The EuroSys conference report blog

520+ attendees

Best Demo: OpenFlow

Best Paper: OpenRoad

Sigcomm 2011 in north america, still waiting for proposals

Pcitures on Flickr

All papers on CCR-online

Session 9: Performance Optimization (Chair: Ratul Mahajan, Microsoft Research)
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Safe and Effective Fine-grained TCP Retransmissions for Datacenter Communication
Vijay Vasudevan (Carnegie Mellon University), Amar Phanishayee (Carnegie Mellon University), Hiral Shah (Carnegie Mellon University), Elie Krevat (Carnegie Mellon University), David Andersen (Carnegie Mellon University), Greg Ganger (Carnegie Mellon University), Garth Gibson (Carnegie Mellon University and Panasas, Inc), Brian Mueller (Panasas, Inc.)
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TCP has a problem in data centers: the dropped packet takes 200ms to be retransmitted

There are some apps that can not tolerate that

solution: enable ms retransmission
improve throughout/latency in datacenter
safe for wide-area

10-100 microsecond, 1-10Gbps
under heavy load, pkt loss is common

1 TCP timout is 1000s times more than RTT

The scenario involves the client sending a single request packet once in a while. This is in contrary of TCP design principles: full window of packets. Hence, the fast-retransmission does not get triggered in case of pkt loss

Solution:
1) eliminate long 200ms timeout
2) TCP must track RTT in microseconds

Interaction with delayed ACK
- The reduction is not so much
Stability? Causing congestion collapse?
- Today’s TCP has mechanisms to cope with that

Q: problem for congestion control?
A: exponential backup takes care of that

14:00-15:30     Session 8: Network Measurement (Chair: Gianluca Iannaccone, Intel Labs Berkeley)
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Spatio-Temporal Compressive Sensing and Internet Traffic Matrices
Yin Zhang (University of Texas at Austin), Matthew Roughan (University of Adelaide), Walter Willinger (AT&T Labs — Research), Lili Qiu (University of Texas at Austin)
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How to fill the missing values in matrix?

The need for missing value interpolation

Traffic volume: a matrix where the rows represent snapshots taken in different times.
There are some missing values, how to interpolate them?

Problem: A(x)=B
Challenge: massively under-constrained

Ideas:
- TMs are low-rank
- exploit spatio-temporal properties
- exploit local structures in TMs

Passive Aggressive Measurement with MGRP
Pavlos Papageorgiou (University of Maryland, College Park), Justin McCann (University of Maryland, College Park), Michael Hicks (University of Maryland, College Park)
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video conference:
- monitoring the quality
-  active probing is expensive
-  we can shape app data for measurement

MGRP: piggyback app data inside active probes

Sender side: after TCP layer, fragmentation & reassembly

For evaluation: PathLoad for measuring the available BW

Q: why not piggyback the probe traffic over app traffic?
A: interesting idea

Q; Is it applicable for data centers?
A: no, not to 1Gbps

Q: MTU discovery?
A: We did not do that.

Q: The final header is UDP or TCP?
A: UDP

Towards Automated Performance Diagnosis in a Large IPTV Network
Ajay Mahimkar (The University of Texas at Austin), Zihui Ge (AT&T Labs - Research), Aman Shaikh (AT&T Labs - Research), Jia Wang (AT&T Labs - Research), Jennifer Yates (AT&T Labs - Research), Yin Zhang (The University of Texas at Austin), Qi Zhao (AT&T Labs - Research)
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IPTV: TV delivered through IP network
characteristics:
- stringent constraints on reliability and performance
- scale
- complexity

Problem statements: characterize faults and performance in IPTV networks
detect and troubleshoot recurring conditions: temporal and spatial

Data analyzed over 3 months:
-> decreasing frequency: Ticket, Live TV video, requested info, …

Daily pattern of events:
- lots of activity between eventing prime time and day time

We can predict the next occurrence of the events and be prepared

Mining challenges:
- massive scale of event-series
- skewed event distribution
- Imperfect timing information due to propagation delay and distributed events

1) classify the reported events
2) make causal graph

16:15-17:45     Session 5: Wireless Networking 2 (Chair: Suman Banerjee, University of Wisconsin at Madison)
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In Defense of Wireless Carrier Sense
Micah Z. Brodsky (MIT), Robert T. Morris (MIT)
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Wireless medium us semi-shared: sometimes the interference happens
when to go for which option?

Solution: carrier-sense
problems: hidden-terminal

DIRC: Increasing Indoor Wireless Capacity Using Directional Antennas
Xi Liu (Carnegie Mellon University), Anmol Sheth (Intel Research Seattle), Michael Kaminsky (Intel Research Pittsburgh), Konstantina Papagiannaki (Intel Research Pittsburgh), Srinivasan Seshan (Carnegie Mellon University), Peter Steenkiste (Carnegie Mellon University)
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wireless capacity demand

Having the barriers, there are multiple paths using directional antenna, which is the optimal in reducing the interference?

Assume, only the AP has directional antenna

Is it worth it? Upper-bound is 70-80% improvement over omni-directional antenna

First each AP seperately send signal to all directions and each reciever reports the strenght of signal: 240ms
Then, they find an optimal scheduling based on SNR
Third, …

Node movement: monitor the changes in throughput

Q: Fairness?
A: no worse than omni-directional, basically everyone gets better