2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) 2014
DOI: 10.1109/ispass.2014.6844481
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Abstract: Abstract-In order to increase utilization, multicore processors share memory resources among an increasing number of cores. This sharing leads to memory interference, which in turn leads to a non-uniform degradation in the execution of concurrent applications, even in the presence of fairness mechanisms. Many utilities rely on application CPU Time both for measuring resource usage and inferring application progress. These utilities are therefore directly affected by the distorting effects of multicore interfer… Show more

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Cited by 4 publications
(4 citation statements)
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“…IPC can be measured easily and cheaply on commodity processors. A wide body of prior interference estimation techniques utilizes IPC as their primary metric to quantify performance [16,25,58]. For even latency-sensitive applications, a prior study from Google leveraged the CPI (Cycles Per Instructions) metric as a performance indicator [69].…”
Section: Goalmentioning
confidence: 99%
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“…IPC can be measured easily and cheaply on commodity processors. A wide body of prior interference estimation techniques utilizes IPC as their primary metric to quantify performance [16,25,58]. For even latency-sensitive applications, a prior study from Google leveraged the CPI (Cycles Per Instructions) metric as a performance indicator [69].…”
Section: Goalmentioning
confidence: 99%
“…There have been many efforts that try to estimate slowdown of applications at runtime [16,17,23,25,40,42,49,58,61,68]. Prior software approaches [16,25,61,68] utilize an online runtime system that periodically pauses all the applications except one for a short time, thus allowing the running application to monopolize the computing resources on the system during those pause periods. The performance of the running application during such pause periods is used to determine slowdown.…”
Section: Introductionmentioning
confidence: 99%
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“…However, these services only monitor CPU usage, network traffic and disk bandwidth, but not low-level memory usage. To measure a VM's performance without contention for reference sampling, past work offer three ways: (1) collecting the VM's performance characteristics before it is deployed in the cloud [19,53]; (2) measuring the performance of other VMs which run similar tasks [34,50]; (3) measuring the Protected VM while pausing all other co-located VMs [22,47]. The drawback of (1) and ( 2) is that it only works for programs with predictable and stable performance characteristics, and does not support arbitrary programs running in the Protected VM.…”
Section: Eliminating Resource Contentionmentioning
confidence: 99%