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1. Chasing the Signal: Statistically Separating Multi-Tenant I/O Workloads

   Chen, Si; Wildani, Avani (December 2018, Machine Learning for Systems)

   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. 

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2. Chasing the Signal: Statistically Separating Multi-Tenant I/O Workloads

   Chen, Si; Wildani, Avani (December 2018, Machine Learning for Systems)

   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. 

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3. Datamime: Generating Representative Benchmarks by Automatically Synthesizing Datasets

   https://doi.org/10.1109/MICRO56248.2022.00082  

   Lee, Hyun Ryong; Sanchez, Daniel (October 2022, 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO))

   Benchmarks that closely match the behavior of production workloads are crucial to design and provision computer systems. However, current approaches fall short: First, open-source benchmarks use public datasets that cause different behavior from production workloads. Second, blackbox workload cloning techniques generate synthetic code that imitates the target workload, but the resulting program fails to capture most workload characteristics, such as microarchitectural bottlenecks or time-varying behavior. Generating code that mimics a complex application is an extremely hard problem. Instead, we propose a different and easier approach to benchmark synthesis. Our key insight is that, for many production workloads, the program is publicly available or there is a reasonably similar open-source program. In this case, generating the right dataset is sufficient to produce an accurate benchmark. Based on this observation, we present Datamime, a profile-guided approach to generate representative benchmarks for production workloads. Datamime uses the performance profiles of a target workload to generate a dataset that, when used by a benchmark program, behaves very similarly to the target workload in terms of its microarchitectural characteristics. We evaluate Datamime on several datacenter workloads. Datamime generates synthetic benchmarks that closely match the microarchitectural features of these workloads, with a mean absolute percentage error of 3.2% on IPC. Microarchitectural behavior stays close across processor types. Finally, time-varying behaviors are also replicated, making these benchmarks useful to e.g. characterize and optimize tail latency 

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4. Autoscaling High-Throughput Workloads on Container Orchestrators

   https://doi.org/10.1109/CLUSTER49012.2020.00024  

   Zheng, Chao; Kremer-Herman, Nathaniel; Shaffer, Tim; Thain, Douglas (September 2020, IEEE Conference on Cluster Computing)

   null (Ed.)

   High-throughput computing (HTC) workloads seek to complete as many jobs as possible over a long period of time. Such workloads require efficient execution of many parallel jobs and can occupy a large number of resources for a longtime. As a result, full utilization is the normal state of an HTC facility. The widespread use of container orchestrators eases the deployment of HTC frameworks across different platforms,which also provides an opportunity to scale up HTC workloads with almost infinite resources on the public cloud. However, the autoscaling mechanisms of container orchestrators are primarily designed to support latency-sensitive microservices, and result in unexpected behavior when presented with HTC workloads. In this paper, we design a feedback autoscaler, High Throughput Autoscaler (HTA), that leverages the unique characteristics ofthe HTC workload to autoscales the resource pools used by HTC workloads on container orchestrators. HTA takes into account a reference input, the real-time status of the jobs’ queue, as well as two feedback inputs, resource consumption of jobs, and the resource initialization time of the container orchestrator. We implement HTA using the Makeflow workload manager, WorkQueue job scheduler, and the Kubernetes cluster manager. We evaluate its performance on both CPU-bound and IO-bound workloads. The evaluation results show that, by using HTA, we improve resource utilization by 5.6×with a slight increase in execution time (about 15%) for a CPU-bound workload, and shorten the workload execution time by up to 3.65×for an IO-bound workload. 

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5. Storage System Trace Characterization, Compression, and Synthesis using Machine Learning – An Extended Abstract

   Pratik Poudel (June 2023, International Conference on Principles of Advanced Discrete Simulation (PADS))

   This study addresses the knowledge gap in request-level storage trace analysis by incorporating workload characterization, com- pression, and synthesis. The aim is to better understand workload behavior and provide unique workloads for storage system test- ing under different scenarios. Machine learning techniques like K-means clustering and PCA analysis are employed to understand trace properties and reduce manual workload selection. By gener- ating synthetic workloads, the proposed method facilitates simu- lation and modeling-based studies of storage systems, especially for emerging technologies like Storage Class Memory (SCM) with limited workload availability. 

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