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.
FastLane: Making short flows shorter with agile drop notification
SoCC 2015: 84-96 Symposium on Cloud Computing, Aug 27, 2015 - Aug 29, 2015, Big Island, Hawaii.
David Zats*†, Anand Padmanabha Iyer*, Ganesh Ananthanarayanan§,
Rachit Agarwal*, Randy H. Katz*, Ion Stoica*, Amin Vahdat†
* University of California, Berkeley
§ Microsoft Research
The drive towards richer and more interactive web content places increasingly stringent requirements on datacenter network performance. Applications running atop these networks typically partition an incoming query into multiple subqueries, and generate the final result by aggregating the responses for these subqueries. As a result, a large fraction — as high as 80% — of the network flows in such workloads are short and latency-sensitive. The speed with which existing networks respond to packet drops limits their ability to meet high-percentile flow completion time SLOs. Indirect notifications indicating packet drops (e.g., duplicates in an end-to-end acknowledgement sequence) are an important limitation to the agility of response to packet drops.
This paper proposes FastLane, an in-network drop notification mechanism. FastLane enhances switches to send high-priority drop notifications to sources, thus informing sources as quickly as possible. Consequently, sources can retransmit packets sooner and throttle transmission rates earlier, thus reducing high-percentile flow completion times. We demonstrate, through simulation and implementation, that FastLane reduces 99.9th percentile completion times of short flows by up to 81%. These benefits come at minimal cost — safeguards ensure that FastLane consume no more than 1% of bandwidth and 2.5% of buffers.
FULL PAPER: pdf