Current caching methods for improving the performance of big-data jobs assume high (e.g., full bi-section) bandwidth; however many enterprise data centers and co-location facilities have large network imbalances due to over-subscription and incremental networking upgrades. We describe D3N, a multi-layer cooperative caching architecture that mitigates network imbalances by caching data on the access side of each layer of a hierarchical network topology, adaptively adjusting cache sizes of each layer based on observed workload patterns and network congestion. We have added (and submitted upstream) a 2-layer D3N cache to the Ceph RADOS Gateway; read bandwidth achieves the 5GB/s speed of our SSDs, and we show that it substantially improves big-data job performance while reducing network traffic.

The rising complexity of distributed server applications in Internet data centers has made the tasks of modeling and analyzing their behavior increasingly difficult. This article presents Modellus , a novel system for automated modeling of complex web-based data center applications using methods from queuing theory, data mining, and machine learning. Modellus uses queuing theory and statistical methods to automatically derive models to predict the resource usage of an application and the workload it triggers; these models can be composed to capture multiple dependencies between interacting applications. Model accuracy is maintained by fast, distributed testing, automated relearning of models when they change, and methods to bound prediction errors in composite models. We have implemented a prototype of Modellus, deployed it on a data center testbed, and evaluated its efficacy for modeling and analysis of several distributed multitier web applications. Our results show that this feature-based modeling technique is able to make predictions across several data center tiers, and maintain predictive accuracy (typically 95% or better) in the face of significant shifts in workload composition; we also demonstrate practical applications of the Modellus system to prediction and provisioning of real-world data center applications.