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CloudGlide: Deconstructing the Landscape of Cloud-Based Analytics
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AI
DOI：10.14778/3773731.3773739.png)
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Abstract
En 中文
Cloud-based analytics now exposes an increasingly vast space of design choices. Key axes include provisioning (static vs. ephemeral), caching (capacity, tiering), scheduling (admission thresholds, parallelism), and pricing (reserved, on-demand, spot); each choice materially affects cost and performance. To navigate this complexity without deploying large-scale infrastructure, we present CloudGlide, a white-box simulation framework for systematically exploring cloud data analytics trade-offs. CloudGlide pairs a queueing-theoretic model with a discrete-event simulator (DES), ingesting real-world workload traces to provide cost and latency predictions under diverse configurations. Validated on industry traces and standard benchmarks, CloudGlide approximates behavior across existing architectures and supports rapid what-if analyses along the above axes, all without the prohibitive costs of live deployments.
Keywords:
Cloud-based analytics
Cost and performance trade-offs
Queueing-theoretic model
Discrete-event simulator
What-if analysis
Journal
P
IF:
3.3
Papers:
556
Citations:
1.2W