. Our results show that Cake allows application programmers to explore the latency vs. throughput trade-off by setting different high-level performance requirements on their workloads. Furthermore, we show that using Cake has concrete economic and business advantages, reducing provisioning costs by up to 50% for a consolidated workload and reducing the completion time of an analytics cycle by up to 40%.
Consolidation, Storage Systems, Service-Level Objectives, Twolevel
FULL PAPER: pdf