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FACT: A Framework for Adaptive Contention-Aware Thread Migrations (March 2011)
Thread scheduling in multi-core systems is a challenging problem because cores on a single chip usually share parts of the memory hierarchy, such as last-level caches, prefetchers and memory controllers, making threads running on different cores interfere with each other while competing for these resources. Data center service providers are interested in compressing the workload onto as few computing units as possible so as to utilize its resources most efficiently and conserve power. However, because memory hierarchy interference between threads is not managed by commercial operating systems, the data center operators still prefer running threads on different chips so as to avoid possible performance degradation due to interference. In this work, we improved the system’s throughput by minimizing inter-workload contention for memory hierarchy resources. We achieved this by implementing FACT, a Framework for Adaptive Contention-aware Thread migrations, which measures the relevant performance monitoring events online, learns to predict the effects of interference on performance of workloads, and then makes optimal thread scheduling decisions. We found that when instantiated with a fuzzy rule-based (FRB) predictive model, FACT achieves on average a 74% prediction accuracy on the new data. In experiments conducted on a quad-core machine running OpenSolaris(TM), SPECcpu2006 workloads under FACT-FRB ran up to 11.6% faster than under the default OpenSolaris scheduler. FACT-FRB was also able to find the best combination of workloads more consistently than the state-of-the-art algorithms that aim to minimize contention for memory resources on each chip. Unlike these algorithms that based on fixed heuristics, FACT can be easily adapted to consider other performance factors so as to accommodate changes in architectural features and performance bottlenecks in future systems.
Kishore Kumar Pusukuri, David Vengerov, Alexandra Fedorova, Vana Kalogeraki
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