BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T203000Z
DTEND:20121114T210000Z
LOCATION:255-BC
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Application interference is prevalent in datacenters due to contention over shared hardware resources. Unfortunately, understanding interference in live datacenters is more difficult than in controlled environments or on simpler architectures. Most approaches to mitigating interference rely on data that cannot be collected efficiently in a production environment. This work exposes eight specific complexities of live datacenters that constrain measurement of interference. It then introduces new, generic measurement techniques for analyzing interference in the face of these challenges and restrictions. We use the measurement techniques to conduct the first large-scale study of application=0Ainterference in live production datacenter workloads. Data is measured across 1000 12-core Google servers observed to be running 1102 unique applications. Finally, our work identifies several opportunities to improve performance that use only the available data; these opportunities are applicable to any datacenter.
SUMMARY:Measuring Interference Between Live Datacenter Applications
PRIORITY:3
END:VEVENT
END:VCALENDAR
BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T203000Z
DTEND:20121114T210000Z
LOCATION:255-BC
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Application interference is prevalent in datacenters due to contention over shared hardware resources. Unfortunately, understanding interference in live datacenters is more difficult than in controlled environments or on simpler architectures. Most approaches to mitigating interference rely on data that cannot be collected efficiently in a production environment. This work exposes eight specific complexities of live datacenters that constrain measurement of interference. It then introduces new, generic measurement techniques for analyzing interference in the face of these challenges and restrictions. We use the measurement techniques to conduct the first large-scale study of application=0Ainterference in live production datacenter workloads. Data is measured across 1000 12-core Google servers observed to be running 1102 unique applications. Finally, our work identifies several opportunities to improve performance that use only the available data; these opportunities are applicable to any datacenter.
SUMMARY:Measuring Interference Between Live Datacenter Applications
PRIORITY:3
END:VEVENT
END:VCALENDAR