Difference between revisions of "News/events"

From CRIU
Jump to navigation Jump to search
 
(85 intermediate revisions by 6 users not shown)
Line 15: Line 15:
  
 
<startFeed/></noinclude>
 
<startFeed/></noinclude>
== Linux Plumbers Conference 2018 ==
 
[[Image:gsoc.png|left|100px|link=]]
 
  
'''Mar-Sep 2019'''
+
== DFRWS USA 2026 ==
 +
[[Image:Dfrws.png|left|120px|link=]]
  
[https://summerofcode.withgoogle.com/organizations/6333591376625664/ Google Summer of Code]
+
'''27-30 July, 2026, Arlington, Virginia, USA'''
 +
<div style="clear: both;"></div>
 +
[https://dfrws.org/conferences/dfrws-usa-2026/ Forensic Analysis of Container Snapshot Chains for Post-Event Reconstruction]
  
--[[User:Avagin]] 21:32, 26 Feb 2019 (PST)
+
== EuroMLSys 2026 ==
 +
[[Image:Euromlsys.png|left|120px|link=]]
  
<br clear="both">
+
'''27 April, 2026, Edinburgh, United Kingdom'''
 +
 
 +
[https://euromlsys.eu/ Towards On-the-Fly Snapshot Memory Compression for Low-Latency Elastic Inference Serving Systems]
 +
<div style="clear: both;"></div>
 +
== KubeCon EU 2026 ==
 +
[[Image:Kubecon.png|left|140px|link=]]
 +
 
 +
'''24-26 March, 2026, Amsterdam, Netherlands'''
 +
<div style="clear: both;"></div>
 +
[https://sched.co/2CW6P Ctrl-X, Ctrl-V Your Pods: WG Checkpoint Restore in Kubernetes]
 +
 
 +
[https://sched.co/2CW7Z Optimizing Error Recovery for Cost-Efficient Distributed AI Model Training with Kubernetes]
  
 
<noinclude><endFeed/>
 
<noinclude><endFeed/>

Latest revision as of 12:57, 5 April 2026


This page collects into about events criu takes part in.

<startFeed/>

DFRWS USA 2026[edit]

Dfrws.png

27-30 July, 2026, Arlington, Virginia, USA

Forensic Analysis of Container Snapshot Chains for Post-Event Reconstruction

EuroMLSys 2026[edit]

Euromlsys.png

27 April, 2026, Edinburgh, United Kingdom

Towards On-the-Fly Snapshot Memory Compression for Low-Latency Elastic Inference Serving Systems

KubeCon EU 2026[edit]

Kubecon.png

24-26 March, 2026, Amsterdam, Netherlands

Ctrl-X, Ctrl-V Your Pods: WG Checkpoint Restore in Kubernetes

Optimizing Error Recovery for Cost-Efficient Distributed AI Model Training with Kubernetes

<endFeed/>

See also[edit]