Detecting Last-Level Cache Contention in Workload Colocation with Meta Learning

While workload colocation improves cluster utilization in cloud environments, it introduces performance-impacting contentions on unmanaged resources. We address the problem of detecting the contentions on last-level cache with low level platform counters, but without application performance data. The detection is performed in a noisy environment with a mix of contention cases and non-contention cases, but without the ground truth. We propose a meta-learning approach to discriminate the increase of cache miss metrics taking the cache occupancy data as the precondition. We assume that given a certain workload intensity, when the cache occupancy of the workload drops below its hot data size, increasing cache misses will be observed. Leveraging the assumption, the threshold of cache miss metrics to detect cache interference under the workload intensity is found by inducing the most discriminating rule from the noisy history. Similarly, we determine whether the cache interference impacts performance by discriminating the increase of cycles per instruction metrics with the interference signal. Experimental results indicate that the new approach achieves a decent performance in identifying cache contentions with performance impact in noisy environments.