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1. The case for benchmarking control operations in cloud native storage

   Alex Merenstein, Vasily Tarasov ( July 2020 , Proceedings of the 12th USENIX Workshop on Hot Topics in Storage (HotStorage'20))

   null (Ed.)

   Storage benchmarking tools and methodologies today suffer from a glaring gap—they are incomplete because they omit storage control operations, such as volume creation and deletion, snapshotting, and volume reattachment and resizing. Control operations are becoming a critical part of cloud storage systems, especially in containerized environments like Kubernetes, where such operations can be executed by regular non-privileged users. While plenty of tools exist that simulate realistic data and metadata workloads, control operations are largely overlooked by the community and existing storage benchmarks do not generate control operations. Therefore, for cloud native environments, modern storage benchmarks fall short of serving their main purpose—holistic and realistic performance evaluation. Different storage provisioning solutions implement control operations indifferent ways, which means we need a unified storage benchmark to contrast and comprehend their performance and expected behaviors. In this position paper, we motivate the need for a cloud native storage benchmark by demonstrating the effect of control operations on storage provisioning solutions and workloads. We identify the challenges and requirements when implementing such benchmark and present our initial ideas for its design. 

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2. 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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3. EC-Fusion: An Efficient Hybrid Erasure Coding Framework to Improve Both Application and Recovery Performance in Cloud Storage Systems

   https://doi.org/10.1109/IPDPS47924.2020.00029  

   Qiu, Han ; Wu, Chentao ; Li, Jie ; Guo, Minyi ; Liu, Tong ; He, Xubin ; Dong, Yuanyuan ; Zhao, Yafei ( May 2020 , 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS))

   Nowadays erasure coding is one of the most significant techniques in cloud storage systems, which provides both quick parallel I/O processing and high capabilities of fault tolerance on massive data accesses. In these systems, triple disk failure tolerant arrays (3DFTs) is a typical configuration, which is supported by several classic erasure codes like Reed-Solomon (RS) codes, Local Reconstruction Codes (LRC), Minimum Storage Regeneration (MSR) codes, etc. For an online recovery process, the foreground application workloads and the background recovery workloads are handled simultaneously, which requires a comprehensive understanding on both two types of workload characteristics. Although several techniques have been proposed to accelerate the I/O requests of online recovery processes, they are typically unilateral due to the fact that the above two workloads are not combined together to achieve high cost-effective performance.To address this problem, we propose Erasure Codes Fusion (EC-Fusion), an efficient hybrid erasure coding framework in cloud storage systems. EC-Fusion is a combination of RS and MSR codes, which dynamically selects the appropriate code based on its properties. On one hand, for write-intensive application workloads or low risk on data loss in recovery workloads, EC-Fusion uses RS code to decrease the computational overhead and storage cost concurrently. On the other hand, for read-intensive or frequent reconstruction in workloads, MSR code is a proper choice. Therefore, a better overall application and recovery performance can be achieved in a cost-effective fashion. To demonstrate the effectiveness of EC-Fusion, several experiments are conducted in hadoop systems. The results show that, compared with the traditional hybrid erasure coding techniques, EC-Fusion accelerates the response time for application by up to 1.77×, and reduces the reconstruction time by up to 69.10%. 

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

   Chen, S. ; Wildani, A. ( December 2018 , Machine Learning in 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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