Cloud Observability: A MELTing Pot for Petabytes of Heterogenous Time Series
Summary: Frames cloud observability as a data-management challenge: petabytes of heterogeneous time series need low-latency ingestion, indexing, and real-time query/analytics for rapid root‑cause triage. Shows current ad‑hoc monitoring stacks fail on performance, cost, and ops; calls for redesigned scalable data/software infrastructure. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Suman Karumuri (Slack Technologies)
- 2. Franco Solleza (Brown University)
- 3. Stan Zdonik (Brown University)
- 4. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{karumuri_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{Cloud Observability: A MELTing Pot for Petabytes of Heterogenous Time Series}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Karumuri, Suman and Solleza, Franco and Zdonik, Stan and Tatbul, Nesime},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,519 | Mach: A Pluggable Metrics Storage Engine for the Age of Observability | 2022 | CIDR | 5.4119882e-05 |
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