Search for: All records

Understanding the different workloaddependent factors that impact the latency or reliability of a storage system is essential for SLA satisfaction and fair resource provisioning. However, due to the volatility of system behavior under multiple workloads, determining even the number of concurrent types of workload functions, a necessary precursor to workload separation, is an unsolved problem in the general case. We introduce CENSUS, a novel classification framework that combines time-series analysis with gradient boosting to identify the number of functional workloads in a shared storage system by projecting workload traces into a high-dimensional feature representation space. We show that CENSUS can distinguish the number of interleaved workloads in a real-world trace segment with up to 95% accuracy, leading to a decrement of the mean square error to as little as 5% compared to the 

Modern cache hierarchies are tangled webs of complexity. Multiple tiers of heterogeneous physical and virtual devices, with many configurable parameters, all contend to optimally serve swarms of requests between local and remote applications. The challenge of effectively designing these systems is exacerbated by continuous advances in hardware, firmware, innovation in cache eviction algorithms, and evolving workloads and access patterns. This rapidly expanding configuration space has made it costly and time-consuming to physically experiment with numerous cache configurations for even a single stable workload. Current cache evaluation techniques (e.g., Miss Ratio Curves) are short-sighted: they analyze only a single tier of cache, focus primarily on performance, and fail to examine the critical relationships between metrics like throughput and monetary cost. Publicly available I/O cache simulators are also lacking: they can only simulate a fixed or limited number of cache tiers, are missing key features, or offer limited analyses. It is our position that best practices in cache analysis should include the evaluation of multi-tier configurations, coupled with more comprehensive metrics that reveal critical design trade-offs, especially monetary costs. We are developing an n-level I/O cache simulator that is general enough to model any cache hierarchy, captures many metrics, provides a robust set of analysis features, and is easily extendable to facilitate experimental research or production level provisioning. To demonstrate the value of our proposed metrics and simulator, we extended an existing cache simulator (PyMimircache). We present several interesting and counter-intuitive results in this paper. 

Identifying the characteristics of a storage workload is critical for resource provisioning for metrics including performance, reliability, and utilization. Although multi-tenant systems are increasingly commonplace, characterization of multiple workloads within a single system trace is difficult because workloads are highly dynamic and typically not labeled. We show that, by converting a block I/O workload to a signal and applying blind source separation, we are able to successfully separate many application workloads. 

Identifying the characteristics of a storage workload is critical for resource provisioning for metrics including performance, reliability, and utilization. Although multi-tenant systems are increasingly commonplace, characterization of multiple workloads within a single system trace is difficult because workloads are highly dynamic and typically not labeled. We show that, by converting a block I/O workload to a signal and applying blind source separation, we are able to successfully separate many application workloads. 

Identifying the characteristics of a storage workload is critical for resource provisioning for metrics including performance, reliability, and utilization. Although multi-tenant systems are increasingly commonplace, characterization of multiple workloads within a single system trace is difficult because workloads are highly dynamic and typically not labeled. We show that, by converting a block I/O workload to a signal and applying blind source separation, we are able to successfully separate many application workloads.