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Title: CENSUS: Counting Interleaved Workloads on Shared Storage
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  more » « less
Award ID(s):
1755958
PAR ID:
10311802
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
36th International Conference on Massive Storage Systems and Technology (MSST 2020)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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